[go: up one dir, main page]

WO2025045959A1 - Système de neuromodulation/neurostimulation en boucle fermée pour permettre une fonction corporelle et/ou traitement d'un état corporel d'un mammifère - Google Patents

Système de neuromodulation/neurostimulation en boucle fermée pour permettre une fonction corporelle et/ou traitement d'un état corporel d'un mammifère Download PDF

Info

Publication number
WO2025045959A1
WO2025045959A1 PCT/EP2024/074100 EP2024074100W WO2025045959A1 WO 2025045959 A1 WO2025045959 A1 WO 2025045959A1 EP 2024074100 W EP2024074100 W EP 2024074100W WO 2025045959 A1 WO2025045959 A1 WO 2025045959A1
Authority
WO
WIPO (PCT)
Prior art keywords
stimulation
function
patient
input data
parameters
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/EP2024/074100
Other languages
English (en)
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ecole Polytechnique Federale de Lausanne EPFL
Original Assignee
Ecole Polytechnique Federale de Lausanne EPFL
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ecole Polytechnique Federale de Lausanne EPFL filed Critical Ecole Polytechnique Federale de Lausanne EPFL
Publication of WO2025045959A1 publication Critical patent/WO2025045959A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36062—Spinal stimulation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36128—Control systems
    • A61N1/36135—Control systems using physiological parameters
    • A61N1/36139—Control systems using physiological parameters with automatic adjustment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/05—Electrodes for implantation or insertion into the body, e.g. heart electrode
    • A61N1/0526—Head electrodes
    • A61N1/0529—Electrodes for brain stimulation
    • A61N1/0534—Electrodes for deep brain stimulation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36128—Control systems
    • A61N1/36132—Control systems using patient feedback
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36128—Control systems
    • A61N1/36135—Control systems using physiological parameters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36128—Control systems
    • A61N1/36146—Control systems specified by the stimulation parameters
    • A61N1/36182—Direction of the electrical field, e.g. with sleeve around stimulating electrode
    • A61N1/36185—Selection of the electrode configuration
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/372—Arrangements in connection with the implantation of stimulators
    • A61N1/37211—Means for communicating with stimulators
    • A61N1/37235—Aspects of the external programmer
    • A61N1/37247—User interfaces, e.g. input or presentation means

