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WO2025096904A1 - Système de surveillance prédictive et de diagnostic en temps réel - Google Patents

Système de surveillance prédictive et de diagnostic en temps réel Download PDF

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Publication number
WO2025096904A1
WO2025096904A1 PCT/US2024/054070 US2024054070W WO2025096904A1 WO 2025096904 A1 WO2025096904 A1 WO 2025096904A1 US 2024054070 W US2024054070 W US 2024054070W WO 2025096904 A1 WO2025096904 A1 WO 2025096904A1
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WO
WIPO (PCT)
Prior art keywords
insertable device
ecg
physiological
subject
insertable
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/US2024/054070
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English (en)
Inventor
Patrick WETHINGTON
Samuel J. Asirvatham
Konstantinos SIONTIS
Jason A. TRI
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.)
Cardiac M2
Mayo Foundation for Medical Education and Research
Mayo Clinic in Florida
Original Assignee
Cardiac M2
Mayo Foundation for Medical Education and Research
Mayo Clinic in Florida
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Filing date
Publication date
Application filed by Cardiac M2, Mayo Foundation for Medical Education and Research, Mayo Clinic in Florida filed Critical Cardiac M2
Publication of WO2025096904A1 publication Critical patent/WO2025096904A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0537—Measuring body composition by impedance, e.g. tissue hydration or fat content
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/28—Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
    • A61B5/283—Invasive
    • A61B5/287—Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
    • A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
    • A61B5/686—Permanently implanted devices, e.g. pacemakers, other stimulators, biochips
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Definitions

  • Non-communicable diseases can also be referred to as chronic diseases.
  • this disclosure relates to systems that include at least one of surface and insertable systems for surveillance, clinical evaluation, diagnosis, and predictive analytics of NCDs.
  • NCDs also known as chronic diseases, are medical conditions that are associated with long durations and slow progress. NCDs can be non-infectious and are the result of several factors, including genetic, physiological, behavioral, and environmental factors. NCDs are the leading cause of death worldwide (41 million), and can include deaths from cardiovascular diseases, cancers, respiratory diseases, and diabetes.
  • NCDs includes a wide range of health problems, such as hepatic, renal, and gastroenterological diseases, endocrine, hematological, and neurological disorders, dermatological conditions, genetic disorders, trauma, mental disorders, and disabilities (e.g., blindness and deafness).
  • NCDs clinical evaluation and diagnosis does not occur until patients become clinically symptomatic and/or a clinical event(s) has/have occurred over time.
  • Rear- view clinical evaluation and diagnosis of NCDs has been improved by the introduction of a variety of wearable and insertable devices which record and report clinical related events. However, these improvements are limited to rear-view analysis of patient data thus limiting the capacity of future clinical interventions.
  • the insertable device may include: an insertable device transceiver; a power management and storage unit; a plurality of electrodes configured to form a plurality of electrocardiogram (ECG) leads; one or more insertable device processors; and an insertable device memory.
  • ECG electrocardiogram
  • the system also includes an external device may include: an external device transceiver; one or more external device processors; and an external device memory storing instructions that, when executed by the one or more external device processors, cause the one or more external device processors to perform operations may include: selecting at least one of the plurality of ECG leads formed by the plurality of electrodes of the insertable device; receiving physiological measures of the subject acquired by the selected at least one ECG lead; analyzing, via a neural network trained using a plurality of data sets that represent one or more physiological events, the physiological measures; determining, based on the analyzed physiological measures, a likelihood that one or more physiological events will occur at a time of data acquisition or at a future time; sending, responsive to the determined likelihood meeting an event prediction threshold, a notification to the subject; and sending, to the insertable device and responsive to the determined likelihood not meeting the event prediction threshold, a request to the insertable device to acquire additional physiological measures at a delay interval.
  • an external device may include: an external device transceiver; one or more external device processor
  • Such a system may optionally include one or more of the following features.
  • the insertable device continuously collects physiological measures.
  • the operations continuously receives and analyzes the collected physiological measures.
  • the system may include one or more surface sensors, the one or more surface sensors configured to be removably attached to an outer surface of the skin on the subject.
  • the operations further may include: receiving physiological measures of the subject from the one or more surface sensors; analyzing, via the neural network, the physiological measures from the surface sensors and the insertable device; determining, based on the analyzed physiological measures, the likelihood that one or more physiological events will occur Attorney Docket No.07039-2212WO1 / 2021-489 at the future time.
  • the insertable device is configured to be positioned under the skin of the subject and near a heart of the subject.
  • the physiological measures include measures representative of an electrocardiogram (ECG).
  • the plurality of data sets include ECG data sets that have one or more cardiac events and ECG data from intervals preceding the cardiac events and intervals succeeding the cardiac events.
  • the neural network determines, based on the ECG data and from intervals preceding the cardiac events of the ECG data sets and the physiological measures, the likelihood that one or more physiological events will occur at a time of data acquisition or at a future time.
  • the at least one bipolar segment is selected from the plurality of bipolar segments of the insertable device by: acquiring a set of ECG signals along respective vectors formed by each of the plurality of ECG leads; identifying a group of ECG signals from the set of ECG signals that include a complete PQRST wave; identifying a group of ECG signals from the set of ECG signals that lack a complete PQRST wave; and selecting the at least one bipolar segment from the group of ECG signals that include a complete PQRST wave.
