WO2025029951A1 - Variance-based adaptive deep brain stimulation - Google Patents

Abstract

Systems, devices, and methods support sensing one or more electrical signals from one or more electrodes in contact with or proximate an anatomical element, determining a variance value associated with a plurality of metric values of the one or more electrical signals, generating a control signal based on comparing the variance value to a threshold variance value, and delivering therapy with respect to the anatomical element in response to the control signal.

Description

VARIANCE-BASED ADAPTIVE DEEP BRAIN STIMULATION

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims the benefit of and priority to U.S. Provisional Application No. 63/530,001 filed on July 31, 2023, entitled “VARIANCE-BASED ADAPTIVE DEEP BRAIN STIMULATION”, the entirety of which is hereby incorporated herein by reference.

FIELD OF INVENTION

[0002] The present disclosure is generally directed to nerve stimulation, and relates more particularly to variance-based adaptive deep brain stimulation.

BACKGROUND

[0003] Some devices may support brain stimulation for treating a medical condition. Improved techniques for delivering stimulation and monitoring effects thereof are desired.

BRIEF SUMMARY

[0004] Example aspects of the present disclosure include:

[0005] A system including: a processor; and a memory storing instructions thereon that, when executed by the processor, cause the processor to: sense one or more electrical signals (e.g., bioelectrical signal(s)) from one or more electrodes in contact with or proximate an anatomical element; determine a variance value associated with a plurality of metric values of the one or more electrical signals; generate a control signal based on comparing the variance value to a threshold variance value; and deliver therapy with respect to the anatomical element in response to the control signal.

[0006] Any of the aspects herein, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value or a plurality of threshold variance values; and a result of comparing the variance value to a threshold range or a plurality of threshold ranges, wherein generating the control signal is based on the state.

[0007] Any of the aspects herein, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold metric value, wherein generating the control signal is based on the state.

[0008] Any of the aspects herein, wherein the instructions are further executable by the processor to set one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values.

[0009] Any of the aspects herein, wherein the instructions are further executable by the processor to set one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of: a type associated with the one or more signals; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

[0010] Any of the aspects herein, wherein the instructions executable by the processor to deliver the therapy are further executable by the processor to: provide electrical signal stimulation to the anatomical element; and deliver medication to a subject in association with treating the anatomical element.

[0011] Any of the aspects herein, wherein the plurality of metric values include power values of the one or more electrical signals.

[0012] Any of the aspects herein, wherein the anatomical element includes nervous tissue.

[0013] A system including: a therapy delivery device; a processor; and a memory storing instructions thereon that, when executed by the processor, cause the processor to: sense one or more electrical signals from one or more electrodes in contact with or proximate an anatomical element; determine a variance value associated with a plurality of metric values of the one or more electrical signals; generate a control signal based on a result of comparing the variance value to a threshold variance value; and deliver therapy with respect to the anatomical element via the therapy delivery device in response to the control signal.

[0014] Any of the aspects herein, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value or a plurality of threshold variance values; and a result of comparing the variance value to a threshold range or a plurality of threshold ranges, wherein generating the control signal is based on the stat^p [0015] Any of the aspects herein, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold value, wherein generating the control signal is based on the state.

[0016] Any of the aspects herein, wherein the instructions are further executable by the processor to set one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values.

[0017] Any of the aspects herein, wherein the instructions are further executable by the processor to set one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of: a type associated with the one or more electrical signals; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

[0018] Any of the aspects herein, wherein the instructions executable by the processor to deliver the therapy are further executable by the processor to: provide electrical signal stimulation to the anatomical element; deliver medication to a subject in association with treating the anatomical element; or both.

[0019] A method including: sensing, via one or more sensors, one or more electrical signals from one or more electrodes in contact with or proximate an anatomical element; calculating a variance value associated with a plurality of metric values of the one or more electrical signals; generating a control signal based on a result of comparing the variance value to a threshold variance value; electronically transmitting the control signal; and causing therapy to be delivered to the anatomical element in response to the control signal. [0020] Any of the aspects herein, further including: setting a state associated with delivering therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value or a plurality of threshold variance values; and a result of comparing the variance value to a threshold range or a plurality of threshold ranges, wherein generating the control signal is based on the state. [0021] Any of the aspects herein, further including: setting a state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold value, wherein generating the control signal is based nn thp [0022] Any of the aspects herein, further including: setting one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values.

[0023] Any of the aspects herein, further including: setting one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of: a type associated with the one or more electrical signals; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

[0024] Any of the aspects herein, further including: providing electrical signal stimulation to the anatomical element; and delivering medication to a subject in association with treating the anatomical element.

[0025] Any aspect in combination with any one or more other aspects.

[0026] Any one or more of the features disclosed herein.

[0027] Any one or more of the features as substantially disclosed herein.

[0028] Any one or more of the features as substantially disclosed herein in combination with any one or more other features as substantially disclosed herein.

[0029] Any one of the aspects/features/implementations in combination with any one or more other aspects/features/implementations.

[0030] Use of any one or more of the aspects or features as disclosed herein.

[0031] It is to be appreciated that any feature described herein can be claimed in combination with any other feature(s) as described herein, regardless of whether the features come from the same described implementation.

[0032] The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims.

[0033] The preceding is a simplified summary of the disclosure to provide an understanding of some aspects of the disclosure. This summary is neither an extensive nor exhaustive overview of the disclosure and its various aspects, implementations, and configurations. It is intended neither to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure but to present selected concepts of the disclosure in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other aspects, implementations, and configurations of the disclosure are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.

[0034] Numerous additional features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the implementation descriptions provided hereinbelow.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS [0035] The accompanying drawings are incorporated into and form a part of the specification to illustrate several examples of the present disclosure. These drawings, together with the description, explain the principles of the disclosure. The drawings simply illustrate preferred and alternative examples of how the disclosure can be made and used and are not to be construed as limiting the disclosure to only the illustrated and described examples. Further features and advantages will become apparent from the following, more detailed, description of the various aspects, implementations, and configurations of the disclosure, as illustrated by the drawings referenced below.

[0036] Fig. 1 illustrates an example of a system in accordance with aspects of the present disclosure.

[0037] Fig. 2 illustrates an example of the system in accordance with aspects of the present disclosure.

[0038] Figs. 3 through 5 illustrate example plots in accordance with aspects of the present disclosure.

[0039] Fig. 6 illustrates an example of the system in accordance with aspects of the present disclosure.

[0040] Fig. 7 illustrates an example of a process flow in accordance with aspects of the present disclosure.

DETAILED DESCRIPTION

[0041] It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example or implementation, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, and/or may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the disclosed techniques according to different implementations of the present disclosure). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a computing device and/or a medical device.

[0042] In one or more examples, the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or codes on a computer-readable storage medium and executed by a hardware-based processing unit. Alternatively, or additionally, functions may be implemented using machine learning models, neural networks, artificial neural networks, or combinations thereof (alone or in combination with instructions). Alternatively, or additionally, functions may be implemented using machine learning models, neural networks, artificial neural networks, or combinations thereof (alone or in combination with instructions). Computer-readable storage media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0043] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple Al l, A12, A12X, A12Z, or A13 Bionic processors; or any other general purpose microprocessors), graphics processing units (e.g., Nvidia GeForce RTX 2000-series processors, Nvidia GeForce RTX 3000-series processors, AMD Radeon RX 5000-series processors, AMD Radeon RX 6000-series processors, or any other graphics processing units), application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

[0044] Before any implementations of the disclosure are explained in detail, it is to be understood that the disclosure is not limited in its application to the details of construction and the arrangement of components fnrth in fop following description or illustrated in the drawings. The disclosure is capable of other implementations and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. Further, the present disclosure may use examples to illustrate one or more aspects thereof. Unless explicitly stated otherwise, the use or listing of one or more examples (which may be denoted by “for example,” “by way of example,” “e.g.,” “such as,” or similar language) is not intended to and does not limit the scope of the present disclosure.

[0045] The terms proximal and distal are used in this disclosure with their conventional medical meanings, proximal being closer to the operator or user of the system, and further from the region of surgical interest in or on the patient, and distal being closer to the region of surgical interest in or on the patient, and further from the operator or user of the system.

[0046] Some adaptive deep brain stimulation (DBS) algorithms are based on thresholding of an input signal. However, in some cases, control techniques based on thresholding may be susceptible to signal drift or changes in the distribution of the underlying signal (e.g., due to temperature changes, electronic noise, fluctuations in the power supply, etc.). Some other techniques may include adaptive neuromodulation in association with providing stimulation to a subject.