Definitions

  • the present invention belongs to the technical field of neuromodulation/neurostimulation, in particular for enabling a body function and/or treatment of a body condition of a mammal, preferably a human patient.
  • Said body function can be a motor function, e.g. involving the upper and/or lower limbs, and/or an autonomic function of the patient.
  • Said body condition can be, e.g., chronic pain.
  • Neuromodulation/neurostimulation techniques use one or several patterns of electrical stimulation applied to the nervous system of a subject.
  • spinal cord stimulation to alleviate symptoms of neuromotor disorders such as, but not limited to, spinal cord injury (SCI), stroke, and/or Parkinson’s disease.
  • SCI spinal cord injury
  • stroke stroke
  • Parkinson Parkinson’s disease.
  • Epidural Electrical Stimulation may be used to restore a lower-limb motor function of a patient after SCI or other neurological disorders.
  • FIG. 1a-d An example of use of EES for the purpose of restoring a lower-limb motor function in a patient is illustrated in Figs. 1a-d.
  • EES is delivered through a paddle lead placed over the lumbosacral segments of the spinal cord.
  • Spinal roots innervate the lower-limb muscles in a person-specific rostro-caudal organization (Fig. 1a).
  • Fig. 1 b shows a library of optimized anode and cathode configurations and stimulation frequencies to modulate motor pools associated with key phases of a gait sequence.
  • a sequence of muscle activity underlying walking in healthy people converted into a spatiotemporal map of motor neuron activity that highlights the timing and location of motor hotspot activation, is translated into a pre-programmed sequence of stimulation bursts aiming at reproducing this activation pattern (Fig. 1c).
  • the configurations of the electrodes targeting each hotspot are derived from the library and injected into this template (Fig. 1d). SCI may lead to severe locomotor deficits or even complete leg paralysis.
  • EES electromyographic
  • stimulation bursts are delivered over specific spinal cord locations with precise timing that reproduces the natural spatiotemporal activation of motoneurons during locomotion.
  • These protocols can also be easily adapted for safe implantation of systems in the vicinity of the spinal cord and conduct experiments involving real-time movement feedback and closed-loop controllers [2],
  • EES EES targeting the dorsal roots of lumbosacral segments for restoration of walking in patients with SCI.
  • EES was delivered using multielectrode paddle leads that were originally designed to target the dorsal column of the spinal cord instead of the lumbosacral dorsal root entry zones.
  • an arrangement of electrodes targeting the ensemble of dorsal roots involved in leg and trunk movements would result in superior efficacy, restoring more diverse motor activities after the most severe SCI.
  • a computational framework was established, that informed on the optimal arrangement of electrodes on a new paddle lead, wider and longer than the ones previously used, and guided its neurosurgical positioning.
  • GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained autonomous operations (ten-session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from results of previous sessions to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability [4],
  • WO2018/129280A1 discloses a method including using a dueling bandits algorithm with correlation among stimulation arms to select a batch of stimulation arms for sequential application to a patient during a therapy session.
  • Each of the stimulation arms specifies complex stimulation waveform parameter values.
  • Feedback from applying the stimulation arms to a patient is recorded and used to update feedback reward values corresponding to at least some of the stimulation arms using a stimulation arm correlation index.
  • a second batch of stimulation arms is selected based upon the updated feedback reward values and applied to a patient.
  • the method is iteratively repeated over a number of therapy sessions until an optimal or near optimal batch of stimulation arms (defining complex stimulation waveforms) is determined.
  • this problem was formulated as a K-armed Dueling Bandits problem where K is the total number of decisions.
  • K is the total number of decisions.
  • existing dueling bandits algorithms suffer huge cumulative regret before converging on the optimal arm.
  • the study considered the dueling bandits problem with a large number of arms that exhibit a low-dimensional correlation structure.
  • the problem was motivated by a clinical decision-making process in large decision space.
  • An efficient algorithm CorrDuel was proposed, allowing optimizing the exploration/exploitation trade-off in this large decision space of clinical treatments. Regret bounds were derived, and performance in simulation experiments as well as on a live clinical trial of therapeutic spinal cord stimulation were evaluated.
  • the method described therein is only adapted to be implemented for optimization of electrode configuration while doing an exhaustive search for proper frequency and amplitude, and is based on a dueling approach with patient and/or medical feedback.
  • the method known from WO2018/129280A1 does not contemplate developing a proper objective function.
  • US11413459B2 discloses a system for planning and/or providing neurostimulation for a patient, the system including a pathological spinal cord map storage module for storing at least one pathological spinal cord map describing activation of a spinal cord of a patient; a healthy spinal cord map storage module for storing at least one reference map describing physiological activation of the spinal cord of at least one healthy subject; an analysis module for generating a deviation map, the deviation map describing an activation difference between the pathological spinal cord map and the reference map; and a compensation module for calculating, based on the deviation map, a neurostimulation protocol for compensating the activation difference.
  • US10981004B2 discloses systems for electrical neurostimulation of the spinal cord of a subject in need of nervous system function support.
  • the system comprises a signal processing device configured to receive signals from the subject and operate signalprocessing algorithms to elaborate stimulation parameter settings; one or more multi-electrode arrays suitable to cover a portion of the spinal cord of the subject; and an IPG configured to receive the stimulation parameter settings from the signal processing device and simultaneously deliver independent current or voltage pulses to the one or more multiple electrode arrays, wherein the independent current or voltage pulses provide multipolar spatiotemporal stimulation of spinal circuits and/or dorsal roots.
  • US11413459B2 and US10981004B2 provide systems allowing targeting specific locations of the spinal cord to optimize modulatory outcomes for various motor tasks.
  • both US11413459B2 and US10981004B2 still fail to provide a solution allowing achieving a robust and flexible (semi-)automated optimization of neurostimulation parameters for enabling a body function, e.g. a motor function.
  • the techniques described therein are limited to spinal cord stimulation, while cannot be adapted to other neurostimulation techniques.
  • this known approach only includes a broad disclosure on the use of a Gaussian process in order to optimize stimulation parameters.
  • the present invention relates to a system, especially a closed-loop system, for planning and/or providing neurostimulation/neuromodulation for enabling a body function, especially a motor function and/or autonomic function, and/or treatment of a body condition of a mammal, especially a human patient, said system comprising:
  • a stimulation module configured and adapted to deliver stimulation to said patient according to one or more stimulation parameters through one or more electrodes;
  • a learning module configured and adapted to:
  • (v) define an internal model of stimulation effect space including a prediction of a value for the objective function for each point defined in the stimulation parameter space;
  • the present invention relates to a neuromodulation/neurostimulation system for planning and/or providing neurostimulation/neuromodulation for enabling a body function and/or treatment of a body condition of a mammal.
  • the system is a closed-loop system.
  • said mammal is a human patient.
  • said human patient may have at least one of a motor complete or motor incomplete spinal cord injury (SCI) and/or ischemic or traumatic brain injury and/or a neurodegenerative condition affecting the brain and/or the spinal cord.
  • SCI motor complete or motor incomplete spinal cord injury
  • ischemic or traumatic brain injury and/or a neurodegenerative condition affecting the brain and/or the spinal cord.
  • the body function may be a motor function of the patient.
  • Said motor function may comprise at least one among standing, stepping, reaching, grasping, voluntary changing a position of one or both legs, walking a motor pattern, and/or sitting down or laying down.
  • the body function may be an autonomic function of the patient.
  • Said autonomic function may comprise at least one of a cardiovascular function, body temperature, metabolic processes, sexual function, vasomotor function, breathing, swallowing, and/or voiding the patient’s bladder or bowel.
  • Said body condition can be, for example, chronic pain.
  • the system comprises a stimulation module.
  • the stimulation module comprises one or more electrode arrays.
  • the stimulation module is configured and adapted to deliver stimulation to the patient according to one or more stimulation parameters through the one or more electrodes.
  • the learning module is configured and adapted to:
  • (v) define an internal model of stimulation effect space including a prediction of a value for the objective function for each point defined in the stimulation parameter space;