  • Some embodiments described herein include a system for conducting surveillance of a physiological condition of a subject an insertable device that is configured to be inserted under a skin of the subject, the insertable device may include: an insertable device transceiver; a power management and storage unit; a plurality of electrodes configured to form a plurality of electrocardiogram (ECG) leads; one or more insertable device processors; and an insertable device memory storing instructions that, when executed by the one or more external device processors, cause the one or more external device processors to perform operations may include: selecting at least one of the plurality of ECG leads formed by the plurality of electrodes of the insertable device; receiving physiological measures of the subject acquired by the selected at least one ECG lead; analyzing, via a neural network trained using a plurality of data sets that represent one or more physiological events, the physiological measures; determining, based on the analyzed physiological measures, a likelihood that one or more physiological events will occur at a time of data acquisition or at a future time; sending, responsive to the determined likelihood meeting an
  • an external device may include: an external device transceiver; one or more external device processors; and an external device memory storing instructions that, when executed by the one or more external device processors, cause the one or more external device processors to perform operations may include: selecting at least one of a plurality of ECG leads formed by a plurality of electrodes of an insertable device that are configured to form a plurality of electrocardiogram (ECG) leads; receiving physiological measures of the subject acquired by the selected at least one ECG lead; analyzing, via a neural network trained using a plurality of data sets that represent one or more physiological events, the physiological measures; determining, based on the analyzed physiological measures, a likelihood that one or more physiological events will occur at a time of data acquisition or at a future time; sending, responsive to the determined likelihood meeting an event prediction threshold, a notification to the subject; and sending, to the insert
  • the disclosed systems include insertable and/or surface electrodes and sensors combined with insertable devices which can facilitate the surveillance, clinical evaluation, diagnosis and predictive analytics of NCDs.
  • the disclosed systems can facilitate improved electrocardiographic NCDs signal resolution with recording of numerous ECG, EEG and neural signals and vectors resulting in more accurate collection of pre-clinical event indicators for NCDs (inputs).
  • Insertable devices can be positioned at the time of device insertion and can be used continuously. The insertion can be positioned under the skin of the subject and near the heart of the subject.
  • the disclosed systems can facilitate data output and integration of the recorded signals for analysis by artificial intelligence (AI) algorithms for pre-clinical event determination and also for AI-enabled diagnostic and prognostic solutions.
  • AI artificial intelligence
  • the disclosed systems facilitate the real time surveillance of NCD-related data, and the real-time surveillance along with specifically trained algorithms for detection, prevention, and intervention improves the efficiency and processing time of the data obtained by the one or more electrodes and sensors, thereby facilitating rapid intervention by predicting NCD events before they occur.
  • Attorney Docket No.07039-2212WO1 / 2021-489 Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
  • FIG. 1A is a schematic diagram of an example system for subject surveillance in accordance with some embodiments.
  • FIG. 1A is a schematic diagram of an example system for subject surveillance in accordance with some embodiments.
  • FIG. 1B is a schematic diagram of an example training system of the example system for subject surveillance of FIG.1A.
  • FIG.2A is a schematic diagram of an example insertable device in accordance with some embodiments.
  • FIG.2B a schematic diagram of the insertable device of FIG.2A with example segment vectors illustrated in accordance with some embodiments.
  • FIG. 3 is a schematic diagram of an example insertable device in a collapsed position in accordance with some embodiments.
  • FIG.4A is a schematic diagram the insertable device of FIG.3 in an expanded position.
  • FIG. 4B is a schematic diagram of an example insertable device in a collapsed position in accordance with some embodiments.
  • FIG.4C is a schematic diagram the insertable device of FIG.4B in an expanded position.
  • FIG.4D is a schematic diagram an insertable device in an expanded position.
  • FIG. 5 is a schematic diagram of an example insertable device in accordance with some embodiments.
  • FIG. 6 is a schematic diagram of an example insertable device in accordance with some embodiments.
  • FIG.7 is a block diagram of computing devices that may be used to implement the systems and methods described in this document, as either a client or as a server or plurality of servers.
  • FIG. 8 is a table that includes clinical examples in accordance with some embodiments. Like reference numbers represent corresponding parts throughout.
  • Embodiments of this disclosure include a system for subject surveillance that includes an insertable device, and external device, or both.
  • the insertable device can include an insertable device transceiver, a power management and storage unit, a plurality of electrodes and/or sensors that are configured to be grouped in a plurality of bipolar segments, each bipolar segment includes a unique segment vector that extends between each electrode of the bipolar segment, one or more insertable device processors, and an insertable device memory.
  • the plurality of electrodes can thus form a set of ECG leads.
  • the external device includes one or more external device processors and an external device memory storing instructions that, when executed by the processors, cause the processors to perform operations.
  • the external device and the insertable device can wirelessly communicate with each other between the insertable device transceiver and the external device transceiver.
  • the systems described herein facilitate subject surveillance that can alert subjects and their physicians before (forward-looking) a NCD clinical event(s) is/are likely to occur (outputs)
  • the systems described herein facilitate real-time predictive surveillance, clinical evaluation, diagnosis and predictive analytics (outputs) for subjects with NCD(s) or subjects that are of high NCD(s) risk that can benefit from surveillance, clinical evaluation, early detection, prediction, diagnosis and management of NCDs.
  • NCD signals can be performed with deep-learning convolutional neural network methodology which recognizes patterns of electrical and non-electrical activity that can be associated with imminent adverse events via supervised and unsupervised training process.
  • the wearable and/or insertable device can include a feedback circuit that analyzes the electrocardiogram signals in real time over a series of signals to detect potential arrhythmias, autonomic Attorney Docket No.07039-2212WO1 / 2021-489 tone, or other variables/clinical events based on various sensor inputs, and will signal the transmission of indicators via wireless transmission.
  • the system can provide continuous surveillance and establish templates for a particular patient and patient profile so that numerous inputs may serve as a learning algorithm to understand future events and predict alarming trends.
  • FIGS. 1A and 1B show an example of a system 100 for subject surveillance.
  • the system 100 includes a training system 101, inputs 103, and outputs 105.
  • the training system 101 that includes an insertable device 102 and one or more external devices (e.g., external device 104).
  • the insertable device 102 is positioned under the skin of a subject 106, and is positioned near the heart 111 of the subject 106.
  • the system 100 can include the insertable device 102 that can be a wireless insertable cardiac respiratory surveillance device.
  • the system 100 can facilitate real-time high-resolution (e.g., above 240 Hz) AI-enabled forward-looking surveillance and analytics for the detection and predictive surveillance of cardiac and non-cardiac events.