[0047] According to example aspects of the present disclosure, systems and techniques are described that support variance— based stimulation in association with delivering therapy to a subject. For example, the systems and techniques may support variance— based adaptive brain stimulation (e.g., variance— based adaptive DBS techniques). In some aspects of the present disclosure, by using the variance of an input signal (e.g., an electrical signal such as a bioelectrical signal, an environmental electrical signal, a noise signal, combinations thereof, etc.), the systems and techniques may mitigate changes in drift or overall variability of the input signal. In some examples, as a result of mitigating the changes in drift or overall variability, the systems and techniques may produce an adaptive technique for providing stimulation having increased stability, responsiveness, and optimization with respect to a medical condition of a patient compared to other stimulation techniques. [0048] Aspects of the present disclosure support threshold-based adaptive deep brain stimulation (DBS) systems based on input signals (e.g., bioelectrical signals) which may vary in variance (e.g., standard deviation). For example, aspects of the present disclosure may include monitoring signal metrics (e.g., power, variance in power, standard deviation of power, etc.) of the input signals with respect to time (e.g., in the time domain (TD)). [0049] The systems and techniques may support using one or more metric values associated with an input signal as a control signal for controlling therapy delivery. In some example implementations, the systems and techniques may support using a moving variance as a primary (or secondary) input control signal for adapting therapy delivery. For example, the systems and techniques may include using moving variance as a control input to an aDBS technique for delivering therapy, thereby providing a variance-based adaptive DBS (vaDBS) technique for delivering therapy. In some aspects, the example techniques described herein may provide resistance to signal drift related to changes in signal amplitude and noise floor.

[0050] In some examples, the systems and techniques may support setting a state associated with delivering therapy based on an input signal (e.g., bioelectrical signal). For example, as will be described herein, the systems and techniques may include variancebased adaptive stimulation (e.g., vaDBS) that includes setting a state (also referred to herein as a vaDBS state) for delivering therapy based on a comparison of variance of the input signal to a threshold variance value. In another example, as will be described herein, the systems and techniques may include setting a state for delivering therapy based on a comparison of the variance to a threshold variance range or a comparison of another metric value (e.g., power, power range, etc.) associated with the input signal to a corresponding threshold value.

[0051] The systems and techniques may support multimodal approaches for stimulation. For example, the systems and techniques may support providing therapy and stimulation using a stimulation mode based on vaDBS techniques (e.g., stimulation based on variance of the bioelectrical signal). In some other aspects, the systems and techniques may support providing therapy and stimulation using the stimulation mode based on vaDBS techniques described herein and a stimulation mode based on aDBS techniques (e.g., stimulation based on measured power of a bioelectrical signal, etc.). In some aspects, the systems and techniques may include providing stimulation based on weighting factors respectively applied to each stimulation mode. [0052] The systems and techniques described herein as applied to deep brain stimulation are not limited thereto. For example, the systems and techniques may support providing clinical insights (e.g., biological responses) associated with stimulation provided to a patient. In some aspects, the systems and techniques may support guiding automated programming of stimulation settings. In some other aspects, the systems and techniques may support stimulation associated with other medical procedures (e.g., chronic deep brain stimulation (CDBS) involving the use of implanted electrodes to stimulate specific regions of the brain).

[0053] Implementations of the present disclosure provide technical solutions to one or more of the problems of (1) signal drift and relatively large noise floor changes and (2) measurement errors associated with a bioelectrical signal that may impact determining parameters for delivering therapy to a patient.

[0054] Fig. 1 illustrates an example of a system 100 that supports aspects of the present disclosure.

[0055] The system 100 includes a computing device 102, one or more imaging devices 112, a robot 114, a navigation system 118, a database 130, and/or a cloud network 134 (or other network). Systems according to other implementations of the present disclosure may include more or fewer components than the system 100. For example, the system 100 may omit and/or include additional instances of one or more components of the computing device 102, the imaging device(s) 112, the robot 114, navigation system 118, the database 130, and/or the cloud network 134. In an example, the system 100 may omit any instance of the computing device 102, the imaging device(s) 112, the robot 114, navigation system 118, the database 130, and/or the cloud network 134. For example, the system 100 may omit the robot 114 and the navigation system 118. The system 100 may support the implementation of one or more other aspects of one or more of the methods disclosed herein.

[0056] The computing device 102 includes a processor 104, a memory 106, a communication interface 108, and a user interface 110. Computing devices according to other implementations of the present disclosure may include more or fewer components than the computing device 102. The computing device 102 may be, for example, a control device including electronic circuitry associated with providing control signals to a stimulation device 154.

[0057] The processor 104 of the computing device 102 may be any processor described herein or any similar processor. The nrnrp««nr 104 mav be configured to execute instructions stored in the memory 106, which instructions may cause the processor 104 to carry out one or more computing steps utilizing or based on data received from the imaging devices 112, the robot 114, the navigation system 118, electrodes 150, the stimulation device 154, the database 130, and/or the cloud network 134.

[0058] The memory 106 may be or include RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other tangible, non-transitory memory for storing computer-readable data and/or instructions. The memory 106 may store information or data associated with completing, for example, any step of the methods or process flows described herein, or of any other methods. The memory 106 may store, for example, instructions and/or machine learning models that support one or more functions of the imaging devices 112, the robot 114, the navigation system 118, and the stimulation device 154. For instance, the memory 106 may store content (e.g., instructions and/or machine learning models) that, when executed by the processor 104, enable image processing 120, segmentation 122, transformation 124, registration 128, and/or navigation processing 129. Such content, if provided as in instruction, may, in some implementations, be organized into one or more applications, modules, packages, layers, or engines.

[0059] Alternatively or additionally, the memory 106 may store other types of content or data (e.g., machine learning models, artificial neural networks, deep neural networks, etc.) that can be processed by the processor 104 to carry out the various method and features described herein. Thus, although various contents of memory 106 may be described as instructions, it should be appreciated that functionality described herein can be achieved through use of instructions, techniques, and/or machine learning models. The data, techniques, and/or instructions may cause the processor 104 to manipulate data stored in the memory 106 and/or received from or via the imaging devices 112, the robot 114, the navigation system 118, the database 130, the cloud network 134, the electrodes 150, and/or the stimulation device 154.

[0060] The computing device 102 may also include a communication interface 108. The communication interface 108 may be used for receiving data or other information from an external source (e.g., the imaging devices 112, the robot 114, the navigation system 118, the database 130, the cloud network 134, the electrodes 150, the stimulation device 154, and/or any other system or component separate from the system 100), and/or for transmitting instructions, data (e.g., bioelectrical signals 126, measurement data associated with the bioelectrical signal 126, control signals, etc.), or other information to an external system or device (e.g., another comp^Fna dpvirp i o? the imaging devices 112, the robot 114, the navigation system 118, the database 130, the cloud network 134, the electrodes 150, the stimulation device 154, and/or any other system or component not part of the system 100). The communication interface 108 may include one or more wired interfaces (e.g., a USB port, an Ethernet port, a Firewire port) and/or one or more wireless transceivers or interfaces (configured, for example, to transmit and/or receive information via one or more wireless communication protocols such as 802.1 la/b/g/n, Bluetooth, NFC, ZigBee, and so forth). In some implementations, the communication interface 108 may support communication between the device 102 and one or more other processors 104 or computing devices 102, whether to reduce the time needed to accomplish a computing-intensive task or for any other reason.

[0061] The computing device 102 may also include one or more user interfaces 110. The user interface 110 may be or include a keyboard, mouse, trackball, monitor, television, screen, touchscreen, and/or any other device for receiving information from a user and/or for providing information to a user. The user interface 110 may be used, for example, to receive a user selection or other user input regarding any step of any method described herein. Notwithstanding the foregoing, any required input for any step of any method described herein may be generated automatically by the system 100 (e.g., by the processor 104 or another component of the system 100) or received by the system 100 from a source external to the system 100. In some implementations, the user interface 110 may support user modification (e.g., by a surgeon, medical personnel, a patient, etc.) of instructions to be executed by the processor 104 according to one or more implementations of the present disclosure, and/or to user modification or adjustment of a setting of other information displayed on the user interface 110 or corresponding thereto.

[0062] In some implementations, the computing device 102 may utilize a user interface 110 that is housed separately from one or more remaining components of the computing device 102. In some implementations, the user interface 110 may be located proximate one or more other components of the computing device 102, while in other implementations, the user interface 110 may be located remotely from one or more other components of the computer device 102.