  • the system further comprises a control module.
  • the control module is configured and adapted to receive at least one set of stimulation parameters from the learning module, and translate the received at least one set of stimulation parameters into a sequence of commands for the stimulation module.
  • this translation can be a direct forwarding of the predicted parameter sets, or a more complex transformation based, e.g., on preferred stimulation timing (e.g., a set repetition rate), safety considerations (e.g., avoiding big stimulation intensity jumps by ensuring amplitude ramping), and/or efficacy considerations (e.g., using knowledge about previous responses to stimulation to estimate minimal amplitude needed to elicit a response).
  • preferred stimulation timing e.g., a set repetition rate
  • safety considerations e.g., avoiding big stimulation intensity jumps by ensuring amplitude ramping
  • efficacy considerations e.g., using knowledge about previous responses to stimulation to estimate minimal amplitude needed to elicit a response.
  • the minimal amplitude to elicit a response can be estimated based on the calibration and then be exploited by the control module to define appropriate stimulation parameters.
  • the invention is based on the basic idea that, by the provision of a learning module configured to implement the above-mentioned steps (i) to (ix), it is possible to personalize and optimize stimulation parameters (e.g., frequency, location, amplitude, pulse-width, burst type, or the like) in a (semi-)automatic manner, without requiring the intervention of highly-trained personnel.
  • stimulation parameters e.g., frequency, location, amplitude, pulse-width, burst type, or the like
  • the system can be easily adapted to any neuromodulation strategy, without being limited to spinal cord stimulation. That is, the system according to the invention is characterized by an enhanced level of flexibility of application in different kinds of treatment.
  • Simple stimulation parameters such as frequency and amplitude, can be encoded (see (iv)) as their real values.
  • encoding (see (iv)) of more complex stimulation parameters requires either a mapping between a list of discrete options and a continuous N- dimensional space, or a method to evaluate similarity between discrete objects (such as optimal transport).
  • the definition of the internal model of stimulation effect space including a prediction of a value for the objective function for each point defined in the stimulation parameter space can be based on prior knowledge, e.g. including one or more among: generic knowledge or trends regarding the effects of different stimulation parameters; information specific to the participant, e.g. implant location, results from personalized computer simulations, or the like; previously acquired sensor and human feedback data, and/or type of model chosen, such as kernel in Gaussian processes.
  • the kernel may encode the relationship between the different electrical parameters by expressing the degree of dependence among each other.
  • frequency and pulses are strictly correlated, but electrode configuration is more independent, as a cathode choice in the wrong spatial location will continue giving erroneous outcomes even with a correct frequency, usually targeting the specific functional movement.
  • the aspects of independence can be expressed with an additive kernel which is a composition of different kernels with relations determined by mathematical operations, such as multiplication and addition.
  • prior knowledge helps the learning module to build an initial internal representation of the stimulation parameter space, which highly speeds up efficient navigation through the parameter space, while acquired data for defining at least one set of stimulation parameters based on a current state of the internal model of stimulation effect space (see (vi)) can be immediately used to update the internal representation of the stimulation parameter space (e.g., in case an actual observation and the assumed prior knowledge do not match).
  • the stopping criterion can be a mathematical formula or logic description of a criterion for stopping the search.
  • the learning module proposes a new set of stimulation parameters to be applied to the patient.
  • the learning module outputs the at least one set of stimulation parameters that lead to the highest values for the objective function (see (ix)).
  • the learning module may be configured and adapted to implement a Gaussian Process Bayesian Optimisation algorithm, preferably a Gaussian Process with Upper Confidence Bound algorithm.
  • Gaussian processes are suitable for low dimensions, low data regimes, and to make sensible interpolation and extrapolation from data.
  • the learning module as well as the acquisition function, may be configured and adapted to implement a deep learning process.
  • Deep learning approaches can be used to create a more complex model which better explains the spinal cord, thereby enabling potentially better predictions.
  • TNP transformer neural process
  • an approach such as TNP can be improved, and the acquisition function can be learned using the same base architecture but trained according to a reinforcement learning approach.
  • the input data may comprise user input data.
  • the user input data may include a qualitative feedback (e.g., good/bad, better/worse, or the like) from the patient and/or operator.
  • a qualitative feedback e.g., good/bad, better/worse, or the like
  • the user input data may include a pseudo-quantified feedback (e.g., by using qualitative scoring scales) from the patient and/or operator.
  • a pseudo-quantified feedback e.g., by using qualitative scoring scales
  • the user input data may include a sporadic feedback (e.g., feeling of discomfort, safe/unsafe, specific assessment of stimulation effects quality, or the like) from the patient and/or operator.
  • a sporadic feedback e.g., feeling of discomfort, safe/unsafe, specific assessment of stimulation effects quality, or the like
  • the patient’s feedback can be provided, e.g., through oral and/or non-verbal communication with other individuals involved in the session.
  • the patient’s feedback can be provided though a user interface, e.g. a keyboard, touch-screen, or the like.
  • the operator’s feedback may as well be provided through a user interface, e.g. a keyboard, touch-screen, or the like.
  • the system may further comprise a sensor means.
  • the sensor means may be arranged either on the patient or in the vicinity of the patient, to acquire data representative of the effects of delivered stimulation.
  • the sensor means may include one or more cameras.
  • the sensor means may include one or more electromyographic sensors.
  • the sensor means may include one or more brain sensors.
  • the sensor means may include one or more kinematic sensors.
  • the senor means may include one or more vesical pressure monitors.
  • the sensor means may include one or more blood pressure sensors.
  • the sensor means may include one or more electrocardiographic sensors.
  • electromyographic signals from different lower-limb muscles can be obtained to quantify muscular activation.
  • camera data can be obtained to observe a movement of the patient.
  • brain recording data can be obtained to detect a feedback from the patient.
  • the input data may comprise sensor data collected through said sensor means.
  • steps (ii) to (vi) based on input data e.g. user input data from the patient and/or operator and/or sensor data, collected during stimulation delivery, it is possible to obtain a more satisfactory and effective outcome.
  • user input data from the patient and/or operator and/or sensor data may be processed by the learning module to update the stimulation parameter space, which can be either shrunk or extended according to the current needs.
  • the user input data from the patient and/or operator and/or sensor data may be processed by the learning module to update the defined objective function.
  • one or more stimulation parameters of the objective function can be adapted, e.g. to handle a trade-off between multiple stimulation effects.
  • user input data from the patient and/or operator and/or sensor data may be processed by the learning module to update the internal representation of the stimulation parameter space.
  • a calibration step may be required to calibrate the sensor means and/or the interpretation of the collected input data.
  • This calibration step may comprise assessment of the effects of a pre-defined sequence of stimulation parameters.
  • the system may comprise a storage module.
  • the storage module may be configured to store input data collected during stimulation delivery.
  • the storage module is operatively connected to the learning module to implement bi-directional communication.
  • the stored input data (user input data from the patient and/or operator and/or sensor data) may thus be stored, analysed and immediately used as additional prior knowledge and/or for adaption of the stimulation parameters.
  • said input data may be collected during the calibration step.
  • the stimulation parameters may include one or more among: stimulation frequency; stimulation amplitude; pulse width; pulse shape; charge distribution; functional stimulation amplitude range; temporal evolution of all parameters during the stimulation; electrode(s) configuration; stimulation timing, and/or anode/cathode configuration with non-binary current distributions.
  • the functional stimulation amplitude range denotes the range of amplitudes that the patient reacts to, which are comfortable and efficient for the purpose of achieving a desired therapeutic outcome.
  • the learning module may be configured and adapted to encode the defined stimulation parameter space for the one or more stimulation parameters by implementing a mapping operation between a list of discrete options and an N-dimensional (often lowerdimensional) space, or by defining a specific metric between discrete objects (such as optimal transport).