  • the system 100 can utilize a series of electrodes distributed under the skin close to the heart (e.g., at the insertable device 102) to facilitate atrial to ventricular signal and vector examination with forward-looking surveillance and analytics.
  • the system 100 facilitates real-time predictive surveillance, clinical evaluation, diagnosis, and predictive analytics (outputs).
  • the training system 101 is provided with inputs 103.
  • the inputs 103 include the patient’s NCD(s) medical history 107 and associated risk factors 109.
  • the NCD(s) medical history 107 can include history of cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes.
  • the inputs 103 include clinical markers 111 such as NCD cardiac electrical signals and non-cardiac signals collected on a beat to beat and breath to breath basis across multiple electrodes and/or sensors.
  • the system s inputs 103 and outputs 105 continuously enable a training model (e.g., of the training system 101) which facilitates exponential learning refining and improving the system’s real-time predictive surveillance, clinical evaluation and predictive analytics of NCD(s) of a subject.
  • the system 100 facilitates the output 105 (real-time predictive surveillance and analytics) that is determined and provided by real-time beat by beat breath by breath signal input collection, speed of prediction (edge AI design) and exponential improvement over accumulated signal and event collection overtime.
  • the insertable device 102 can be a wireless insertable surveillance device that includes a thin, flexible, and biocompatible material, and the insertable device 102 is operable to record cardiac and non-cardiac signals/activity (e.g., biosignals, nerve, physiological measures).
  • the external device 104 includes an external device transceiver that facilitates wireless communication with the insertable device 102.
  • the biosignals and/or physiological measures recorded by the insertable device 102 can be wirelessly communicated to the external device 104.
  • the system 100 can be configured to collect data from one or more subjects 106, as will be described in further detail below. The data collected by the insertable device 102 can be communicated throughout the system 100.
  • the data from the insertable device 102 can be sent to a cloud artificial intelligence (AI) platform 110, sent to the edge interface 116, and output to the external device 104.
  • AI artificial intelligence
  • the external device 104 can include an external device transceiver that receives the biosignals and/or physiological measures from the insertable device 102 (e.g., as signals that include data sets or data streams).
  • the external device 104 can communicate with the cloud AI platform 110 and the edge interface 116.
  • the edge interface 116 can include one or more edge interface processors 117 that can facilitate the operations of the edge interface 116.
  • the edge interface 116 can include a memory unit that can be integrated with the operating computing hardware, or separately at the external device.
  • the memory unit may include an-analog-to digital Attorney Docket No.07039-2212WO1 / 2021-489 converter (ADC) to convert an analog biosignal and store it in a memory bank as bits.
  • ADC attorney Docket No.07039-2212WO1 / 2021-489 converter
  • the memory unit can also store instructions that, when executed by the processors, cause the processors to perform operations described in more detail below.
  • the cloud AI platform 110 can facilitate the training of the AI model(s) based on data from the insertable device 102 and the inputs 103.
  • the cloud AI platform 110 can facilitate continuous training of the AI model(s) responsive to continuous data collection from the insertable device 102.
  • the system 100 can include a plurality of surface electrodes and/or sensors 120.
  • the one or more surface electrodes and/or sensors 120 can be removably attached to an outer surface of the skin of the subject 106.
  • the one or more surface electrodes and/or sensors 120 can independently collect biosignals and/or physiological measures from the insertable device 102.
  • the one or more surface sensors 120 can interface with the insertable device 102 to collect biosignals and/or physiological measures from the subject 106.
  • the one or more surface sensors 120 can facilitate patient-specific signal collection and impedance based measurements.
  • the one or more surface electrodes/sensors 120 can be ECG patches, ultrasound sensors, impedance sensors, or wearable devices that facilitate data collection from the subject 102.
  • the data collection from the one or more surface sensors 120 in addition to the data collected insertable device 102 can facilitate the collection of subcutaneous biosignals and surface biosignals from the subject 106.
  • the combination of these signals can allow for higher resolution signals to be acquired allowing the acquisition of EEG signals, ECG, and or neural signals to be correlated with neurological events.
  • FIGS. 2A-B illustrates an example of the insertable device 102 removed from the subject.
  • the insertable device 102 can include an insertable device transceiver 124.
  • the insertable device transceiver 124 can facilitate communication between the insertable device 102 and the external device 104.
  • the insertable device transceiver 124 can include a wireless transmission and reception element that converts a wireless signal between the wireless and wired domain.
  • the wireless transmission/reception element may be implemented as an antenna to capture or radiate a wireless signal in the radio frequency spectrum, or it can be implemented as a coil to capture or radiate a wireless signal through inductive coupling.
  • the insertable device transceiver 124 can facilitate short-range communication, such as using a Bluetooth, WiFi, or other such transceivers.
  • the insertable device 102 can include a power management and storage unit 126.
  • the power management and storage unit 126 can be a battery that provides power to the insertable device 102. In some embodiments, the power management and storage unit 126 can be rechargeable. The power management and storage unit 126 can include a rechargeable battery that facilitates long-term rechargeable surveillance.
  • the insertable device 102 can include one or more insertable device processors 128 and an insertable device memory 129.
  • the memory 129 can be integrated with the insertable device transceiver 124 to store the biosignal and/or physiological measures acquired from the plurality of electrodes 130a-i.
  • the memory 129 can include an-analog-to digital converter (ADC) to convert an analog biosignal and store it in a memory bank as bits.
  • ADC analog-to digital converter
  • the insertable device 102 includes a plurality of electrodes 130a-i that can collect a plurality of biosignals and/or physiological measures from the subject.
  • the insertable device 102 can include nine electrodes spaced apart across the insertable device 102. While the plurality of electrodes 130a-i nine electrodes, other numbers of electrodes can be implemented.
  • the insertable device can include between 5 and 10 electrodes, between 10 and 20 electrodes, between 20 and 30 electrodes, or more electrodes.