[0063] The imaging device 112 may be operable to image anatomical feature(s) (e.g., a bone, veins, tissue, etc.) and/or other aspects of patient anatomy to yield image data (e.g., image data depicting or corresponding to a bone, veins, tissue, etc.). “Image data” as used herein refers to the data generated or captured by an imaging device 112, including in a machine-readable form, a graphical/ «na1 form and in any other form. In various examples, the image data may include data corresponding to an anatomical feature of a patient, or to a portion thereof. The image data may be or include a preoperative image, an intraoperative image, a postoperative image, or an image taken independently of any surgical procedure. In some implementations, a first imaging device 112 may be used to obtain first image data (e.g., a first image) at a first time, and a second imaging device 112 may be used to obtain second image data (e.g., a second image) at a second time after the first time. The imaging device 112 may be capable of taking a 2D image or a 3D image to yield the image data. The imaging device 112 may be or include, for example, an ultrasound scanner (which may include, for example, a physically separate transducer and receiver, or a single ultrasound transceiver), an O-arm, a C-arm, a G-arm, or any other device utilizing X-ray-based imaging (e.g., a fluoroscope, a CT scanner, or other X-ray machine), a magnetic resonance imaging (MRI) scanner, an optical coherence tomography (OCT) scanner, an endoscope, a microscope, an optical camera, a thermographic camera (e.g., an infrared camera), a radar system (which may include, for example, a transmitter, a receiver, a processor, and one or more antennae), or any other imaging device 112 suitable for obtaining images of an anatomical feature of a patient. The imaging device 112 may be contained entirely within a single housing, or may include a transmitter/emitter and a receiver/ detector that are in separate housings or are otherwise physically separated. [0064] In some implementations, the imaging device 112 may include more than one imaging device 112. For example, a first imaging device may provide first image data and/or a first image, and a second imaging device may provide second image data and/or a second image. In still other implementations, the same imaging device may be used to provide both the first image data and the second image data, and/or any other image data described herein. The imaging device 112 may be operable to generate a stream of image data. For example, the imaging device 112 may be configured to operate with an open shutter, or with a shutter that continuously alternates between open and shut so as to capture successive images. For purposes of the present disclosure, unless specified otherwise, image data may be considered to be continuous and/or provided as an image data stream if the image data represents two or more frames per second.

[0065] The robot 114 may be any surgical robot or surgical robotic system. The robot 114 may be or include, for example, the Mazor X™ Stealth Edition robotic guidance system. The robot 114 may be configured to position the imaging device 112 at one or more precise position(s) and orientation(s), and/or to return the imaging device 112 to the same position(s) and orientation(s) a^f ” nnint In time. The robot 114 may additionally or alternatively be configured to manipulate a surgical tool (whether based on guidance from the navigation system 118 or not) to accomplish or to assist with a surgical task. In some implementations, the robot 114 may be configured to hold and/or manipulate an anatomical element during or in connection with a surgical procedure. The robot 114 may include one or more robotic arms 116. In some implementations, the robotic arm 116 may include a first robotic arm and a second robotic arm, though the robot 114 may include more than two robotic arms. In some implementations, one or more of the robotic arms 116 may be used to hold and/or maneuver the imaging device 112. In implementations where the imaging device 112 includes two or more physically separate components (e.g., a transmitter and receiver), one robotic arm 116 may hold one such component, and another robotic arm 116 may hold another such component. Each robotic arm 116 may be positionable independently of the other robotic arm. The robotic arms 116 may be controlled in a single, shared coordinate space, or in separate coordinate spaces.

[0066] The robot 114, together with the robotic arm 116, may have, for example, one, two, three, four, five, six, seven, or more degrees of freedom. Further, the robotic arm 116 may be positioned or positionable in any pose, plane, and/or focal point. The pose includes a position and an orientation. As a result, an imaging device 112, surgical tool, or other object held by the robot 114 (or, more specifically, by the robotic arm 116) may be precisely positionable in one or more needed and specific positions and orientations.

[0067] The robotic arm(s) 116 may include one or more sensors that enable the processor 104 (or a processor of the robot 114) to determine a precise pose in space of the robotic arm (as well as any object or element held by or secured to the robotic arm).

[0068] In some implementations, reference markers (e.g., navigation markers) may be placed on the robot 114 (including, e.g., on the robotic arm 116), the imaging device 112, or any other object in the surgical space. The reference markers may be tracked by the navigation system 118, and the results of the tracking may be used by the robot 114 and/or by an operator of the system 100 or any component thereof. In some implementations, the navigation system 118 can be used to track other components of the system (e.g., imaging device 112) and the system can operate without the use of the robot 114 (e.g., with the surgeon manually manipulating the imaging device 112 and/or one or more surgical tools, based on information and/or instructions generated by the navigation system 118, for example).

[0069] The navigation system 118 may provide navigation for a surgeon and/or a surgical robot during an operation. Tt>^ navigation «v«t m 118 may be any now-known or future-developed navigation system, including, for example, the Medtronic

Stealth Station™ S8 surgical navigation system or any successor thereof. The navigation system 118 may include one or more cameras or other sensor(s) for tracking one or more reference markers, navigated trackers, or other objects within the operating room or other room in which some or all of the system 100 is located. The one or more cameras may be optical cameras, infrared cameras, or other cameras. In some implementations, the navigation system 118 may include one or more tracking devices 140 (e.g., electromagnetic sensors, acoustic sensors, etc.).

[0070] In some aspects, the navigation system 118 may include one or more of an optical tracking system, an acoustic tracking system, an electromagnetic tracking system, a radar tracking system, an inertial measurement unit (IMU) based tracking system, and a computer vision based tracking system. The navigation system 118 may include a corresponding transmission device 136 capable of transmitting signals associated with the tracking type. In some aspects, the navigation system 118 may be capable of computer vision based tracking of objects present in images captured by the imaging device(s) 112. [0071] In various implementations, the navigation system 118 may be used to track a position and orientation (e.g., a pose) of the imaging device 112, the robot 114 and/or robotic arm 116, and/or one or more surgical tools (or, more particularly, to track a pose of a navigated tracker attached, directly or indirectly, in fixed relation to the one or more of the foregoing). The navigation system 118 may include a display for displaying one or more images from an external source (e.g., the computing device 102, imaging device 112, or other source) or for displaying an image and/or video stream from the one or more cameras or other sensors of the navigation system 118.

[0072] In some implementations, the system 100 can operate without the use of the navigation system 118. The navigation system 118 may be configured to provide guidance to a surgeon or other user of the system 100 or a component thereof, to the robot 114, or to any other element of the system 100 regarding, for example, a pose of one or more anatomical elements, whether or not a tool is in the proper trajectory, and/or how to move a tool into the proper trajectory to carry out a surgical task according to a preoperative or other surgical plan.

[0073] The processor 104 may utilize data stored in memory 106 as a neural network. The neural network may include a machine learning architecture. In some aspects, the neural network may be or include one or more classifiers. In some other aspects, the neural network may be or include any machine Ipamino network such as, for example, a deep learning network, a convolutional neural network, a reconstructive neural network, a generative adversarial neural network, or any other neural network capable of accomplishing functions of the computing device 102 described herein. Some elements stored in memory 106 may be described as or referred to as instructions or instruction sets, and some functions of the computing device 102 may be implemented using machine learning techniques.

[0074] For example, the processor 104 may support machine learning model(s) 138 which may be trained and/or updated based on data (e.g., training data 146) provided or accessed by any of the computing device 102, the imaging device 112, the robot 114, the navigation system 118, the electrodes 150, stimulation device 154, the database 130, and/or the cloud network 134. The machine learning model(s) 138 may be built and updated by the computing device 102 based on the training data 146 (also referred to herein as training data and feedback).

[0075] Example aspects of the electrodes 150 and stimulation device 154 will be described with reference the following figures.

[0076] The database 130 may store information that correlates one coordinate system to another (e.g., one or more robotic coordinate systems to a patient coordinate system and/or to a navigation coordinate system). The database 130 may additionally or alternatively store, for example, one or more surgical plans (including, for example, patient treatment plans associated with the electrodes 150 and stimulation device 154); one or more images useful in connection with a surgery to be completed by or with the assistance of one or more other components of the system 100; and/or any other useful information. The database 130 may additionally or alternatively store, for example, location or coordinates of the stimulation device 154.

[0077] The database 130 may be configured to provide any such information to the computing device 102 or to any other device of the system 100 or external to the system 100, whether directly or via the cloud network 134. In some implementations, the database 130 may include treatment information (e.g., a therapy delivery plan) associated with a patient. In some implementations, the database 130 may be or include part of a hospital image storage system, such as a picture archiving and communication system (PACS), a health information system (HIS), and/or another system for collecting, storing, managing, and/or transmitting electronic medical records including image data.

[0078] In some aspects, the computing device 102 may communicate with a server(s) and/or a database (e.g., database 1301 directl nr indirectly over a communications network (e.g., the cloud network 134). The communications network may include any type of known communication medium or collection of communication media and may use any type of protocols to transport data between endpoints. The communications network may include wired communications technologies, wireless communications technologies, or any combination thereof.