  • the acquisition function is optimized by using a list of pre-defined stimulation parameter combinations or by heuristically searching and increasing the size of the search space.
  • a well-built or learnt invertible map to an N-dimensional continuous space allows for efficient optimization of the acquisition function (avoiding high dimensions) to obtain the next stimulation protocol to test, without the need for a pre-defined search space, as well as the use of more standard metrics between continuous objects (such as Euclidean distance).
  • such continuous space can also be a byproduct of deep learning approaches which, with the use of data, may create a specific data-driven embedding space specific for the problem at hand.
  • the learning module may be further configured and adapted to estimate a level of uncertainty for one or more of the predicted values in the internal model of stimulation effect space.
  • estimation of the level of uncertainty can be based on prior knowledge as described above.
  • Estimating the level of uncertainty for one or more of the predicted values is helpful in order to enhance the overall level of accuracy.
  • k(x) is the corresponding kernel matrix and t is the number of tested points.
  • the learning module may be configured and adapted to perform the optimization of the acquisition function (see (vi)) by implementing a designed or learned heuristic that aims to deal with the exploration-exploitation dilemma.
  • the learning module chooses the next stimulation parameters to be tried based on the current state of the internal model of stimulation effect space.
  • the learning module may be configured and adapted to define the objective function based on a multidimensional, mathematical relationship between the received input data (e.g., user input from the patient and/or operator and/or sensor data) and an effect to be induced through delivered stimulation.
  • delivered stimulation can be automatically and effectively optimized to obtain a desired therapeutic effect, based on received input data from the user and/or operator and/or sensor means.
  • the iteration stopping criterion may include an adaptable threshold.
  • the adaptable threshold may be based on a predefined objective function.
  • the adaptable threshold may be based on a predefined limit on the number of iterations.
  • the adaptable threshold may be based on a binary user feedback.
  • the user command for a selected stimulation function may include an indication of a target function.
  • the user command may include one or more among: a predefined set of stimulation parameters; patient-related information; previously-acquired input data, and/or one or more optimization algorithm parameters.
  • said previously-acquired input data may include user input data from the patient and/or operator and/or sensor data.
  • the stimulation module may be configured and adapted to deliver epidural electrical stimulation (EES).
  • EES epidural electrical stimulation
  • the stimulation module may be configured and adapted to deliver transcutaneous electrical stimulation (TENS).
  • TENS transcutaneous electrical stimulation
  • the stimulation module may be configured and adapted to deliver dorsal root ganglion (DRG) electrical stimulation.
  • DRG dorsal root ganglion
  • the stimulation module may be configured and adapted to deliver functional electrical stimulation (FES). In addition or alternatively, the stimulation module may be configured and adapted to deliver brain stimulation.
  • FES functional electrical stimulation
  • the stimulation module may be configured and adapted to deliver brain stimulation.
  • the system according to invention is therefore suitable for use in a wide variety of either invasive or non-invasive therapeutic applications.
  • the system of the invention is also suitable for use in different applications, e.g. for neuro- or muscular stimulation for sports and/or cognitive augmentation.
  • the system described above may be used for implementing a method for planning and/or providing neurostimulation/neuromodulation for enabling a body function, especially a motor function and/or autonomic function, and/or treatment of a body condition of a mammal, especially a human patient, said method comprising the steps of:
  • said stimulation module being configured and adapted to deliver stimulation to said patient according to one or more stimulation parameters through one or more electrodes;
  • said learning module being configured and adapted to implement the following steps:
  • control module being configured and adapted to implement the following steps: receiving at least one set of stimulation parameters from the learning module; translating the received at least one set of stimulation parameters into a sequence of commands for the stimulation module, and forwarding the obtained sequence of commands to the stimulation module.
  • the body function may be a motor function of the patient, e.g. comprising at least one of standing, stepping, reaching, grasping, voluntary changing a position of one or both legs, walking a motor pattern, and/or sitting down or laying down.
  • the body function may be an autonomic function of the patient, e.g. comprising at least one of a cardiovascular function, body temperature, metabolic processes, sexual function, vasomotor function, breathing, swallowing, and/or voiding the patient’s bladder or bowel.
  • the patient may have at least one of a motor complete or motor incomplete spinal cord injury (SCI) and/or ischemic or traumatic brain injury and/or a neurodegenerative condition affecting the brain and/or the spinal cord.
  • SCI motor complete or motor incomplete spinal cord injury
  • ischemic or traumatic brain injury and/or a neurodegenerative condition affecting the brain and/or the spinal cord.
  • the method may further include implementing a Gaussian Process Bayesian Optimization algorithm, preferably a Gaussian Process with Upper Confidence Bound algorithm.
  • the method may include implementing a deep learning process.
  • the input data may include user input data from the patient and/or operator.
  • said user input data include one or more among: a qualitative feedback from the patient and/or the operator; a pseudo-quantified feedback from the patient and/or the operator, and/or a sporadic feedback from the patient and/or the operator.
  • the input data may include sensor data obtained through sensor means of the system.
  • said sensor means of the system may include one or more among: one or more cameras; one or more electromyographic sensors; one or more brain sensors; one or more kinematic sensors; one or more vesical pressure monitors; one or more blood pressure sensors, and/or one or more electrocardiographic sensors.
  • the method may further include storing input data collected during stimulation delivery.
  • This step is implemented by using storage means of the system.
  • the one or more stimulation parameters include at least one among: stimulation frequency; stimulation amplitude; pulse width; pulse shape; charge distribution; functional stimulation amplitude range; temporal evolution of all parameters during the stimulation burst; stimulation amplitude profile; electrode(s) configuration; stimulation timing, and/or anode/cathode configuration with non-binary current distributions.
  • the method may further include the step of implementing a mapping operation between a list of discrete or continuous options and an N-dimensional space, for encoding the defined stimulation parameter space for the one or more stimulation parameters.
  • the method may further include the step of estimating, through the learning module, a level of uncertainty for one or more of the predicted values in the internal model of stimulation effect space.
  • the step of estimating the level of uncertainty for one or more of the predicted values in the internal model of stimulation effect space is carried out by implementing a Gaussian Process.
  • the step of performing an acquisition function for defining at least one set of stimulation parameters based on a current state of the internal model of stimulation effect space is carried out by implementing a designed heuristic or learned acquisition function that aims to deal with the exploration-exploitation dilemma.
  • the method may include defining the objective function based on a multidimensional, mathematical relationship between the received input data (e.g., user input data from the patient and/or operator and/or sensor data) and an effect to be induced through delivered stimulation.
  • the received input data e.g., user input data from the patient and/or operator and/or sensor data
  • the method may include setting the stopping criterion to include an adaptable threshold.
  • the adaptable threshold may be based on one or more among: a predefined objective function; a predefined limit on the number of iterations, and/or a binary user feedback.
  • the user command for a selected stimulation function may include an indication of a target function.
  • said user command may include one or more among: a predefined set of stimulation parameters; patient-related information; previously-acquired input data, and/or one or more optimization algorithm parameters.
  • said previously-acquired input data include user input data (e.g., from the patient and/or operator) and/or sensor data.
  • the method is adapted to carry out invasive and/or non-invasive therapeutic treatments, and in particular to deliver one or more among: epidural electrical stimulation (EES); transcutaneous electrical stimulation (TENS); dorsal root ganglion (DRG) electrical stimulation; functional electrical stimulation (FES), and/or brain stimulation.
  • EES epidural electrical stimulation
  • Tungsten transcutaneous electrical stimulation
  • DRG dorsal root ganglion
  • FES functional electrical stimulation
  • Figs. 1a-e are diagrams showing an exemplary use of EES for restoring a motor function of the lower-limbs of a patient.