  • the insertable device 102 can include an additional sheathed extension that has another plurality of electrodes that can interface with the plurality of electrodes 130a-i to expand the grid of electrodes.
  • the insertable device 102 facilitates post insertion selection of electrode and/or sensor pairs via surface or remote programing e.g. selection of electrode pair to optimize vector(s) and/or bypass inactive electrodes, etc.
  • the insertable device 120 facilitates individual and/or combined utilization of electrodes and sensors to for collection of electroanatomic cardiac signal inputs
  • the plurality of electrodes 130a-i can be grouped in a plurality of bipolar segments or unipolar electrodes that have a reference to the catheter or Winston central terminal.
  • a bipolar segment can include two of the plurality of electrodes 130a-i (e.g., electrodes 130a and 130b), where each electrode of the bipolar segment serves as a pole of the bipolar segment.
  • the insertable device 102 can include a plurality of bipolar segments, where each electrode of the plurality of electrodes 130a-i can be part of a bipolar segment with any of the other electrodes of the plurality of electrodes 130a-i.
  • electrode 130a can form a bipolar segment with any of electrodes 130b- Attorney Docket No.07039-2212WO1 / 2021-489 130i
  • electrode 130b can form a bipolar segment with any of the electrodes 130a and 130c-i
  • electrode 130c can form a bipolar segment with any of the electrodes 130a-b and 130d-i, and so on for the remaining electrodes 130d-i.
  • the plurality of bipolar segments facilitate recording of biosignals and/or physiological measures between each pole of each respective bipolar segment.
  • Each bipolar segment includes a unique segment vector (see e.g., the plurality of dashed lines in FIG.2B) that extends between each electrode of the bipolar segment.
  • the plurality of segment vectors facilitate recording of biosignals and/or physiological measures for each bipolar segment by extending between each pole of each respective bipolar segment.
  • a segment vector 132a extends between the electrodes 130a and 130b of the bipolar segment.
  • a segment vector 132b extends between the electrodes 130b and 130c of a bipolar segment that includes electrodes 130b and 130c.
  • a segment vector 132c extends between the electrodes 130d and 130f of a bipolar segment that includes electrodes 130d and 130f.
  • the examples of segment vectors 132a-c can be applied to each bipolar segment of the insertable device 102.
  • each electrode 130a-i can have a segment vector that extends to each of the other electrodes.
  • each electrode of the plurality of electrodes 130a-i includes eight segment vectors that extend between each of the other electrodes (i.e., poles) of the bipolar segments.
  • the insertable device 102 can be insertable under the surface of the skin of a subject. In some embodiments, the insertable device 102 can be inserted and positioned under the skin of a patient during a minimally invasive outpatient (clinical based) procedure.
  • the insertable device 102 can include a leading edge 134 that can facilitate insertion and attachment of the insertable device 102 to the patient under the skin.
  • the insertable device 102 can include a tab 136 that facilitates a gripping surface for insertion and removal of the insertable device 102.
  • the insertable device 102 can include a connection to cauterize via recording electrodes strategically placed at sites of fibrogenitc areas, hook or twine to pull for removal, etc.
  • Attorney Docket No.07039-2212WO1 / 2021-489 Referring to FIGS.1, 2A, and 2B, the system 100 can facilitate surveillance of the subject 106.
  • the system 100 can facilitate subject surveillance by utilizing instructions executed by the system (e.g., via the operating computing hardware 116 of the external device 104 and the insertable device processors 128).
  • the instructions can include selecting at least one bipolar segment from the plurality of bipolar segments of the implant.
  • the selected bipolar segment(s) can be selected based on a quality, resolution, noise, and clarity of the biosignal and/or physiological measures acquired by the selected bipolar segment(s).
  • the system 100 can test the plurality of segment vectors and bipolar segments to acquire a plurality of data sets that reflect the signals acquired for each of the bipolar segments.
  • the at least one bipolar segment is selected from the plurality of data sets of bipolar segments of the insertable device 102.
  • the plurality of data sets can include ECG signals along each segment vector of the plurality of bipolar segments.
  • the system 100 can identify a group of ECG signals from the set of ECG signals that include a complete PQRST wave.
  • the data sets that include the complete PQRST wave can facilitate improved surveillance of the subject 106.
  • the data sets that include the complete PQRST waves can indicate that the segment vectors are aligned to facilitate cardiac data acquisition that illustrates complete signals.
  • the system 100 can identify a group of ECG signals from the set of ECG signals that lack a complete PQRST wave.
  • the system 100 can select the at least one bipolar segment from the group of ECG signals that include a complete PQRST wave. Selection of one or more segment vectors and bipolar segments of the insertable device 102 facilitates customization and tailoring of the biosignals acquired by the insertable device 102 to improve signal resolution and clarity.
  • the system 100 can receive biosignals and/or physiological measures of the subject 106 recorded along the selected segment vector(s) of at the at least one selected bipolar segment of the insertable device 102.
  • the insertable device 102 can continuously collect biosignals and or/physiological measures from the selected bipolar segments via the electrodes of the plurality of electrodes 130a-i that are selected.
  • the physiological measures include an electrocardiogram (ECG) and in some EEG signals, ECG, and or neural signals to be correlated with neurological events.
  • ECG electrocardiogram
  • the system 100 can both provide surveillance of the subject 106 and facilitate AI-enabled real-time analytics, detection, and predictive surveillance of the NCD Attorney Docket No.07039-2212WO1 / 2021-489 subject 106.
  • the system 100 can record and store NCD event(s) for real-time physician clinical evaluation and diagnosis.
  • the acquired data can be transmitted wirelessly to a patient’s smartphone and uploaded into a cloud-based service for patient and physician review.
  • the cloud storage can also enable further post- hoc AI analyses of collected data.
  • the high-resolution electrocardiographic digital signals recorded by the insertable device 102 facilitates real-time artificial intelligence (AI) analysis of these signals using edge AI techniques.
