Method or System Using Machine Learning and/or Artificial Intelligence (AI) to … — Medibotics Llc (US20240065550A1)

ABSTRACT

Abstract

This invention is a method or system which uses machine learning and/or artificial intelligence (AI) to adjust, manage, and/or control the operation of one or more implanted medical devices. This method or system identifies the lagged effects of operating parameters of the one or more implanted medical devices on a person's biometric indicators and then adjusts the operating parameters of the one or more implanted medical devices to change the values of the biometric indicators in a desired direction and/or by a desired amount.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of U.S. patent application Ser. No. 17/410,297 filed on 2021 Aug. 24. U.S. patent application Ser. No. 17/410,297 was a continuation in part of U.S. patent application Ser. No. 16/598,514 filed on 2019 Oct. 10. U.S. patent application Ser. No. 17/410,297 was also a continuation in part of U.S. patent application Ser. No. 16/568,580 filed on 2019 Sep. 12. U.S. patent application Ser. No. 17/410,297 was also a continuation in part of U.S. patent application Ser. No. 16/150,469 filed on 2018 Oct. 3.

U.S. patent application Ser. No. 16/598,514 claimed the priority benefit of U.S. provisional patent application 62/857,942 filed on 2019 Jun. 6. U.S. patent application Ser. No. 16/568,580 claimed the priority benefit of U.S. provisional patent application 62/857,942 filed on 2019 Jun. 6. U.S. patent application Ser. No. 16/150,469 was a continuation in part of U.S. patent application Ser. No. 15/418,620 filed on 2017 Jan. 27. U.S. patent application Ser. No. 16/150,469 was a continuation in part of U.S. patent application Ser. No. 14/459,937 filed on 2014 Aug. 14.

U.S. patent application Ser. No. 15/418,620 claimed the priority benefit of U.S. provisional patent application 62/439,147 filed on 2016 Dec. 26. U.S. patent application Ser. No. 15/418,620 claimed the priority benefit of U.S. provisional patent application 62/297,827 filed on 2016 Feb. 20. U.S. patent application Ser. No. 15/418,620 was a continuation in part of U.S. patent application Ser. No. 14/951,475 filed on 2015 Nov. 24 which issued as U.S. patent Ser. No. 10/314,492.

U.S. patent application Ser. No. 14/951,475 claimed the priority benefit of U.S. provisional patent application 62/245,311 filed on 2015 Oct. 23. U.S. patent application Ser. No. 14/951,475 was a continuation in part of U.S. patent application Ser. No. 14/623,337 filed on 2015 Feb. 16 which issued as U.S. Pat. No. 9,582,035. U.S. patent application Ser. No. 14/951,475 was a continuation in part of U.S. patent application Ser. No. 14/071,112 filed on 2013 Nov. 4. U.S. patent application Ser. No. 14/951,475 was a continuation in part of U.S. patent application Ser. No. 13/901,131 filed on 2013 May 23 which issued as U.S. Pat. No. 9,536,449. U.S. patent application Ser. No. 14/459,937 claimed the priority benefit of U.S. provisional patent application 61/866,583 filed on 2013 Aug. 16.

The entire contents of these related applications are incorporated herein by reference.

FEDERALLY SPONSORED RESEARCH

Not Applicable

SEQUENCE LISTING OR PROGRAM

Not Applicable

BACKGROUND

Field of Invention

This invention relates to methods and systems for human circulatory assistance.

INTRODUCTION

Proper cardiovascular functioning, including good blood circulation and tissue oxygenation, is important for human physiological functioning and tissue health. When cardiovascular functioning is significantly impaired, a cardiac device such as a pacemaker (to assist in cardiac pacing) or a ventricular assist device (to assist in blood pumping) can be implanted to improve cardiovascular functioning. A person's cardiovascular needs vary over time based on the person's activity level, as well as physiological and anatomical changes over time. Accordingly, it is important that the operation of an implanted cardiac device be automatically adjusted to respond to these changing needs.