  • Fig. 1a Stimulation is delivered through a paddle lead placed over the lumbosacral segments of the spinal cord. Spinal roots innervate the lower-limb muscles in a person-specific rostro-caudal organization.
  • Fig. 1 b Library of optimized anode and cathode configurations and stimulation frequencies to modulate motor pools associated with the key phases of gait.
  • Fig. 1c is exemplary use of EES for restoring a motor function of the lower-limbs of a patient.
  • Fig. 1a Stimulation is delivered through a paddle lead placed over the lumbosacral segments of the spinal cord. Spinal roots innervate the lower-limb muscles in a person-specific rostro-caudal organization.
  • Fig. 1 b Library of optimized anode and cathode configurations and stimulation
  • Sequence of muscle activity underlying walking in healthy people converted into a spatiotemporal map of motor neuron activity that highlights the timing and location of motor hotspot activation, translated into a pre-programmed sequence of stimulation bursts (template) that aims to reproduce this activation pattern.
  • the configurations of electrodes targeting each hotspot are derived from the library and injected into this template.
  • Fig. 1d Software enabling live adjustments of stimulation patterns and parameters based on real-time feedback from muscle activity and kinematic sensors that are synchronized with stimulation sequences.
  • Fig. 1e Walking on a treadmill with stimulation after less than 1 hour of configuration and independent walking between parallel bars less than 1 week after the onset of the therapy, including sequence of stimulation and underlying muscle activity.
  • Fig. 2 is a diagram showing the concept of using automated Gaussian-process based optimization of a limited set of stimulation parameters for targeted single joint movements in a patient, according to the prior art.
  • Fig. 3 is a schematic view of a system for planning and/or providing neurostimulation/neuromodulation for enabling a body function, especially a motor function and/or autonomic function of a mammal, in particular a human patient, according to an embodiment of the present invention.
  • components that are optional and/or alternative are depicted in dashed lines.
  • Fig. 4 is a diagram showing the operative interactions between components of the system of Fig. 3.
  • Figs. 5a-b are diagrams showing EES system setups used with different participants in a clinical trial carried out on human patients (Patients 1-5).
  • Fig. 5a System used for Patients 1-4: here, EES commands from a custom-built stimulation software are transmitted to commercially-available IPG Medtronic Activa® RC via Bluetooth (1) to a module that converts them into infrared signals (2), which are then transferred to the stimulation programmer device (2’).
  • the stimulation programmer transmits EES commands into the IPG via induction telemetry (4), using an antenna (3) taped to the skin of the patient and aligned to the IPG. EES is delivered through the paddle array (5).
  • Fig. 6 is a diagram showing an example of automated optimization process and preliminary results of the above-mentioned clinical trial.
  • (1) Flow diagram. First, the target functional movement is selected. Then, the learning algorithm determines the stimulation to explore, the control module checks the stimulation parameters before delivering the electrical stimuli, then feedback (from sensors, physiotherapist, and/or patient) is collected and the learning algorithm’s knowledge is updated before making the next choice.
  • Figs. 7a-d are diagrams showing preliminary test results on three different patients in the above-referred clinical trial.
  • Fig. 7a Increased search space used to optimize stimulation parameters.
  • Fig. 7b Time needed for the learning algorithm to find 6 predefined target movements (here, results shown for the three patients).
  • Fig. 7c Results of an online run along with algorithm predictions for the most performant cathode locations. The best configurations found are shown alongside polar plots that represent selectivity of movement.
  • Fig. 7d Representation of another online run, in particular on Patient 4. Here, model predictions by iteration for the cathodes, frequency and pulse are shown.
  • Figs. 8a-c are diagrams showing optimization of left knee extension in Patient 1 , in the above-referred clinical trial.
  • Fig. 8a Predictions for cathode locations for a fixed frequency and pulse, using two Gaussian processes with two distinct kernels priors. Darkest areas denote the highest values. The area predicted by the upper Gaussian process is closer to the desired target for left knee extension on Patient 1 . The lower predictions are more spread out.
  • Fig. 8b Comparison with and without transfer knowledge: when the learning algorithm contains more explored points, it converges faster to better stimulation parameters, depicted as a higher reward.
  • Fig. 8c Comparison between predictions error using the two Gaussian process used in (a). With an incorrect kernel, with respect to the problem at hand, the algorithm makes worse predictions, nevertheless given enough data, the difference in the error diminishes. The results shown are based on 10 runs of the algorithm.
  • Figs. 9a-b are diagrams showing further results concerning the above-referred clinical trial.
  • Fig. 9a Reward values of an offline run using a TNP trained on one session data and fitted in 30 points of the actual session it was tested on.
  • Fig. 9b Showcasing of 3 examples with respect to the predictions of the value of quantified EMGs for different situations. Predictions to other patients can be improved the more data collected.
  • Fig. 10 provides diagrams showing further results concerning the abovereferred clinical trial, in particular illustrating optimization of functional movements including: hip flexion, knee extension, and whole leg flexion.
  • 1. Definition of an automated algorithm to optimize EES. 2. Multimodal characterisation of functional movements. 3. An interpretable reward defines movement quality. 4. Rapid personalization of EES parameters. 5. Results are functional and comparable to expert- driven optimization.
  • Fig. 3 provides a schematic overview of a neuromodulation/neurostimulation system 100 according to an embodiment of the invention.
  • the system 100 is a closed-loop system that is configured and adapted for planning and/or providing neurostimulation/neuromodulation for enabling a body function in a mammal.
  • said mammal is a human patient P (Fig. 3).
  • the patient P may have one or more of a motor complete or motor incomplete spinal cord injury (SCI) and/or ischemic or traumatic brain injury and/or a neurodegenerative condition affecting the brain and/or the spinal cord.
  • said body function may be a motor function of the patient P, e.g. standing, stepping, reaching, grasping, voluntary changing a position of one or both legs, walking a motor pattern, and/or sitting down or laying down.
  • said body function may be an autonomic function of the patient P, e.g. a cardiovascular function, body temperature, metabolic processes, sexual function, vasomotor function, breathing, swallowing, and/or voiding the patient’s bladder or bowel.
  • the system 100 may be configured and adapted to deliver invasive and/or non-invasive stimulation.
  • the system 100 may be configured and adapted to deliver at least one among: epidural electrical stimulation (EES); transcutaneous electrical stimulation (TENS); dorsal root ganglion (DRG) electrical stimulation; functional electrical stimulation (FES); and/or brain stimulation.
  • EES epidural electrical stimulation
  • Tungsten S transcutaneous electrical stimulation
  • DRG dorsal root ganglion
  • FES functional electrical stimulation
  • brain stimulation at least one among: epidural electrical stimulation (EES); transcutaneous electrical stimulation (TENS); dorsal root ganglion (DRG) electrical stimulation; functional electrical stimulation (FES); and/or brain stimulation.
  • the system 100 can be easily adapted to a wide variety of either invasive or non- invasive stimulation strategies, thereby showing an enhanced level of flexibility.
  • the one or more stimulation parameters include at least one among: stimulation frequency; stimulation amplitude; pulse width; pulse shape; charge distribution; functional stimulation amplitude range; temporal evolution of all parameters during the stimulation burst; stimulation amplitude profile; electrode(s) configuration; stimulation timing, and/or anode/cathode configuration with non-binary current distributions.
  • the system 100 includes a stimulation module 10 (Figs. 3-4).
  • the stimulation module 10 includes one or more electrodes 12 (Fig. 3).
  • the stimulation module 10 is configured and adapted to deliver stimulation to the patient P according to one or more stimulation parameters, through said one or more electrodes 12.
  • the system further includes a learning module 14 (Figs. 3-4).
  • the learning module 14 is configured and adapted to:
  • (v) define an internal model of stimulation effect space including a prediction of a value for the objective function for each point defined in the stimulation parameter space;
  • control module 16 provided (Figs. 3-4).
  • control module 16 is configured and adapted to receive at least one set of stimulation parameters from the learning module 14, and translate the received at least one set of stimulation parameters into a sequence of commands for the stimulation module 10 (Fig. 4).
  • stimulation parameters e.g., frequency, location, amplitude, pulse-width, burst type, or the like
  • stimulation parameters e.g., frequency, location, amplitude, pulse-width, burst type, or the like
  • the learning module 14 is configured and adapted to implement a Gaussian Process Bayesian Optimisation algorithm, preferably a Gaussian Process with Upper Confidence Bound algorithm.
  • the learning module 14 may be configured and adapted to implement a deep learning process.