  • the system 100 can encompass the edge AI design thus facilitating computation at the source of the insertable device 102 and/or the external device 104 to facilitate faster computation.
  • the system 100 can analyze, via a neural network trained using a plurality of data sets that include one or more physiological events, the biosignals and/or physiological measures.
  • the data source 118 can communicate a plurality of data sets to the training computing hardware 110 that can facilitate a training of the neural network.
  • the neural network can leverage AI algorithms utilized for ECG applications offering the same or similar algorithms applied to both surface (ECG) and subcutaneous collected electrical signals.
  • the system 100 can determine, based on the analyzed physiological measures, a likelihood that one or more physiological events will occur at a time of data acquisition or at a future time.
  • the biosignals and/or physiological measures can be analyzed for detection, prediction, and surveillance of NCD events.
  • AI artificial intelligence
  • the examples include the prediction of cardiac arrhythmia events (for example, imminent atrial fibrillation), and non-arrhythmic events (for example, imminent hypoglycemia or myocardial infarction in a diabetic patient).
  • Other applications can include wellness and stress management.
  • Multiple sensors (e.g., surface sensors 120 and insertable device 102) of the system 100 can facilitate impedance-based surveillance of respiratory activity along with heart signal collection that facilitates an auto feedback loop (e.g. patient’s respiratory rate increasing due to anxiety being a precursor to a rising heart rate).
  • the system 100 dual sensors and algorithms can alert the subject and/or a physician ahead of time of impending anxiety attack that the patient is about to have such that the patient may then apply stress reduction interventions.
  • MI myocardial infarction
  • CVF Congestive Heart Failure
  • the system can interact with other cardiac implanted devices (pacemakers or defibrillators) to provide surveillance of changes in thoracic impedance to evaluate the patient’s fluid status e.g. prior to when the patient reaches a fluid overload state the system could examine changes in impedance, respiratory and heart rates alerting the patient’s physician to modify medication to prevent CHF related exacerbations/hospital visits.
  • CHF Congestive Heart Failure
  • NCDs cardiovascular diseases
  • CVDs cardiovascular diseases
  • RA rheumatoid arthritis
  • cerebrovascular disease osteopenia/osteoporosis
  • degenerative disc disease sarcopenia and frailty
  • depression cognitive impairment
  • neurodegenerative diseases among others.
  • CVDs cardiovascular diseases
  • RA rheumatoid arthritis
  • Another example of the system’s 100 real-time predictive surveillance, clinical evaluation, diagnosis and predictive analytics (outputs) is as follows.
  • the subject 106 can be at high-risk of having Atrial Fibrillation and associated stroke risk.
  • the system 100 records (e.g., via the insertable device 102 and optionally the surface sensors 120) the subtle electrical signal changes that precede an AF event (based on AI analytics).
  • the system 100 provides an alert to the patient and physician for initiation of anticoagulation and increased arrhythmia awareness.
  • the high-resolution recording benefits the physician by providing maximum atrial and ventricular signal collection thus providing more vectors (via the plurality of segment vectors) for the physician to diagnose the rhythm during the diagnosis stage.
  • the improved atrial (p-wave) visibility (via the plurality of segment vectors) facilities supraventricular tachycardias to be deciphered from ventricular arrhythmias e.g. AT, AFL, AF vs. VT.
  • the system 100 can optionally receive physiological measures of the subject from the one or more surface sensors 120.
  • the physiological measures from the surface sensors 120 can be separately analyzed from the data acquired by the insertable device 102, or the physiological measures from the surface sensors 120 can be concurrently analyzed with the data acquired by the insertable device 102.
  • the system 100 can continually receive and analyze the collected physiological measures.
  • the insertable device 102 can continuously collect and send biosignals and/or physiological measures to the external device 104.
  • the system 100 can provide surveillance of electrical activity of the heart in a continuous storing of information in a circular memory as electrocardiograms Attorney Docket No.07039-2212WO1 / 2021-489 (ECGs).
  • the system 100 can be implemented for long-term cardiac surveillance in patients with suspected, infrequent episodic arrhythmias that are not easy to identify with external patch and/or insertable rhythm monitors.
  • the “learning” aspect of the algorithm on data overtime combined with the multiple vector surveillance capability enabled via the plurality of segment vectors will lower false positive and false negatives.
  • the system 100 sends a notification to the subject and/or their physician in response to a determined likelihood exceeding an event prediction threshold.
  • the analysis of the biosignals and/or physiological measures by the neural network can identify one or more trends, predictive events, measured levels, or otherwise predictive characteristics of the biosignals and/or physiological measures that are analyzed and a likelihood of a future event is calculated.
  • the system 100 sends notifications and/or alerts to patients and/or healthcare providers (e.g., nurses, physicians, care team members, pharmacists, etc.)
  • patients and/or healthcare providers e.g., nurses, physicians, care team members, pharmacists, etc.
  • alerts and/or notifications programmed as (e.g., alert and notification settings): an audio alert; as an audio and vibration alert (e.g., where the system 100 vibrates) along with alerts to the user’s device (e.g., computer, smart device, smartphone, etc.); and as an audio and vibration alert (e.g., where the system 100 vibrates) along with alerts to the user’s device (e.g., computer, smart device, smart phone, etc.) and along with text messages and email messages.
  • an audio alert e.g., an audio and vibration alert
  • the system 100 vibrates along with alerts to the user’s device (e.g., computer, smart device, smart phone, etc.) and along with text messages and email messages.
  • a medical office e.g., including the physician’s office
  • the system 100 sends, responsive to a determined likelihood below the event prediction threshold, a request to the insertable device 102 to acquire additional physiological measures at a delay interval. For example, the system 100 can determine that the subject 106 is not currently at risk of a NCD event within a window of time based on the acquired and analyzed physiological measures.
  • the system 100 can set a delay interval of Attorney Docket No.07039-2212WO1 / 2021-489 time before the insertable device 102 collects another set of biosignals for that specific event prediction. Other predictive algorithms will still be operative at the same time.