However, many cardiovascular problems first appear in the extremities of a person's body, such as the person's feet and hands. Biometric information from those extremities is needed for accurate adjustment and optimization of the operation of a cardiac device, but this information is not available from sensors in a cardiac device which is implanted in a person's torso. This is the unmet clinical need which is addressed by this invention. A method or system for assisting human cardiovascular functioning which adjusts and optimizes the operation of an implanted cardiac device based on biometric information from body extremities can help to manage chronic heart conditions, improve tissue health (particularly in extremities such as feet and hands), promote wound healing, and potentially even avoid amputation.

REVIEW OF THE RELEVANT ART

U.S. patent application 20040131998 (Marom et al., Jul. 8, 2004, “Cerebral Programming”) and U.S. Pat. No. 7,499,894 (Marom et al., Mar. 3, 2009, “Cerebral Programming”) disclose training a biological neural network to control a pacemaker. U.S. patent applications 20050081847 (Lee et al., Apr. 21, 2005, Automatic Activation of Medical Processes”) and 20100106211 (Lee et al., Apr. 29, 2010, “Automatic Activation of Medical Processes”), as well as U.S. Pat. No. 7,668,591 (Lee et al., Feb. 23, 2010, “Automatic Activation of Medical Processes”) and U.S. Pat. No. 8,380,296 (Lee et al., Feb. 19, 2013, “Automatic Activation of Medical Processes”) disclose automatic modification of therapies or other medical processes based on brain state.

U.S. patent applications 20050115561 (Stahmann et al., Jun. 2, 2005, “Patient Monitoring, Diagnosis, and/or Therapy Systems and Methods”) and 20110061647 (Stahmann et al., Mar. 17, 2011, “Patient Monitoring, Diagnosis, and/or Therapy Systems and Methods”), as well as U.S. Pat. No. 7,787,946 (Stahmann et al., Aug. 31, 2010, “Patient Monitoring, Diagnosis, and/or Therapy Systems and Methods”), disclose systems and methods involving an implantable device configured to perform at least one cardiac-related function, a patient-external respiratory therapy device, and a communication channel configured to facilitate communication between the implantable device and the respiratory therapy device. U.S. patent application 20060195039 (Drew et al, Aug. 31, 2006, “Clustering with Combined Physiological Signals”) and U.S. Pat. No. 8,768,446 (Drew et al., Jul. 1, 2014, “Clustering with Combined Physiological Signals”) disclose a method of generating an extended cluster by an implanted medical device.

U.S. patent applications 20070260286 (Giftakis et al., Nov. 8, 2007, “System and Method for Utilizing Brain State Information to Modulate Cardiac Therapy”) and U.S. patent application 20070265677 (Giftakis et al., Nov. 15, 2007, “System and Method for Utilizing Brain State Information to Modulate Cardiac Therapy”) and U.S. Pat. No. 8,209,019 (Giftakis et al., Jun. 26, 2012, “System and Method for Utilizing Brain State Information to Modulate Cardiac Therapy”) and U.S. Pat. No. 8,214,035 (Giftakis et al., Jul. 3, 2012, “System and Method for Utilizing Brain State Information to Modulate Cardiac Therapy”) disclose a system for regulating or modulating cardiac therapy using brain state information.

U.S. Pat. No. 7,313,440 (Miesel, Dec. 25, 2007, “Collecting Posture and Activity Information to Evaluate Therapy”) discloses using posture to set therapy parameters for a medical device. U.S. Pat. No. 8,090,432 (Cinbis et al., Jan. 3, 2012, “Implantable Tissue Perfusion Sensing System and Method”) and U.S. Pat. No. 8,165,662 (Cinbis et al., Apr. 24, 2012, “Implantable Tissue Perfusion Sensing System and Method”) disclose using electrodes to sense cardiac signals to identify a cardiac event.

U.S. Pat. No. 8,108,038 (Giftakis et al., Jan. 31, 2012, “System and Method for Segmenting a Cardiac Signal Based on Brain Activity”) and U.S. Pat. No. 8,209,009 (Giftakis et al., Jun. 26, 2012, “System and Method for Segmenting a Cardiac Signal Based on Brain Stimulation”) disclose a medical device system with a brain monitoring element, cardiac monitoring element and a processor. U.S. Pat. No. 8,112,148 (Giftakis et al., Feb. 7, 2012, “System and Method for Monitoring Cardiac Signal Activity in Patients with Nervous System Disorders”) discloses a medical device system and method for monitoring cardiac signal activity in patients with nervous system disorders.