  • the input data include user input data, preferably comprising one or more among: a qualitative feedback from the patient P and/or the operator; a pseudo-quantified feedback from the patient P and/or the operator, and/or a sporadic feedback from the patient P and/or the operator.
  • the system 100 further comprises a sensor means 18 (Figs. 3-4).
  • the input data may include sensor data collected through the sensor means 18 (Fig. 4).
  • the sensor means 18 may include one or more among: one or more cameras; one or more electromyographic sensors; one or more brain sensors; one or more kinematic sensors; one or more vesical pressure monitors; one or more blood pressure sensors, and/or one or more electrocardiographic sensors.
  • the system 100 further comprises a storage module 20 (Figs. 3- 4).
  • the storage module 20 is configured and adapted to store input data collected during stimulation delivery.
  • the storage module 20 is operatively connected to the learning module 14 for implementing bi-directional communication (Fig. 4).
  • the learning module 14 is configured and adapted to encode the defined stimulation parameter space for the one or more stimulation parameters by implementing a mapping operation between a list of discrete options and an N-dimensional space, or by defining a specific metric between discrete objects.
  • the learning module 14 is configured and adapted to estimate a level of uncertainty for one or more of the predicted values in the internal model of stimulation effect space.
  • the level of uncertainty for one or more of the predicted values in the internal model of stimulation effect space is estimated by implementing a Gaussian Process.
  • the learning module 14 is configured and adapted to perform said acquisition function by implementing a designed heuristic or learned acquisition function that aims to deal with the exploration-exploitation dilemma.
  • the learning module 14 is configured and adapted to define the objective function based on a multidimensional, mathematical relationship between the received input data (e.g., user input data from the patient P and/or operator and/or sensor data) and an effect to be induced through delivered stimulation.
  • the received input data e.g., user input data from the patient P and/or operator and/or sensor data
  • the iteration stopping criterion includes an adaptable threshold.
  • said adaptable threshold is based on one or more among: a predefined objective function; a predefined limit on the number of iterations, and/or a binary user feedback.
  • the user command for a selected stimulation function includes an indication of a target function.
  • said user command includes one or more among: a predefined set of stimulation parameters; patient-related information; previously-acquired input data, and/or one or more optimization algorithm parameters.
  • said previously-acquired input data include user input data (e.g. from the patient P and/or operator) and/or sensor data.
  • the system 100 may be used for implementing a method for planning and/or providing neurostimulation/neuromodulation for enabling a body function, especially a motor function and/or autonomic function, and/or for treatment of a body condition of a mammal, especially a human patient, such as the method described in detail in the foregoing.
  • control and estimation routines included herein can be used with various system configurations.
  • the control methods and routines disclosed herein may be stored as executable instructions in non-transitory memory and may be carried out by a control unit such as a microcontroller (or a computer) in combination with the described components of the system 100.
  • the specific routines described herein may represent one or more of any number of processing strategies such as event-driven, interrupt-driven, multitasking, multi-threading, and the like.
  • various actions, operations, and/or functions illustrated may be performed in the sequence illustrated, in parallel, or in some cases omitted.
  • the order of processing is not necessarily required to achieve the features and advantages of the example embodiments described herein but is provided for ease of illustration and description.
  • One or more of the illustrated actions, operations and/or functions may be repeatedly performed depending on the particular strategy being used. Further, the described actions, operations and/or functions may graphically represent a code to be programmed into non-transitory memory of a computer readable storage medium in a control unit (e.g. a microcontroller) of the system 100, where the described actions are carried out by executing the instructions in a system including the various hardware components in combination with an electronic control unit.
  • a control unit e.g. a microcontroller
  • a clinical trial has been carried out with respect to a plurality of human patients (Patients 1-5), involved in experimental trials directed at restoring a motor function of the lower-limbs after SCI (www.clinicaltrials.gov identifier NCT02936453 and NCT04632290, respectively).
  • FIG. 5a- b Exemplary stimulation setups used in the context of these clinical trials are shown in Figs. 5a- b.
  • Fig. 5a shows of the stimulation setup used for Patients 1-4
  • Fig. 5b shows the stimulation setup used for Patient 5.
  • EES Epidural Electrical Stimulation
  • FIG. 1a-e An exemplary implementation of targeted EES for restoration of a lower-limb motor function is illustrated in Figs. 1a-e, described in detail in the foregoing.
  • the participants were implanted with a 16-electrode paddle lead over the lumbosacral region of the spinal cord.
  • the first participants were implanted with a commercially-available Medtronic Specify® 5-6-5 paddle lead used off-label, whereas the later participants were implanted with a more targeted, investigational paddle lead.
  • the paddle lead was connected to commercially-available IPG Activa® RC from Medtronic, endowed with real-time triggering capacities through research firmware (Fig. 5a).
  • the EES commands from a custom-built stimulation software were transmitted to the IPG (Activa® RC, Medtronic) via Bluetooth (1) to a module that converted them into infrared signals (2), which were then transferred to the stimulation programmer device (2’).
  • the stimulation programmer transmitted EES commands into the IPG via induction telemetry (4), using an antenna (3) taped to the skin of the patient and aligned to the IPG. EES was delivered through the paddle array (5)
  • This communication chain allowed the control of up to 4 concomitant stimulation waveforms in real-time, with a response latency of approximately 120 ms.
  • EES commands were transmitted to the used IPG (Onward ARC IM ®) via Bluetooth to the main controller (1).
  • the commands were sent via induction telemetry (2) and EES was finally delivered through the paddle array (3).
  • This communication chain allowed the control of up to 6 concomitant and independent waveforms real time.
  • a target functional movement was selected.
  • a learning algorithm determined stimulation to explore, and a control module checked stimulation parameters before delivering the electrical stimuli.
  • a feedback e.g., a user input from the patient and/or operator (such as a physiotherapist), and/or sensor data, were collected.
  • the user e.g., a physiotherapist. Is enabled to alter the importance given to the predefined muscle groups in the optimisation process.
  • the measure can be scaled according on the feedback of the physiotherapist to take into account the specific responses of each patient.
  • the main properties of the learning algorithm are: a. parameter search space; b. objective function, e.g. reward measure; c. encoding of the electrical stimulation, and d. internal model (Gaussian Process and Acquisition Function).
  • a control module ensured that delivered stimulation was always safe and comfortable for the participant (3).
  • the system setup used to deliver EES to each participant is illustrated under (4).
  • the time to convergence corresponds to the amount of time needed for the learning algorithm to explore high-reward stimulations.
  • Figs. 8 refers to left knee extension optimization in Patient 1.
  • Fig. 8a shows predictions for cathode locations, for a fixed frequency and pulse, using two Gaussian processes with two distinct kernels priors.
  • the area predicted by the upper Gaussian process is closer to the desired target for left knee extension in the patient.
  • Fig. 8b provides a comparison with and without transfer knowledge. It may be noted that, when the learning algorithm contains more explored points, it converges faster to better stimulation parameters depicted as a higher reward.
  • Fig. 8c provides a comparison between predictions error using the two Gaussian process used in (a).
  • the search space given to the algorithm was built dynamically, being a subset from a significantly bigger one.
  • PMF probability mass function
  • This electrode configuration along with the kernel of the Gaussian process, was used to sample a subset of points from a larger search space.
  • Fig. 9a shows reward values of an offline run using a TNP trained on one session data and fitted in 30 points of the actual session it was tested.
  • Fig. 9b provides a sselling of 3 examples with respect to the predictions of the value of the quantified EMGs for different situations.
  • Fig. 10 shows further experimental results obtained in the context of above-referred clinical trial, in particular regarding optimization of three functional movements, namely hip flexion, knee extension, and whole leg flexion.
  • a reward measure was established by characterizing motor output through wireless electromyography and kinematics sensors (3.).
  • the results revealed that the present approach allows obtaining a therapeutic outcome that is comparable to that obtained through highly-trained experts, but with a significant reduction in time required to achieve personalized and efficient stimulation (approx, a 20-fold reduction in time) (5.).
  • Electrode(s) (stimulation module)