[0079] Wired communications technologies may include, for example, Ethernet-based wired local area network (LAN) connections using physical transmission mediums (e.g., coaxial cable, copper cable/wire, fiber-optic cable, etc.). Wireless communications technologies may include, for example, cellular or cellular data connections and protocols (e.g., digital cellular, personal communications service (PCS), cellular digital packet data (CDPD), general packet radio service (GPRS), enhanced data rates for global system for mobile communications (GSM) evolution (EDGE), code division multiple access (CDMA), single-carrier radio transmission technology (1 *RTT), evolution-data optimized (EVDO), high speed packet access (HSPA), universal mobile telecommunications service (UMTS), 3G, long term evolution (LTE), 4G, and/or 5G, etc.), Bluetooth®, Bluetooth® low energy, Wi-Fi, radio, satellite, infrared connections, and/or ZigBee® communication protocols.

[0080] The Internet is an example of the communications network that constitutes an Internet Protocol (IP) network consisting of multiple computers, computing networks, and other communication devices located in multiple locations, and components in the communications network (e.g., computers, computing networks, communication devices) may be connected through one or more telephone systems and other means. Other examples of the communications network may include, without limitation, a standard Plain Old Telephone System (POTS), an Integrated Services Digital Network (ISDN), the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a wireless LAN (WLAN), a Session Initiation Protocol (SIP) network, a Voice over Internet Protocol (VoIP) network, a cellular network, and any other type of packet-switched or circuit-switched network known in the art. In some cases, the communications network may include of any combination of networks or network types. In some aspects, the communications network may include any combination of communication mediums such as coaxial cable, copper cable/wire, fiber-optic cable, or antennas for communicating data (e.g., transmitting/receiving data).

[0081] The computing device 102 may be connected to the cloud network 134 via the communication interface 108, using a wirpd mnn inn, a wireless connection, or both. In some implementations, the computing device 102 may communicate with the database 130 and/or an external device (e.g., a computing device) via the cloud network 134. [0082] The system 100 or similar systems may be used, for example, to carry out one or more aspects of any of the methods or process flows described herein. The system 100 or similar systems may also be used for other purposes.

[0083] Fig. 2 illustrates an example implementation of the system 100 that supports variance based stimulation in accordance with aspects of the present disclosure. Aspects of the system 100 previously described with reference to Fig. 1 and descriptions of like elements are omitted for brevity.

[0084] In an example, electrode 150 may be in contact with or proximate an anatomical element 149 of a subject 148. Electrode 150 may support sensing a bioelectrical signal 126 of the subject 148. In the example of Fig. 2, the anatomical element 149 may be nervous tissue. For example, the anatomical element 149 may include nervous tissue of the brain of the subject 148, and sensing may include receiving one or more signals (e.g., EEG, ECoG, MEG, LFP, etc.) from the nervous tissue. Example aspects of the present disclosure are not limited thereto and the anatomical element 149 may include other nervous tissue of the subject 148.

[0085] The stimulation device 154 may provide data 125 (e.g., bioelectrical signal 126) associated with the subject 148 to the computing device 102. In some aspects, the computing device 102 may compute measurement data (e.g., metric values, power values, variance, etc.) associated with the bioelectrical signal 126. In some other aspects, the stimulation device 154 may compute the measurement data or a portion thereof and provide the measurement data to the computing device 102. Based on the metric values of the bioelectrical signal 126, the computing device 102 may generate and provide control signals 155 to the stimulation device 154 for delivering therapy to the subject 148. For example, based on a variance 170 (also referred to herein as a variance value) between two or more metric values of the bioelectrical signal 126, the computing device 102 may generate and provide control signals 155 to the stimulation device 154. Non-limiting examples of metric values include signal amplitude, signal envelope, peak-to-peak amplitude, latency, decay, normalized variance, frequency of variance signals, spectral information, etc. It should also be appreciated that the metric values may be used to determine different types of variance values, including variance values in a time domain, variance values in a spectral domain, or combinations thereof. [0086] An example implementation is described with reference to Fig. 2 and example plots 160-a through 162-a. The plot 160-a includes an ordinate axis corresponding to power 165 of a bioelectrical signal 126 in units of voltage squared and an abscissa axis corresponding to time in units of milliseconds. The plot 161-a includes an ordinate axis corresponding to variance 170-a in units of voltage squared and an abscissa axis corresponding to time in units of milliseconds. The plot 162-a includes an ordinate axis corresponding to a state value (e.g. between 0.0 and 1.0) and an abscissa axis corresponding to time in units of milliseconds.

[0087] The stimulation device 154 may sense a bioelectrical signal 126 from an electrode 150 (or multiple electrodes 150) in contact with or proximate the anatomical element 149 of the subject 148. The computing device 102 (or stimulation device 154) may compute metric values of the bioelectrical signal 126. In an example, the metric values of the bioelectrical signal 126 may include power values.

[0088] In some aspects, the computing device 102 (or stimulation device 154) may determine additional metrics associated with the bioelectrical signal 126. For example, referring to plot 161-a, the computing device 102 may calculate a variance 170-a associated with the metric values of the bioelectrical signal 126. An example of the variance 170-a versus time (e.g., seconds) is illustrated at plot 161-a.

[0089] The computing device 102 may generate a control signal 155 based on the metrics associated with the bioelectrical signal 126. For example, in association with a vaDBS mode, the computing device 102 may generate the control signal 155 based on comparing the variance 170-a to a threshold variance value 171-a. In an example, in response to a comparison result (e.g., in which the variance 170-a is greater than the threshold variance value 171-a), the computing device 102 may generate and provide a control signal 155 to the stimulation device 154. In another example, in response to a different comparison result (e.g., in which the variance 170-a is less than the threshold variance value 171-a), the computing device 102 may refrain from providing a control signal 155 to the stimulation device 154. In some other example implementations, the computing device 102 may generate and provide a control signal in response to the comparison result in which the variance 170-a is greater than the threshold variance value 171-a, and the computing device 102 may generate and provide a different control signal in response to the comparison result in which the variance 170-a is less than the threshold variance value 171-a. It should also be appreciated that the computing device 102 may generate and provide a control signal in response to comparing the variance 170-a to a plurality of threshold variance values (e.g., an upper threshold and lower threshold). [0090] In another example, in association with an aDBS mode, the computing device 102 may generate the control signal 155 based on comparing the metric values (e.g., power) to a threshold value 166-a (e.g., power). In an example, in response to a comparison result (e.g., in which the metric value is greater than the threshold value 166- a), the computing device 102 may generate and provide control signal 155 to the stimulation device 154. In another example, in response to a different comparison result (e.g., in which the metric value is less than the threshold value 166-a), the computing device 102 may refrain from providing control signal 155 to the stimulation device 154. In some other example implementations, the computing device 102 may generate and provide control signal in response to the comparison result in which the metric value is greater than the threshold value 166-a, and the computing device 102 may generate and provide a different control signal in response to the comparison result in which the metric value is less than the threshold value 166-a.

[0091] The metrics based on which the computing device 102 may generate and provide a control signal 155 are examples, and aspects of the present disclosure are not limited thereto. Other non-limiting examples of parameters based on which the computing device 102 may evaluate the variance 170-a may include a threshold variance range 172, a baseline value (not illustrated) associated with the variance 170-a, and the like For example, the computing device 102 may generate and provide a control signal 155 and may set one or more parameters associated with delivering therapy based on a deviation between the variance 170-a and the range 172 (e.g., a case in which the variance 170-a is greater than an upper boundary of the range 172 or less than a lower boundary of the range 172), a deviation between the variance 170 and the baseline value, and the like. Other non-limiting examples of parameters based on which the computing device 102 may evaluate the variance 170-a may include dynamic thresholds or target values associated with the variance 170-a. For example, the computing device 102 may compare the variance 170-a to different thresholds respective to different states (e.g., sleep, wake, exercise, etc.) of the subject 148.

[0092] The stimulation device 154 may deliver therapy to the subject 148 in response to the control signal 155. In some aspects, the stimulation device 154 may deliver the therapy with respect to the anatomical element 149 and/or another anatomical element 149 of the subject 148. In an example of deli ver ¹¹⁽¹ thpran thp stimulation device 154 may provide electrical signal stimulation to the anatomical element 149 via the electrode 150. In another example (not illustrated), the stimulation device 154 may deliver medication to the subject 148 in association with treating the anatomical element 149 and/or another anatomical element 149 of the subject 148.

[0093] The system 100 may support multimodal approaches for delivering therapy (e.g., stimulation, medication, etc.) to the subject 148. For example, the system 100 may support setting different states for delivering therapy to the subject 148. The states may be associated with different stimulation modes (e.g., aDBS, vaDBS) as described herein. In an example, the system 100 may support setting the states based on metric values of the bioelectrical signal 126 and characteristics (e.g., variance 170, deviation of the variance 170, etc.) associated with the metric values. Examples of the different states (e.g., state 175 and state 180) described herein are illustrated at plot 162-a.