U.S. Pat. No. 8,428,729 (Schwartz et al., Apr. 23, 2013, “Cardiac Stimulation Apparatus and Method for the Control of Hypertension”) discloses a method that electrically stimulates a heart muscle to alter the ejection profile of the heart, to control the mechanical function of the heart and reduce the observed blood pressure of the patient. U.S. Pat. No. 8,463,345 (Kuhn et al., Jun. 11, 2013, “Device and Method for Monitoring of Absolute Oxygen Saturation and Total Hemoglobin Concentration”), U.S. Pat. No. 8,634,890 (Kuhn et al., Jan. 21, 2014, “Device and Method for Monitoring of Absolute Oxygen Saturation and Tissue Hemoglobin Concentration”) and U.S. Pat. No. 8,666,466 (Kuhn et al., Mar. 4, 2014, “Device and Method for Monitoring of Absolute Oxygen Saturation and Tissue Hemoglobin Concentration”) disclose an implantable oxygen saturation monitor.

U.S. Pat. No. 8,515,548 (Rofougaran et al., Aug. 20, 2013, “Article of Clothing Including Bio-Medical Units”) discloses an article of clothing with a plurality of bio-medical units integrated into the clothing fabric. U.S. Pat. No. 8,617,082 (Zhang et al., Dec. 13, 2013, “Heart Sounds-Based Pacing Optimization”) discloses an implantable medical device that receives both heart sounds and electrogram signals. U.S. Pat. No. 8,634,890 (Kuhn et al., Jan. 21, 2014, “Device and Method for Monitoring of Absolute Oxygen Saturation and Tissue Hemoglobin Concentration”) discloses a method and device for measuring tissue oxygen level.

U.S. patent application 20150246166 (Greatrex et al., Sep. 3, 2015, “Ventricular Assist Device and Method of Controlling Same”) discloses a method of controlling the speed of a ventricular assist device, in particular the rotational speed of a rotary blood pump, wherein at least temporarily the speed of the device is modulated around a mean speed and a response of the native heart to this modulation is measured. U.S. Pat. No. 9,131,865 (Thompson-Nauman et al., Sep. 15, 2015, “Method and Apparatus for Cardiac Function Monitoring”) and U.S. Pat. No. 9,138,157 (Thompson-Nauman et al., Sep. 22, 2015, “Method and Apparatus for Cardiac Function Monitoring”) disclose a method and medical device for monitoring cardiac function in a patient that includes a plurality of electrodes to deliver cardiac pacing therapy.

U.S. Pat. No. 9,283,341 (Ujhazy et al., Mar. 15, 2016, “Methods and Apparatus for Heart Failure Treatment”) discloses methods and an apparatus for assessing the condition of and treating patients for heart failure by the delivery of continuous positive airway pressure. U.S. Pat. No. 9,307,907 (Condurso et al., Apr. 12, 2016, “System and Method for Dynamically Adjusting Patient Therapy”) discloses a system and method of managing therapy that monitors all aspects of the medication delivery to a patient. U.S. Pat. No. 9,320,443 (Libbus et al., Apr. 26, 2016, “Multi-Sensor Patient Monitor to Detect Impending Cardiac Decompensation”) discloses a system for detecting impending acute cardiac decompensation.

U.S. patent application 20160143533 (Keenan et al., May 26, 2016, “Medical Device System Having an Implanted Medical Device and an External Device”) discloses an external device for wireless monitoring of an implanted device. U.S. patent application 20160228627 (Wiesener et al., Aug. 11, 2016, “Blood Pump Control System and Method for Controlling a Blood Pump”) discloses methods for controlling the speed of a pump based on a valve state index and/or for deriving a valve state from time-series signal representing a pressure difference or a flow rate. U.S. patent application 20180126053 (Zilbershlag, May 10, 2018, “Wristwatch for Monitoring Operation of an Implanted Ventricular Assist Device”) discloses a wristwatch wirelessly connected to an implanted medical device such as a VAD to monitors the operation of the VAD.