Landscapes

  • Health & Medical Sciences (AREA)
  • Neurology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Neurosurgery (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Physiology (AREA)
  • Biophysics (AREA)
  • Orthopedic Medicine & Surgery (AREA)
  • Electrotherapy Devices (AREA)

Abstract

La présente invention concerne un système (100), en particulier un système en boucle fermée, pour planifier et/ou fournir une neurostimulation/neuromodulation pour permettre une fonction corporelle, en particulier une fonction motrice et/ou une fonction autonome, et/ou le traitement d'un état corporel d'un mammifère, en particulier d'un patient humain (P).
PCT/EP2024/074100 2023-08-31 2024-08-29 Système de neuromodulation/neurostimulation en boucle fermée pour permettre une fonction corporelle et/ou traitement d'un état corporel d'un mammifère Pending WO2025045959A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP23194415.8 2023-08-31
EP23194415 2023-08-31

Publications (1)

Publication Number Publication Date
WO2025045959A1 true WO2025045959A1 (fr) 2025-03-06

Family

ID=87889975

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2024/074100 Pending WO2025045959A1 (fr) 2023-08-31 2024-08-29 Système de neuromodulation/neurostimulation en boucle fermée pour permettre une fonction corporelle et/ou traitement d'un état corporel d'un mammifère

Country Status (1)

Country Link
WO (1) WO2025045959A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119909316A (zh) * 2025-04-01 2025-05-02 浙江大学 减轻皮肤反应的经颅电刺激系统及方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170080234A1 (en) * 2015-09-21 2017-03-23 Boston Scientific Neuromodulation Corporation Automated program optimization
WO2018129280A1 (fr) 2017-01-05 2018-07-12 California Institute Of Technology Algorithme stochastique à deux bras pour la thérapie par neuromodulation
US10981004B2 (en) 2015-12-22 2021-04-20 Ecole Polytechnique Federale De Lausanne (Epfl) System for selective spatiotemporal stimulation of the spinal cord
US20210346699A1 (en) * 2020-05-08 2021-11-11 Emory University Systems and Methods for Automated Deep Brain Stimulation Programming
US11413459B2 (en) 2017-12-05 2022-08-16 Ecole Polytechnique Federale De Lausanne (Epfl) System for planning and/or providing neurostimulation for a patient