[0094] In an example, the computing device 102 may set a state 175-a (e.g., vaDBS state) associated with delivering therapy based on the result of comparing the variance 170-a to the threshold variance value 171 -a. The computing device 102 may generate and provide control signal 155 based on the state 175-a. In an example, the computing device 102 generate and provide control signal 155 in response to a low state (e.g., state 175-a = ‘0’), and the computing device 102 refrain from generating/providing control signal 155 in response to a high state (e.g., state 175-a = ‘ 1 ’).

[0095] In another example, the computing device 102 may set a state 180 (e.g., aDBS state) associated with delivering therapy based on the result of comparing the metric value (described with reference to plot 160-a) to the threshold value 166-a. The computing device 102 may generate and provide control signal 155 based on the state 180-a. In an example, the computing device 102 generate and provide control signal 155 in response to a low state (e.g., state 180-a = ‘0’), and the computing device 102 refrain from generating/providing control signal 155 in response to a high state (e.g., state 180-a = ‘ 1 ’). [0096] In another example (not illustrated), the computing device 102 may set a state associated with delivering therapy with respect to the anatomical element 149, based on a result of comparing the variance 170-a to a threshold range value 172-a or a plurality of threshold ranges. The computing device 102 may generate the control signal 155 based on the state.

[0097] Aspects of the present disclosure may support multiple thresholds. For example, referring to plot 160-a, the system 100 may support an additional threshold value associated with generating and proviso the control <4 anal 155 (or a different control signal 155) and setting state 180-a. In another example, referring to plot 161-a, the system 100 may support an additional threshold variance value (not illustrated) associated with generating and providing the control signal 155 (or a different control signal 155) and setting state 175-a. In some other examples, the system 100 may support thresholds (not illustrated) associated with any metric value of the bioelectrical signal 126.

[0098] In some aspects, the system 100 may support different therapy profiles associated with providing therapy to the subject 148. The system 100 may apply any of the therapy profiles based on one or more criteria. In some aspects, the system 100 may apply a therapy profile based on the metric type (e.g., power 165, variance 170, etc.) associated with evaluating the bioelectrical signal 126. In some other aspects, the system 100 may apply different therapy profiles based on metric values of a corresponding metric type. [0099] For example, the computing device 102 may apply a therapy profile associated with the value of the variance 170-a. For example, the computing device 102 may apply different therapy profiles for cases in which the variance 170-a is included in a range 172- a or are outside the range 172-a. In another example, the computing device 102 may apply a second therapy profile associated with metric values (e.g., power 165) of the bioelectrical signal 126.

[0100] The computing device 102 may set one or more parameters associated with delivering the therapy based on the therapy profiles. In the example in which delivering therapy includes providing electrical stimulation, the one or more parameters may include frequency associated with delivering electrical pulses, amplitude (strength) of the electrical current, pulse width (temporal duration) of the electrical pulses, stimulation mode (e.g., continuous stimulation, intermittent stimulation, burst stimulation, etc.), polarity of the electrical current, and the like. In the example in which delivering therapy includes delivering medication to the subject 148, the one or more parameters may include frequency associated with administering the medication, dosage amount, and the like. [0101] Accordingly, for example, the system 100 may support guided programming for delivering therapy in association with multiple situation types (e.g., power level of the bioelectrical signal 126 above or below a threshold value 166-a, variance 170-a above or below a threshold variance value 171-a, etc.). In some aspects, each therapy profile may correspond to various device settings of the stimulation device 154. As described herein, the system 100 may support multiple control techniques based on the same data set.

[0102] The system 100 may support features for setting sensing parameters associated with sensing the bioelectrical signal 1 Facpd nn nn? _or more criteria. For example, the system 100 may support setting the parameters based on a type associated with the bioelectrical signal 126, the variance 170 of the bioelectrical signal 126, metric values (e.g., threshold value 166) of the bioelectrical signal 126, whether the variance 170 is within the threshold variance range 172, and the like.

[0103] Example sensing parameters include, electrode configuration, sampling rate, and sensing center frequency. In an example, the system 100 may implement the electrode configuration as a primary sensing parameter (e.g., among multiple sensing parameters) and/or a standalone sensing parameter. In another example, the system 100 may set the sampling rate to a default value, and the sampling rate may be fixed or configurable. In some other examples, the system 100 may utilize different center frequencies respective to different symptoms.

[0104] The system 100 may support applying weighting factors to any of the parameters described herein in association with generating a control signal 155 and delivering therapy. For example, the system 100 may support applying respective weighting factors to a comparison associated with metric value (e.g., power 165) and the threshold value 166, a comparison associated with variance 170-a and threshold variance value 171-a, a comparison associated with the variance 170-a and the range 172-a, and the like. In some aspects, the system 100 may support applying respective weighting factors to states (e.g., state 175-a, state 180-a, etc.) associated with the comparisons. Accordingly, for example, the system 100 may support delivering therapy based on the weighting factors as applied to each parameter (e.g., metric value comparison to a threshold value 166-a, comparison of the variance 170-a to threshold variance value 171-a, comparison of the variance 170-a to the range 172-a, etc.).

[0105] In some aspects, as illustrated in Fig. 2, the state 175-a (e.g., vaDBS state) may be inverted compared to the state 180-a (e.g., aDBS state). In some aspects, referring to the example of plot 162-a, the temporal duration (e.g., after about 570 ms) in which state 175-a is ‘0’ and state 180-a is ‘ 1 ’ may be referred to as a stable state. In some cases, the system 100 may support tracking multiple variables. In an example, the system 100 may generate a control signal and/or direct therapy to increase stimulation for cases in which activity for a first tracked variable (e.g., one of state 175-a or state 180-a) is relatively high and, alternatively or additionally, generate a control signal and/or direct therapy to decrease stimulation for cases in which activity for a second tracked variable (e.g., the other of state 175-a or state 180-a) is relatively high. [0106] Figs. 3 through 5 include plots 160 (e.g., plot 160-b through plot 160-d), plots 161(e.g., plot 161-b through plot 161-d), and plots 162 (e.g., plot 162-b through plot 162-d) illustrating additional examples of the power 165, threshold value 166, variance 170, threshold variance value 171, state 175, and state 180 in accordance with aspects of the present disclosure.

[0107] Plots 160 (e.g., plot 160-b through plot 160-d), plots 161(e.g., plot 161-b through plot 161-d), and plots 162 (e.g., plot 162-b through plot 162-d) may include aspects of plot 160-a through plot 162-a. For example, plot 160-b through plot 160-d each include an ordinate axis corresponding to power 165 of a bioelectrical signal 126 in units of voltage squared and an abscissa axis corresponding to time in units of milliseconds. Plot 161-b through plot 161-d each include an ordinate axis corresponding to variance 170 in units of voltage squared and an abscissa axis corresponding to time in units of milliseconds. Plot 162-b through plot 162-d each include an ordinate axis corresponding to a state value (e.g. between 0.0 and 1.0) and an abscissa axis corresponding to time in units of milliseconds. [0108] Referring to the example of Fig. 3, plot 160-b illustrates an example of signal drift associated with the power 165-b of the bioelectrical signal 126. Aspects of the system 100 may support mitigating the impact of signal drift by delivering therapy based on the variance 170-b. For example, variance 170-b remains below threshold variance value 171-b (and state 175-b remains in a low state ‘0’), even for cases in which, due to signal drift, the signal noise floor increases above the threshold value 166-b.

[0109] Referring to the example of Fig. 4, plot 160-c illustrates an example of signal drift associated with the power 165-c of the bioelectrical signal 126. Aspects of the system 100 may support mitigating the impact of signal drift by delivering therapy based on the variance 170-c. For example, referring to plot 161-c, the power 165-c does not increase above the threshold value 166-c until about 700 seconds, whereas variance 170-c is above threshold variance value 171-c beginning at about 500 seconds. State 175-c transitions to a high state ‘ 1’ at about 500 seconds, and the computing device 102 may provide control signal 155 for delivering therapy. Accordingly, for example, different from aDBS stimulation techniques based on power 165-c, the computing device 102 (using vaADBS stimulation techniques) may deliver therapy even for cases in which the signal representing power 165-c is below the threshold value 166-c.

[0110] Referring to Fig. 5, variance 170-d is above threshold variance value 171-d until about 515 seconds, at which time the state 175-d transitions to a low state ‘0.’ [0111] Accordingly, for example, aspects of the system 100 may support therapy delivery having increased robustness to signal drift and measurement errors. For example, by mitigating the impact of signal drift, the system 100 may support therapy delivery that more accurately reflects a biological condition of the subject 148. The example case of Fig. 5 illustrates how variance 170-d may be robust to noise/outliers.

[0112] Fig. 6 illustrates an example of the system 100 in accordance with aspects of the present disclosure.