  • the delay interval can be a minute, an hour, a day, a week, or longer depending on the risk factors of the subject 106.
  • the system 100 can turn off an algorithmic function, stop collecting the corresponding data, or otherwise conserve power throughout the system 100.
  • the delay interval and power conservation can facilitate a longer lasting insertable device 102 and power management unit, can reduce occurrences of false positives when the subject is below a risk threshold, can facilitate expedited data processing by streamlining the amount of data communicated from the insertable device 102 to the external device 104.
  • the streamlined data sets can improve the efficiency of the AI-based algorithm, and can improve the AI algorithm by providing tailored data at desired time intervals. By auto-regulating data collection and operations of specific algorithms in a continuous manner, the system can efficiently and actively predict adverse events utilizing actively functioning algorithms.
  • the proposed digital data output facilitates integration of the recorded signals for analysis by artificial intelligence (AI) algorithms for rhythm determination and also for AI-enabled NCD surveillance and prognostic solutions, for example in the examples #1-11 of FIG. 8.
  • AI artificial intelligence
  • NCDs cardiovascular diseases
  • CVDs cardiovascular diseases
  • RA rheumatoid arthritis
  • cerebrovascular disease osteopenia/osteoporosis
  • degenerative disc disease sarcopenia and frailty
  • depression cognitive impairment
  • neurodegenerative diseases among others.
  • the insertable device 102 can be paired with one of the noted configuration series of electrodes to maximize the resolution and fidelity of the cardiac signal recorded to maximize sensitivity, specificity and vector analysis of the NCD event(s).
  • the pairing of the surface sensors 120 with the insertable device can include a wired or wireless connection (e.g., near-field Attorney Docket No.07039-2212WO1 / 2021-489 communication, Bluetooth® wireless communication, WiFi, wired connection, or other connections).
  • the system 100 can be MRI compatible.
  • the system 100 can have precision location/tracking functionality.
  • the system 100 can facilitate pre-procedure surveillance.
  • pre-procedure surveillance can be associated with electrocardiographic imaging (ECGI), and can facilitate informed therapy decisions by the physician.
  • ECGI electrocardiographic imaging
  • Pre- procedure surveillance enables the physician to: complete surveillance anytime outside the EP lab, Panoramic surveillance – facilitates simultaneous surveillance of multiple chambers, Single beat surveillance – facilitates surveillance of infrequent and non- sustained arrhythmias.
  • the system 100 can implement the AI algorithm analysis described above to create 3D visualization to identify both changes in conduction, anatomy, and cardiac health over time through the use of ECG, ultrasound, wearable ultrasound or x-rays.
  • the system 100 facilitates the generation of a more complete pre- procedure surveillance.
  • the system 100 and associated algorithm will improve dramatically from the high false positives and negatives in previous solutions at least by facilitating: a) increase signal resolution; b) higher sampling rate; c) analog to analog continuous signal collection limiting data loss earns on itself as data accumulates over time; d) noise filtering and reduction; e) means to decipher between physiologic vs. non- physiologic signals; f) ability to interface with external sensors such as ECG patches, ultrasound, or wearable devices.
  • the system 100 can be implemented in a minimally invasive outpatient (clinical based) procedure.
  • the insertable device 102 is placed in the patient’s chest region just below the skin. Local anesthesia may be used, and the patient can be awake and able to communicate with the physician and nurses during the procedure.
  • the insertable device 102 is discreet and not visible in many patients. After the small incision has healed, the patient may also continue all their regular activities, including bathing, swimming, and exercise.
  • the electrode and sensor with 3D print out of oval ICM with electrodes/sensors surface placement positioning is completed to confirm position at which P and R-wave amplitude and impedance respiratory signal sensing are maximized, steady and reliable (e.g., this can be confirmed by displaying an electrode/sensor display on to a user that displays the placement of each of the electrodes and illustrates color-coded indications of signal verification for each electrode).
  • Attorney Docket No.07039-2212WO1 / 2021-489 Once pre-insertion placement positioning and skin surface preparation is completed the insertable device 102 (e.g., including the plurality of electrodes 130a-i) may be inserted in a percutaneous manner.
  • the insertable device 102 is positioned on the left anterior side of the chest.
  • the insertable device 102 can be placed in a position in which they are closest to the heart, atrial to ventricular signal is acquired and the system is exposed the least to body positional movements.
  • the incision site is closed.
  • the next step is the recording of the patient’s heart rhythm.
  • the physician programs the system 100 to a surveillance mode. AI analyses are continuously ran in the insertable device processors 128 of insertable device 102. In the event of an event meeting a critical threshold, the patient is notified directly via their smartphone and the physician is notified via a cloud-based system in order for appropriate clinical action to be implemented.
  • Identify (ID) insertable device 102 placement site a. Use as needed external electrodes and/or sensors and 3D print-out ICM with electrodes and/or sensors (e.g., via a cable connection) with cable to connect to viewer box provided within the ICM packaging to facilitate pre-insertion electrode and/or sensor placement positioning. 2. Prepare the insertion site. 3. Pinch the skin and make incision. 4. Insert system insertion tool and insertable device 102 (e.g., including the plurality of electrodes 130a-i). 5. Record signals. a.
  • signal validation recording can be completed to verify device positioning is adequate for electrocardiogram (ECG) signal collection.
  • ECG electrocardiogram
  • the electrodes displayed on a signal display screen are color coded (e.g. turn green) indicating (ECG) signal collection is ready for data collection.
  • FIGS.3 and 4A illustrate another example of an insertable device 302 that can be implemented in the system 100 in place of the insertable device 102.
  • the insertable device 302 can share features with the insertable device 102.
  • the insertable device 302 can include a sheath 303 that extends over a collapsed and expandable grid 304 of a plurality of electrodes and sensors 330.
  • the plurality of electrodes and sensors 330 within the sheath can expand outwardly when the sheath is removed (see e.g., FIG.4).