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170080234A1 (en) * 2015-09-21 2017-03-23 Boston Scientific Neuromodulation Corporation Automated program optimization
US10981004B2 (en) 2015-12-22 2021-04-20 Ecole Polytechnique Federale De Lausanne (Epfl) System for selective spatiotemporal stimulation of the spinal cord
WO2018129280A1 (fr) 2017-01-05 2018-07-12 California Institute Of Technology Algorithme stochastique à deux bras pour la thérapie par neuromodulation
US11413459B2 (en) 2017-12-05 2022-08-16 Ecole Polytechnique Federale De Lausanne (Epfl) System for planning and/or providing neurostimulation for a patient
US20210346699A1 (en) * 2020-05-08 2021-11-11 Emory University Systems and Methods for Automated Deep Brain Stimulation Programming

Non-Patent Citations (9)

* Cited by examiner, † Cited by third party
Title
ABRANCHES DE CARVALHO, P.: "Gaussian Processes of Spinal Electrical Stimulation, Master Project Report", DEPARTAMENTO DE CIÊNCIA DE COMPUTADORES FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DO PORTO, 2019
CAPOGROSSO, MWAGNER, F.BGANDAR, J.MORAUD, E.M.WENGER, N.MILEKOVIC, T.SHKORBATOVA, PPAVLOVA, NMUSIENKO, P.BEZARD, E.: "Configuration of electrical spinal cord stimulation through real-time processing of gait kinematics", NAT PROTOC, vol. 13, 2018, pages 2031 - 2061, XP036591313, Retrieved from the Internet <URL:https://doi.org/10.1038/s41596-018-0030-9> DOI: 10.1038/s41596-018-0030-9
DESAUTELS, T.ACHOE, J.GAD, P.NANDRA, M.SROY, R.RZHONG, HTAI, Y.C.EDGERTON, V.RBURDICK, J.W: "An active learning algorithm for control of epidural electrostimulation", IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol. 62, no. 10, 2015, pages 2443 - 2455, XP011668910, DOI: 10.1109/TBME.2015.2431911
MARCO BONIZZATO, ROSE GUAY HOTTIN, SANDRINE L. COTE, ELENA MASSAI, LÉO CHOINI6RE, UZAY MACAR, SAMUEL LAFERRIÈRE, PARIKSHAT SIRPAL,: " Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys.", CELL REPORTS MEDICINE, vol. 4, 2023, pages 101008, ISSN: 2666-3791, Retrieved from the Internet <URL:https://doi.org/10.1016/j.xcrm.2023.101008>
ROWALD, AKOMI, SDEMESMAEKER, R.BAAKLINI, EHERNANDEZ-CHARPAK, S. DPAOLES, EMONTANARO, H.CASSARA, ABECCE, FLLOYD, B: "Activity-dependent spinal cord neuromodulation rapidly restores trunk and leg motor functions after complete paralysis", NATURE MEDICINE, vol. 28, no. 2, 2022, pages 260 - 271, XP037700060, Retrieved from the Internet <URL:https://doi.org/10.1038/s41591-021-01663-5> DOI: 10.1038/s41591-021-01663-5
SARIKHANI PARISA ET AL: "Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's disease and essential tremor", JOURNAL OF NEURAL ENGINEERING, IOP PUBLISHING, BRISTOL, GB, vol. 19, no. 4, 18 August 2022 (2022-08-18), XP020430234, ISSN: 1741-2560, [retrieved on 20220818], DOI: 10.1088/1741-2552/AC86A2 *
SUI, Y. ET AL.: "Correlational dueling bandits with application to clinical treatment in large decision spaces", ARXIV PREPRINT ARXIV:1707.02375 [6, 2017
SUI, Y.YUE, Y.BURDICK, J.W: "Correlational dueling bandits with application to clinical treatment in large decision spaces", ARXIV PREPRINT ARXIV:1707.02375, 2017
WAGNER, F.B.MIGNARDOT, J.-BLE GOFF-MIGNARDOT, C.G.DEMESMAEKER, R.KOMI, S.CAPOGROSSO, MROWALD, A.SEANEZ, I.CABAN, M.PIRONDINI, E.: "Targeted neurotechnology restores walking in humans with spinal cord injury", NATURE, vol. 563, 2018, pages 65 - 71, XP037693446, Retrieved from the Internet <URL:https://doi.org/10.1038/s41586-018-0649-2> DOI: 10.1038/s41586-018-0649-2

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119909316A (zh) * 2025-04-01 2025-05-02 浙江大学 减轻皮肤反应的经颅电刺激系统及方法

Similar Documents

Publication Publication Date Title
AU2018205507B2 (en) Dueling bandits algorithm for neuromodulation therapy
CN107361741B (zh) 神经刺激器装置
CN110121375B (zh) 用于运动功能恢复的感觉信息顺应性脊髓刺激系统
US11413459B2 (en) System for planning and/or providing neurostimulation for a patient
US11752342B2 (en) System for neuromodulation
CN107073265A (zh) 短脉冲宽度刺激
US11992684B2 (en) System for planning and/or providing neuromodulation
US11511116B2 (en) System for planning and/or providing neuromodulation
US11420056B2 (en) System for planning and/or providing neuromodulation, especially neurostimulation
US20250281752A1 (en) System for planning and/or providing neuromodulation
US20250090849A1 (en) System for providing neuromodulation, especially neurostimulation
Desautels et al. An active learning algorithm for control of epidural electrostimulation
WO2025045959A1 (fr) Système de neuromodulation/neurostimulation en boucle fermée pour permettre une fonction corporelle et/ou traitement d&#39;un état corporel d&#39;un mammifère
Kilgore et al. Stimulation for return of upper and lower extremity function
CN121219043A (zh) 用于恢复运动功能和促进神经复原的神经调控/神经刺激系统
Borton Rewiring the nervous system, without wires

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24762665

Country of ref document: EP

Kind code of ref document: A1