[0113] System 100 includes stimulation device 154 (e.g., an implantable medical device, an external stimulation device, etc.), lead extension 156, one or more leads 157 (e.g., lead 157-a, lead 157-b) with respective sets of electrodes 150 (e.g., electrode 150-a, electrode 150-b) and computing device 102. Computing device 102 may support programming functions associated with the stimulation device 154. Stimulation device 154 may include monitoring circuitry in electrical connection with the electrodes 150 of leads 157, respectively.

[0114] System 100 may support monitor one or more bioelectrical signal 126 of subject 148. For example, stimulation device 154 may include sensing circuitry that senses bioelectrical signal 126 of one or more regions of anatomical element 149 (e.g., brain, nervous tissue, etc.). In some aspects, the signals may be sensed by electrodes 150 and conducted to the sensing circuitry within stimulation device 154 via conductors within the respective leads 157. In some aspects, control circuitry of stimulation device 154 or another device (e.g., computing device 102) monitors the bioelectrical signal 126 within anatomical element 149 of subject 148 to assess neural activation based on metrics described herein (e.g., power 165, variance 170, etc.) of bioelectrical signal(s) 126 and/or perform the other functions referenced herein. Control circuitry of stimulation device 154 or another device (e.g., computing device 102) may control delivery of electrical stimulation or other therapy to anatomical element 149 based on the variance 170 of the bioelectrical signal 126 in a manner that treats a medical condition (e.g., brain condition) of subject 148.

[0115] In some examples, the sensing circuitry of stimulation device 154 may receive the bioelectrical signal 126 from electrodes 150 or other electrodes positioned to monitor bioelectrical signal 126 of subject 148 (e.g., if a housing of stimulation device 154 is implanted in or proximate anatomical element 149, an electrode of the housing can be used to sense bioelectrical signal 126 and/or deliver stimulation to anatomical element 149). Electrodes 150 may also be use4 Hplivpr plpctrical stimulation from stimulation generation circuitry of the stimulation device 154 to target sites within anatomical element 149 as well as to sense bioelectrical signal 126 within anatomical element 149. In some aspects, the sensing circuitry of stimulation device 154 may sense bioelectrical signal 126 via one or more of the electrodes 150 that are also used to deliver electrical stimulation to anatomical element 149. In some other aspects, one or more of electrodes 150 may be used to sense bioelectrical signal 126 while one or more different electrodes 150 may be used to deliver electrical stimulation.

[0116] The bioelectrical signal 126 monitored by stimulation device 154 (and for which variance 170 may be calculated as described herein) may reflect changes in electrical current produced by the sum of electrical potential differences across the anatomical element 149 (e.g., across nervous tissue). Examples of the monitored bioelectrical signal 126 include, but are not limited to, an EEG signal, an ECoG signal, an MEG signal, and/or a LFP signal sensed from within or about one or more regions of anatomical element 149. [0117] According to example aspects of the present disclosure, stimulation device 154 may deliver therapy to any suitable portion of anatomical element 149. In some embodiments, system 100 may deliver therapy to subject 148 to manage a medical condition (e.g., a neurological disorder) of subject 148. For example, system 100 may provide therapy to correct a brain disorder and/or manage symptoms of a neurodegenerative brain condition. Subject 148 may be a human patient. In some cases, however, aspects of the present disclosure may support applying the techniques described herein to other mammalian or non-mammalian non-human patients.

[0118] Stimulation generation circuitry of the stimulation device 154 may generate and deliver electrical stimulation therapy to one or more regions of anatomical element 149 of subject 148 via the electrodes 150 of leads 157, respectively. In the example shown in FIG. 6, system 100 may be referred to as a deep brain stimulation system, and stimulation device 154 may provide electrical stimulation therapy directly to tissue within anatomical element 149 (e.g., a tissue site under the dura mater of anatomical element 149). In some other examples, leads 157 may be positioned to sense brain activity and/or deliver therapy to a surface of anatomical element 149 (e.g., the cortical surface of the brain) or another location in or along the subject 148.

[0119] In the example shown in FIG. 6, stimulation device 154 may be implanted within a subcutaneous pocket below the clavicle of subject 148. In other embodiments, stimulation device 154 may be implanted within other regions of subject 148 (e.g., a subcutaneous pocket in the abdomen nr FnttncFc nf subject 148, proximate the cranium of subject 148, etc.). Implanted lead extension 156 is coupled to stimulation device 154 via a connector component (also referred to as a header). The connector component may include, for example, electrical contacts that electrically couple to respective electrical contacts on lead extension 156. The electrical contacts electrically couple the electrodes 150 carried by leads 157 to stimulation device 154.

[0120] In the example in which anatomical element 149 is the brain of the anatomical element 149, lead extension 156 may traverse from an implant site of stimulation device 154 within a chest cavity of subject 148, along the neck of subject 148 and through the cranium of subject 148 to access anatomical element 149. Stimulation device 154 may be constructed of a biocompatible material that resists corrosion and degradation from bodily fluids. Stimulation device 154 may include a hermetic housing to substantially enclose control circuitry components, such as processor 104, sensing circuitry, therapy programming circuitry, and memory 106. In some implementations, stimulation device 154 and other components (e.g., leads 157) may be implanted in the head of the patient (e.g., under the scalp) and not in the chest and neck regions.

[0121] The system 100 may support configuring the electrical stimulation to be delivered to one or more regions of anatomical element 149 based on one or more criteria (e.g., type of patient condition for which system 100 is implemented to manage, relative level of neural activation identified by variance 170, state 175 (vaDBS state), etc.). In some cases, in the case in which anatomical element 149 is the brain, leads 157 may be implanted within the right and left hemispheres of anatomical element 149. In other examples, one or both of leads 157 may be implanted within one of the right or left hemispheres. Aspects of the present disclosure support implanting one or more leads 157 at different sites (e.g., on or within the cranium). In addition, in some examples, leads 157 may be coupled to a single lead that is implanted within one hemisphere of anatomical element 149 or implanted through both right and left hemispheres of anatomical element 149.

[0122] Leads 157 may be positioned to deliver electrical stimulation to one or more target tissue sites within anatomical element 149 to manage patient symptoms associated with a medical condition of subject 148. Leads 157 may be implanted to position electrodes 150 at desired locations of anatomical element 149. Leads 157 may be placed at any location within or along anatomical element 149 such that electrodes 150 are capable of providing electrical stimulation to target tissue sites of anatomical element 149 during treatment. In some embodiments, lea4« ma 6A nlarpd such that electrodes 150 directly contact or are otherwise proximate targeted tissue of a region of the anatomical element 149.

[0123] In the example shown in FIG. 6, electrodes 150 of leads 157 may be ring electrodes. Ring electrodes may be capable of sensing and/or delivering an electrical field to any tissue adjacent to leads 157 (e.g., in all directions away from an outer perimeter of leads 157). In other examples, electrodes 150 of leads 157 may have different configurations. For example, electrodes 150 of leads 157 may have a complex electrode array geometry that is capable of producing shaped electrical fields. The complex electrode array geometry may include multiple electrodes (e.g., partial ring or segmented electrodes) around the perimeter of each lead 157, rather than a ring electrode. In this manner, electrical brain sensing and/or electrical stimulation may be associated with a specific direction from leads 157 (e.g., in less than the entire outer perimeter of leads 157) to enhance direction sensing and/or therapy efficacy and reduce possible adverse side effects from stimulating a large volume of tissue in the case of stimulation. As such, electrodes can be positioned to sense from one side of a lead and to stimulate targeted tissue and avoid stimulating non-targeted tissue.

[0124] In some embodiments, outer housing of stimulation device 154 may include one or more stimulation and/or sensing electrodes. For example, housing can comprise an electrically conductive material that is exposed to tissue of subject 148 when stimulation device 154 is implanted in subject 148, or an electrode can be attached to housing. In alternative examples, leads 157 may have shapes other than elongated cylinders as illustrated in Fig. 6. For example, leads 157 may be paddle leads, spherical leads, bendable leads, or any other type of shape effective in treating subject 148.

[0125] Stimulation generation circuitry, under the control of processor 104, may generate stimulation signals for delivery to subject 148 via selected combinations of electrodes 150. Processor 104 may control the stimulation generation circuitry according to stimulation programs stored in memory 106 to apply stimulation parameter values (e.g., amplitude, pulse width, timing, pulse rate, etc.) associated with one or more stimulation programs. In some aspects, stimulation generation circuitry generates and delivers stimulation signals to one or more target portions of anatomical element 149 via a select combination of electrodes 150.

[0126] Leads 157 may be implanted at or within a target location of anatomical element 149 via any suitable technique. For example, for the case in which the anatomical element 149 is the brain, the leads 157 may b^p imnlantprl ttirnnoh respective burr holes in a skull of subject 148 or through a common burr hole in the cranium. Leads 157 may be placed at any location within anatomical element 149 such that electrodes 150 of leads 157 are capable of sensing electrical activity of areas of the anatomical element 149 and/or providing electrical stimulation to targeted tissue for treatment. A lead, as the term is used herein, can be in the form of a probe having one electrode or multiple electrodes, and may be applied to devices other than an implantable device.