  • the grid 304 of electrodes can include 24 electrodes. Additional electrodes 330 can be positioned along the insertable device 302 and outside of the grid of electrodes.
  • the insertable device 302 can include 24 electrodes, some of which are part of the expandable grid and others are separate from the expandable grid.
  • the insertable device can include between 5 and 10 electrodes, between 10 and 20 electrodes, between 20 and 30 electrodes, or more electrodes and/or sensors.
  • FIGS. 4B, 4C, and 4D illustrate additional embodiments of the insertable device 302 that can be implemented in system 100 in place of the insertable device 102.
  • the insertable device 302 shown in FIGS.4B, 4C, and 4D can share features with each of the insertable devices 102 and 302 described herein.
  • FIGS. 5 and 6 illustrate another example of an insertable device 502 that can be implemented as the insertable devices 102, 302 described above.
  • the insertable device 502 can share features with the insertable devices 102, 302 described above.
  • the insertable device 502 can include arms that extend outwardly to position one or more Attorney Docket No.07039-2212WO1 / 2021-489 electrodes 530.
  • the arms can include one, two, or a plurality of electrodes and/or sensors530.
  • the insertable device 502 can include one, two, or a plurality of arms.
  • FIG. 7 shows an example of a computing device 700 and an example of a mobile computing device that can be used to implement the techniques described herein.
  • the computing device 700 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers.
  • the mobile computing device is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart-phones, and other similar computing devices.
  • the components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document.
  • the computing device 700 includes a processor 702, a memory 704, a storage device 706, a high-speed interface 708 connecting to the memory 704 and multiple high-speed expansion ports 710, and a low-speed interface 712 connecting to a low- speed expansion port 714 and the storage device 706.
  • Each of the processor 702, the memory 704, the storage device 706, the high-speed interface 708, the high-speed expansion ports 710, and the low-speed interface 712 are interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate.
  • the processor 702 can process instructions for execution within the computing device 700, including instructions stored in the memory 704 or on the storage device 706 to display graphical information for a GUI on an external input/output device, such as a display 716 coupled to the high-speed interface 708.
  • multiple processors and/or multiple buses can be used, as appropriate, along with multiple memories and types of memory.
  • the memory 704 stores information within the computing device 700.
  • the memory 704 is a volatile memory unit or units.
  • the memory 704 is a non-volatile memory unit or units.
  • the memory 704 can also be another form of computer-readable medium, such as a magnetic or optical disk. Attorney Docket No.07039-2212WO1 / 2021-489
  • the storage device 706 is capable of providing mass storage for the computing device 700.
  • the storage device 706 can be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations.
  • a computer program product can be tangibly embodied in an information carrier.
  • the computer program product can also contain instructions that, when executed, perform one or more methods, such as those described above.
  • the computer program product can also be tangibly embodied in a computer- or machine- readable medium, such as the memory 704, the storage device 706, or memory on the processor 702.
  • the high-speed interface 708 manages bandwidth-intensive operations for the computing device 700, while the low-speed interface 712 manages lower bandwidth- intensive operations. Such allocation of functions is exemplary only.
  • the high-speed interface 708 is coupled to the memory 704, the display 716 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 710, which can accept various expansion cards (not shown).
  • the low-speed interface 712 is coupled to the storage device 706 and the low-speed expansion port 714.
  • the low-speed expansion port 714 which can include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet) can be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
  • the computing device 700 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a standard server 720, or multiple times in a group of such servers. In addition, it can be implemented in a personal computer such as a laptop computer 722. It can also be implemented as part of a rack server system 724.
  • components from the computing device 700 can be combined with other components in a mobile device (not shown), such as a mobile computing device 750.
  • a mobile computing device 750 Each of such devices can contain one or more of the computing device 700 and the mobile computing device 750, and an entire system can be made up of multiple computing devices communicating with each other.
  • the mobile computing device 750 includes a processor 752, a memory 764, an input/output device such as a display 754, a communication interface 766, and a Attorney Docket No.07039-2212WO1 / 2021-489 transceiver 768, among other components.
  • the mobile computing device 750 can also be provided with a storage device, such as a micro-drive or other device, to provide additional storage.
  • Each of the processor 752, the memory 764, the display 754, the communication interface 766, and the transceiver 768, are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate.
  • the processor 752 can execute instructions within the mobile computing device 750, including instructions stored in the memory 764.
  • the processor 752 can be implemented as a chipset of chips that include separate and multiple analog and digital processors.
  • the processor 752 can provide, for example, for coordination of the other components of the mobile computing device 750, such as control of user interfaces, applications run by the mobile computing device 750, and wireless communication by the mobile computing device 750.
  • the processor 752 can communicate with a user through a control interface 758 and a display interface 756 coupled to the display 754.
  • the display 754 can be, for example, a TFT (Thin-Film-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology.
  • the display interface 756 can comprise appropriate circuitry for driving the display 754 to present graphical and other information to a user.
  • the control interface 758 can receive commands from a user and convert them for submission to the processor 752.
  • an external interface 762 can provide communication with the processor 752, so as to enable near area communication of the mobile computing device 750 with other devices.
  • the external interface 762 can provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces can also be used.
  • the memory 764 stores information within the mobile computing device 750.
  • the memory 764 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units.
  • An expansion memory 774 can also be provided and connected to the mobile computing device 750 through an expansion interface 772, which can include, for example, a SIMM (Single In Line Memory Module) card interface.
  • SIMM Single In Line Memory Module
  • the expansion memory 774 can provide extra storage space for the mobile computing device 750, or can also store applications or other information for the mobile computing device 750.
  • the expansion memory 774 can include instructions to carry out or supplement the Attorney Docket No.07039-2212WO1 / 2021-489 processes described above, and can include secure information also.
  • the expansion memory 774 can be provided as a security module for the mobile computing device 750, and can be programmed with instructions that permit secure use of the mobile computing device 750.