[0127] In some aspects, processing circuitry of system 100 (e.g., processor 104 of computing device 102 or a processor of stimulation device 154) may control delivery of electrical stimulation by activating electrical stimulation, deactivating electrical stimulation, increasing the intensity of electrical stimulation, or decreasing the intensity of electrical stimulation delivered to anatomical element 149 in association with titrating electrical stimulation therapy. The processing circuitry (and/or control circuitry of the computing device 102 or the stimulation device 154) may support starting, stopping, and/or changing delivery of the therapy in any manner and based on any parameter or finding as discussed herein.

[0128] System 100 may support storing a plurality of stimulation programs (e.g., a set of electrical stimulation parameter values) at a data repository (e.g., database 130). One or more of the stimulation programs may be associated with increasing and decreasing nervous tissue stimulation (e.g., neural activation). Stimulation device 154 or computing device 102 may select a stored stimulation program that defines electrical stimulation parameter values for delivery of electrical stimulation to anatomical element 149.

[0129] According to example aspects of the present disclosure, computing device 102 as a medical device may be a larger workstation or a separate application within another multi -function device, rather than a dedicated computing device. For example, the multifunction device may be a notebook computer, tablet computer, workstation, cellular phone, personal digital assistant or another computing device. The circuitry components of the computing device 102 and other devices described herein can be control circuitry as means for performing functions as described herein (e.g., receiving signals from stimulation device 154 via telemetry, measuring amplitude or power 165 of the signals, calculating variance 170, assessing nervous tissue activation levels).

[0130] Any of the functions described herein may be performed by control circuitry of the stimulation device 154, control circuitry of computing device 102, or by control circuitry that is distributed between stimulation device 154 and computing device 102. For example, the control circuitry of stim’4atinn i 4 may perform the sensing and amplitude (or power 165) measuring functions and transmit data 125 including the amplitude (or power) information to control circuitry of the computing device 102, and computing device 102 may perform variance calculations and other functions described herein. The control circuitry of the computing device 102 may determine therapy settings based on any of the information (e.g., power 165, variance 170, etc.) and transmit the therapy settings to the control circuitry of the stimulation device 154. and the control circuitry of the stimulation device 154 may delivers therapy based on the settings.

[0131] In an example, computing device 102 may be configured for use by the clinician, and computing device 102 may be used to transmit initial programming information to stimulation device 154. This initial information may include hardware information, such as the type of leads 157, the arrangement of electrodes 150 on leads 157, the position of leads 157 within anatomical element 149, initial programs defining therapy parameter values, ranges and/or thresholds for closed loop therapy adjustment, and any other information that may be useful for programming into stimulation device 154. Computing device 102 may also be capable of controlling circuitry of the stimulation device 154 in carrying out the functions described herein (e.g., functions relating to sensing signals, calculating variance 170, assessing nervous tissue activation, and/or delivering therapy). [0132] The clinician may also store therapy programs within stimulation device 154 with the aid of computing device 102. During a programming session, the clinician may determine one or more stimulation programs (e.g., neural activation programs) that may effectively bring about a therapeutic outcome that treats a medical condition (e.g., a brain condition). For example, the clinician may select one or more electrode combinations with which stimulation is delivered to anatomical element 149 to increase or decrease neural activation. During the programming session, the clinician may evaluate the efficacy of the one or more electrode combinations based on one or more findings of functional magnetic resonance imaging (MRI), patient self-reporting, LEP, EEG or other signals. In some examples, the processor of computing device 102 may calculate and display one or more therapy metrics for evaluating and comparing therapy programs available for delivery of therapy from stimulation device 154 to subject 148.

[0133] Computing device 102 may also provide an indication to subject 148 when therapy is being delivered, which may aid the assessment of therapy efficacy. For example, following the delivery of electrical stimulation or sensing one or more metrics (e.g., power 165, variance 170, etc.) being out of a target range, the computing device 102 may deliver one or more prompts to cnhiprt 148 fnr evaluating whether the subject 148 is experiencing symptoms. In some examples, the prompt may include an indication to answer questions presented on the computing device 102. The information may be used to assess whether delivered therapy is manifesting as something observable by the subject 148.

[0134] Fig. 7 illustrates an example of a process flow 700 in accordance with aspects of the present disclosure. In some examples, process flow 700 may implement aspects of a computing device 102 described herein.

[0135] In the following description of the process flow 700, the operations may be performed in a different order than the order shown, or the operations may be performed in different orders or at different times. Certain operations may also be left out of the process flow 700, or one or more operations may be repeated, or other operations may be added to the process flow 700.

[0136] It is to be understood that while a is described as performing a number of the operations of process flow 700, any device (e.g., another computing device 102 in communication with the computing device 102) may perform the operations shown. [0137] The process flow 700 may be implemented by a system including: a processor; and a memory storing instructions thereon that, when executed by the processor, cause the processor to perform operations of the process flow 700.

[0138] At 705, the process flow 700 may include sensing a bioelectrical signal from one or more electrodes in contact with or proximate an anatomical element.

[0139] In some examples, the anatomical element includes nervous tissue.

[0140] In some aspects, the process flow 700 may include setting one or more sensing parameters associated with sensing the bioelectrical signal based on at least one of: a type associated with the bioelectrical signal; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

[0141] At 710, the process flow 700 may include determining a variance value associated with a plurality of metric values of the bioelectrical signal.

[0142] In some examples, the plurality of metric values include power values of the bioelectrical signal.

[0143] At 715, the process flow 700 may include generating a control signal based on comparing the variance value to a threshold variance value.

[0144] In some aspects, generating the control signal may be based on a state associated with delivering therapy with respect to the anatomical element. For example, at 720, the process flow 700 may include setting a with delivering therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value; and a result of comparing the variance value to a threshold range, wherein generating the control signal is based on the state. In another example, at 720, the process flow 700 may include setting the state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold metric value, wherein generating the control signal is based on the state.

[0145] At 725, the process flow 700 may include delivering therapy with respect to the anatomical element (e.g., via a therapy delivery device) in response to the control signal. [0146] In some aspects, delivering the therapy may include: providing electrical signal stimulation to the anatomical element; delivering medication to a subject in association with treating the anatomical element; or both.

[0147] In some aspects, at 730, the process flow 700 may include setting one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values; and a third therapy profile associated with a range corresponding to the plurality of metric values.

[0148] The process flow 700 (and/or one or more operations thereof) may be carried out or otherwise performed, for example, by at least one processor. The at least one processor may be the same as or similar to the processor(s) 104 of the computing device 102 described above. A processor other than any processor described herein may also be used to execute the process flow 700. The at least one processor may perform operations of the process flow 700 by executing elements stored in a memory such as the memory 106. The elements stored in memory and executed by the processor may cause the processor to execute one or more operations of a function as shown in the process flow 700. One or more portions of the process flow 700 may be performed by the processor executing any of the contents of memory, such as image processing 120, a segmentation 122, a transformation 124, and/or a registration 128.

[0149] As noted above, the present disclosure encompasses methods with fewer than all of the steps identified in Fig. 7 (and the corresponding description of the process flow 700), as well as methods that include additional steps beyond those identified in Fig. 7 (and the corresponding description of the process flow 700). The present disclosure also encompasses methods that include o^np from one method described herein, and one or more steps from another method described herein. Any correlation described herein may be or include a registration or any other correlation.

[0150] The foregoing is not intended to limit the disclosure to the form or forms disclosed herein. In the foregoing Detailed Description, for example, various features of the disclosure are grouped together in one or more aspects, implementations, and/or configurations for the purpose of streamlining the disclosure. The features of the aspects, implementations, and/or configurations of the disclosure may be combined in alternate aspects, implementations, and/or configurations other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed aspect, implementation, and/or configuration. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate preferred implementation of the disclosure.

[0151] Moreover, though the foregoing has included description of one or more aspects, implementations, and/or configurations and certain variations and modifications, other variations, combinations, and modifications are within the scope of the disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative aspects, implementations, and/or configurations to the extent permitted, including alternate, interchangeable and/or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and/or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.

[0152] Example aspects of the present disclosure include:

[0153] A system including: a processor; and a memory storing instructions thereon that, when executed by the processor, cause the processor to: sense a bioelectrical signal from one or more electrodes in contact with or proximate an anatomical element; determine a variance value associated with a plurality of metric values of the bioelectrical signal; generate a control signal based on comparing the variance value to a threshold variance value; and deliver therapy with respect to the anatomical element in response to the control signal.

[0154] Any of the aspects herein, wherein the instructions are further executable by the processor to: set a state associated w'Oi Hplivprina therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value; and a result of comparing the variance value to a threshold range, wherein generating the control signal is based on the state.