  • secure applications can be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.
  • the memory can include, for example, flash memory and/or NVRAM memory (non-volatile random access memory), as discussed below.
  • NVRAM memory non-volatile random access memory
  • a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above.
  • the computer program product can be a computer- or machine-readable medium, such as the memory 764, the expansion memory 774, or memory on the processor 752.
  • the computer program product can be received in a propagated signal, for example, over the transceiver 768 or the external interface 762.
  • the mobile computing device 750 can communicate wirelessly through the communication interface 766, which can include digital signal processing circuitry where necessary.
  • the communication interface 766 can provide for communications under various modes or protocols, such as GSM voice calls (Global System for Mobile communications), SMS (Short Message Service), EMS (Enhanced Messaging Service), or MMS messaging (Multimedia Messaging Service), CDMA (code division multiple access), TDMA (time division multiple access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple ACCess), CDMA2000, or GPRS (General Packet Radio Service), among others.
  • GSM voice calls Global System for Mobile communications
  • SMS Short Message Service
  • EMS Enhanced Messaging Service
  • MMS messaging Multimedia Messaging Service
  • CDMA code division multiple access
  • TDMA time division multiple access
  • PDC Personal Digital Cellular
  • WCDMA Wideband Code Division Multiple ACCess
  • CDMA2000 Code Division Multiple ACCess
  • GPRS General Packet Radio Service
  • a GPS (Global Positioning System) receiver module 770 can provide additional navigation- and location-related wireless data to the mobile computing device 750, which can be used as appropriate by applications running on the mobile computing device 750.
  • the mobile computing device 750 can also communicate audibly using an audio codec 760, which can receive spoken information from a user and convert it to usable digital information.
  • the audio codec 760 can likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile computing device 750.
  • Such sound can include sound from voice telephone calls, can include recorded sound (e.g., voice messages, music files, etc.) and can also include sound generated by applications operating on the mobile computing device 750.
  • the mobile computing device 750 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a cellular telephone 780. It can also be implemented as part of a smart-phone 782, personal digital assistant, or other similar mobile device.
  • Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof.
  • These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • These computer programs also known as programs, software, software applications or code
  • machine- readable medium and computer-readable medium refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal.
  • machine-readable signal refers to any signal used to provide machine instructions and/or data to a programmable processor.
  • the systems and techniques described here can be implemented on a computer having a display device (e.g., a LCD (liquid crystal display) display screen for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer.
  • a display device e.g., a LCD (liquid crystal display) display screen for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer.
  • Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory Attorney Docket No.07039-2212WO1 / 2021-489 feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
  • the systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components.
  • the components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.
  • the computing system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network.
  • the relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. While this specification contains many specific implementation details, these should not be construed as limitations on the scope of the disclosed technology or of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of particular disclosed technologies. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment in part or in whole. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
  • receiving from a first device may include receiving the data from a network, but may not include the first device transmitting the data.
  • Determining by a computing system can include the computing system requesting that another device perform the determination and supply the results to the computing system.
  • displaying or “presenting” by a computing system can include the computing system sending data for causing another device to display or present the referenced information.
  • the systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components.
  • the components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network).
  • Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), peer-to-peer networks (having ad-hoc or static members), grid computing infrastructures, and the Internet.
  • the computing system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
  • a user may be provided with controls allowing the user to make an election as to both if and when systems, programs or features Attorney Docket No.07039-2212WO1 / 2021-489 described herein may enable collection of user information (e.g., information about a user's social network, social actions or activities, profession, a user's preferences, or a user's current location), and if the user is sent content or communications from a server.
  • user information e.g., information about a user's social network, social actions or activities, profession, a user's preferences, or a user's current location
  • certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed.
  • a user's identity may be treated so that no personally identifiable information can be determined for the user, or a user's geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined.
  • location information such as to a city, ZIP code, or state level
  • the user may have control over what information is collected about the user, how that information is used, and what information is provided to the user.
  • Embodiments of the subject matter and the functional operations described in this specification can be implemented at least in part in digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them.
  • Embodiments of the subject matter described in this specification can be implemented at least in part as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non- transitory storage medium for execution by, or to control the operation of, data processing apparatus.
  • the computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.
  • the program instructions can be encoded on an artificially-generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus.
  • data processing apparatus refers to data processing hardware and encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers.
  • Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or both, or any other kind of central processing unit.
  • a central processing unit will receive instructions and data from a read-only memory or a random access memory or both.
  • Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices.

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Abstract

L'invention concerne des systèmes, dispositifs, procédés et techniques pour la surveillance prédictive en temps réel, l'évaluation clinique, le diagnostic et l'analyse prédictive. L'invention concerne par exemple des systèmes pouvant être insérés qui facilitent une surveillance prédictive en temps réel, une évaluation clinique, un diagnostic et une analyse prédictive d'événements de maladie qui ne peuvent pas être communiqués.
PCT/US2024/054070 2023-11-02 2024-11-01 Système de surveillance prédictive et de diagnostic en temps réel Pending WO2025096904A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2704795B1 (fr) * 2011-05-03 2015-01-14 Medtronic, Inc. Vérification de paramètres de pression
US20230277856A1 (en) * 2020-06-16 2023-09-07 National University Of Ireland, Galway An intraluminal contraction augmentation system
US20230346288A1 (en) * 2022-04-28 2023-11-02 The Board Of Trustees Of The Leland Stanford Junior University Systems and Methods for Evaluating Cardiovascular Disease Risks

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2704795B1 (fr) * 2011-05-03 2015-01-14 Medtronic, Inc. Vérification de paramètres de pression
US20230277856A1 (en) * 2020-06-16 2023-09-07 National University Of Ireland, Galway An intraluminal contraction augmentation system
US20230346288A1 (en) * 2022-04-28 2023-11-02 The Board Of Trustees Of The Leland Stanford Junior University Systems and Methods for Evaluating Cardiovascular Disease Risks

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