[0155] Any of the aspects herein, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold metric value, wherein generating the control signal is based on the state.

[0156] Any of the aspects herein, wherein the instructions are further executable by the processor to set one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values.

[0157] Any of the aspects herein, wherein the instructions are further executable by the processor to set one or more sensing parameters associated with sensing the bioelectrical signal based on at least one of: a type associated with the bioelectrical signal; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

[0158] Any of the aspects herein, wherein the instructions executable by the processor to deliver the therapy are further executable by the processor to: provide electrical signal stimulation to the anatomical element; and deliver medication to a subject in association with treating the anatomical element.

[0159] Any of the aspects herein, wherein the plurality of metric values include power values of the bioelectrical signal.

[0160] Any of the aspects herein, wherein the anatomical element includes nervous tissue.

[0161] A system including: a therapy delivery device; a processor; and a memory storing instructions thereon that, when executed by the processor, cause the processor to: sense a bioelectrical signal from one or more electrodes in contact with or proximate an anatomical element; determine a variance value associated with a plurality of metric values of the bioelectrical signal; generate a control signal based on a result of comparing the variance value to a threshold variance value; and deliver therapy with respect to the anatomical element via the therapy delivery device in response to the control signal. [0162] Any of the aspects herein, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value; and a result of comparing the variance value to a threshold range, wherein generating the control signal is based on the state.

[0163] Any of the aspects herein, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold value, wherein generating the control signal is based on the state.

[0164] Any of the aspects herein, wherein the instructions are further executable by the processor to set one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values.

[0165] Any of the aspects herein, wherein the instructions are further executable by the processor to set one or more sensing parameters associated with sensing the bioelectrical signal based on at least one of: a type associated with the bioelectrical signal; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

[0166] Any of the aspects herein, wherein the instructions executable by the processor to deliver the therapy are further executable by the processor to: provide electrical signal stimulation to the anatomical element; deliver medication to a subject in association with treating the anatomical element; or both.

[0167] A method including: sensing, via one or more sensors, a bioelectrical signal from one or more electrodes in contact with or proximate an anatomical element; calculating a variance value associated with a plurality of metric values of the bioelectrical signal; generating a control signal based on a result of comparing the variance value to a threshold variance value; electronically transmitting the control signal; and causing therapy to be delivered to the anatomical element in response to the control signal.

[0168] Any of the aspects herein, further including: setting a state associated with delivering therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value; and a result of comparing the variance value to a threshold range, wherein generating the control signal is based on the state.

[0169] Any of the aspects herein, further including: setting a state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold value, wherein generating the control signal is based on the state.

[0170] Any of the aspects herein, further including: setting one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values.

[0171] Any of the aspects herein, further including: setting one or more sensing parameters associated with sensing the bioelectrical signal based on at least one of: a type associated with the bioelectrical signal; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

[0172] Any of the aspects herein, further including: providing electrical signal stimulation to the anatomical element; and delivering medication to a subject in association with treating the anatomical element.

[0173] Any aspect in combination with any one or more other aspects.

[0174] Any one or more of the features disclosed herein.

[0175] Any one or more of the features as substantially disclosed herein.

[0176] Any one or more of the features as substantially disclosed herein in combination with any one or more other features as substantially disclosed herein.

[0177] Any one of the aspects/features/implementations in combination with any one or more other aspects/features/implementations.

[0178] Use of any one or more of the aspects or features as disclosed herein.

[0179] It is to be appreciated that any feature described herein can be claimed in combination with any other feature(s) as described herein, regardless of whether the features come from the same described implementation.

[0180] The phrases “at least one,” “one or more,” “or,” and “and/or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B, or C,” “A, B, and/or C,” and “A, B, or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.

[0181] The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more,” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising,” “including,” and “having” can be used interchangeably.

[0182] The term “automatic” and variations thereof, as used herein, refers to any process or operation, which is typically continuous or semi-continuous, done without material human input when the process or operation is performed. However, a process or operation can be automatic, even though performance of the process or operation uses material or immaterial human input, if the input is received before performance of the process or operation. Human input is deemed to be material if such input influences how the process or operation will be performed. Human input that consents to the performance of the process or operation is not deemed to be “material.”

[0183] Aspects of the present disclosure may take the form of an implementation that is entirely hardware, an implementation that is entirely software (including firmware, resident software, micro-code, etc.) or an implementation combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.” Any combination of one or more computer-readable medium(s) may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.

[0184] A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0185] A computer readable signal medium may include a propagated data signal with computer readable program code emFndipd terpin for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0186] The terms “determine,” “calculate,” “compute,” and variations thereof, as used herein, are used interchangeably and include any type of methodology, process, mathematical operation or technique.

Claims

CLAIMS What is claimed is:

1. A system comprising: a processor; and a memory storing instructions thereon that, when executed by the processor, cause the processor to: sense one or more electrical signals from one or more electrodes in contact with or proximate an anatomical element; determine a variance value associated with a plurality of metric values of the one or more electrical signals; generate a control signal based on comparing the variance value to a threshold variance value; and deliver therapy with respect to the anatomical element in response to the control signal.

2. The system according to claim 1, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value or a plurality of threshold variance values; and a result of comparing the variance value to a threshold range or a plurality of threshold ranges, wherein generating the control signal is based on the state.

3. The system according to any preceding claim, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold metric value, wherein generating the control signal is based on the state.

4. The system according to any preceding claim, wherein the instructions are further executable by the processor to set one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values.

5. The system according to any preceding claim, wherein the instructions are further executable by the processor to set one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of: a type associated with the one or more electrical signals; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

6. The system according to any preceding claim, wherein the instructions executable by the processor to deliver the therapy are further executable by the processor to: provide electrical signal stimulation to the anatomical element; and deliver medication to a subject in association with treating the anatomical element.

7. The system according to any preceding claim, wherein the plurality of metric values comprise power values of the one or more electrical signals.

8. The system according to any preceding claim, wherein the anatomical element comprises nervous tissue.

9. A system comprising: a therapy delivery device; a processor; and a memory storing instructions thereon that, when executed by the processor, cause the processor to: sense one or more electrical signals from one or more electrodes in contact with or proximate an anatomical element; determine a variance value associated with a plurality of metric values of the one or more electrical signals; generate a control signal based on a result of comparing the variance value to a threshold variance value; and deliver therapy with respect to the anatomical element via the therapy delivery device in response to the control signal.

10. The system according to claim 9, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value or a plurality of threshold variance values; and a result of comparing the variance value to a threshold range or a plurality of threshold ranges, wherein generating the control signal is based on the state.

11. The system according to claim 9 or 10, wherein the instructions are further executable by the processor to: set a state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold value, wherein generating the control signal is based on the state.

12. The system according to any of claims 9 thru 11, wherein the instructions are further executable by the processor to set one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values.

13. The system according to any of claims 9 thru 12, wherein the instructions are further executable by the processor to set one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of: a type associated with the one or more electrical signals; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

14. The system according to any of claims 9 thru 13, wherein the instructions executable by the processor to deliver the therapy are further executable by the processor to: provide electrical signal stimulation to the anatomical element; deliver medication to a subject in association with treating the anatomical element; or both.

15. A method compri sing : sensing, via one or more sensors, one or more electrical signals from one or more electrodes in contact with or proximate an anatomical element; calculating a variance value associated with a plurality of metric values of the one or more electrical signals; generating a control signal based on a result of comparing the variance value to a threshold variance value; electronically transmitting the control signal; and causing therapy to be delivered to the anatomical element in response to the control signal.

16. The method according to claim 15, further comprising: setting a state associated with delivering therapy with respect to the anatomical element, based on at least one of: a result of comparing the variance value to a threshold variance value or a plurality of threshold variance values; and a result of comparing the variance value to a threshold range or a plurality of threshold ranges, wherein generating the control sianal is based on the state.

17. The method according to claim 15 or 16, further comprising: setting a state associated with delivering therapy with respect to the anatomical element, based on a result of comparing one or more metric values of the plurality of metric values to a threshold value, wherein generating the control signal is based on the state.

18. The method according to any of claims 15 thru 17, further comprising: setting one or more parameters associated with delivering the therapy based on at least one of: a therapy profile associated with the variance value; a second therapy profile associated with a range associated with the variance value; and a third therapy profile associated with one or more metric values of the plurality of metric values.

19. The method according to any of claims 15 thru 18, further comprising: setting one or more sensing parameters associated with sensing the one or more electrical signals based on at least one of: a type associated with the one or more electrical signals; the variance value; a range associated with the variance value; and one or more metric values of the plurality of metric values.

20. The method of according to any of claims 15 thru 19, further comprising: providing electrical signal stimulation to the anatomical element; and delivering medication to a subject in association with treating the anatomical element.