U.S. patent Ser. No. 10/010,662 (Wiesener et al., Jul. 3, 2018, “Blood Pump Control System and Method for Controlling a Blood Pump”) discloses methods for controlling the speed of a blood pump based on a valve state index. U.S. patent Ser. No. 10/258,798 (Panken et al., Apr. 6, 2019, “Patient Directed Therapy Control”) discloses patient control of therapy by changing their mental activity. U.S. patent application 20190298987 (Freeman et al., Oct. 3, 2019, “Garments for Wearable Cardiac Monitoring and Treatment Devices”) discloses a garment with ECG monitors.

U.S. patent Ser. No. 10/485,978 (Thakur et al., Nov. 26, 2019, “Pacing Site and Configuration Optimization Using a Combination of Electrical and Mechanical Information”) discloses using heart sounds to evaluate alternative cardiac pacing sites. U.S. patent application 20200179706 (Thakur et al., Jun. 11, 2020, “Hemodynamically Optimized Rate Response Pacing Using Heart Sounds”) disclose san apparatus comprising a stimulus circuit configured to deliver electrical pacing therapy to a subject when operatively coupled to a plurality of electrodes, including a heart sound sensing circuit.

U.S. patent application 20200215246 (Tal et al., Jul. 9, 2020, “Method and System for Ventricular Assistive Device Adjustment Using a Wearable Device”) discloses control of a VAD device using wearable sensors, such as blood pressure sensors. U.S. patent application 20210000349 (Goetz, Jan. 7, 2021, “Remote Titration of Therapy Delivered by an Implantable Medical Device”) discloses techniques for remotely titrating a therapy delivered using an implantable medical device system. U.S. patent application 20210177335 (Varadan et al., Jun. 17, 2021, “Wearable Congestive Heart Failure Management System”) discloses a non-invasive, wearable and portable medical device for evaluation and monitoring the heart condition for patients with congestive heart failure.

U.S. patent application 20210228886 (Pronovici et al., Jul. 29, 2021, “Adjustment of Mechanical Motion Sensing for Controlling Cardiac Pacing”) discloses sensing the mechanical motion of a heart for cardiac pacing therapy. U.S. patent application 20210236801 (Sarkisyan et al., Aug. 5, 2021, “Integrated Adjustable Multi-Pump Mechanical Circulatory Support Device”) discloses a mechanical circulatory support device with first and second blood pumps. U.S. patent application 20210252277 (Webster et al., Aug. 19, 2021, “Wearable Medical Device With Integrated Blood or tissue oxygen Saturation Level Device”) discloses a wearable medical device (WMD) with a blood or tissue oxygen level sensor.

U.S. patent application 20210298658 (Ghosh, Sep. 30, 2021, “Pacing Efficacy Determination Using a Representative Morphology of External Cardiac Signals”) discloses systems and methods for detecting the efficiency of cardiac pacing using external cardiac signals. U.S. patent application 20210315521 (Webster et al., Oct. 14, 2021, “Modular Cardiac Patient Treatment and Monitoring”) discloses a wearable healthcare system having a reconfigurable medical device. U.S. patent application 20220040487 (Burnam et al., Feb. 10, 2022, “An Intelligently, Continuously, and Physiologically Controlled Pacemaker and Method of Operation of the Same”) discloses a pacemaker control system with internal and external sensors.

U.S. patent application 20220072316 (Yoon et al., Mar. 10, 2022, “Dual Sensors to Control Pacing Rate”) discloses a medical device which uses acceleration and temperature to adjust the rate of cardiac pacing. U.S. patent application 20220088369 (Chopra et al., Mar. 24, 2022, “Systems and Methods for Pump-Assisted Blood Circulation”) discloses systems, devices, and methods that use a pump to assist blood flow.

U.S. patent application 20220152397 (Kelley et al., May 19, 2022, “Neurostimulation Evaluation, Progra

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of U.S. patent application Ser. No. 17/410,297 filed on 2021 Aug. 24. U.S. patent application Ser. No. 17/410,297 was a continuation in part of U.S. patent application Ser. No. 16/598,514 filed on 2019 Oct. 10. U.S. patent application Ser. No. 17/410,297 was also a continuation in part of U.S. patent application Ser. No. 16/568,580 filed on 2019 Sep. 12. U.S. patent application Ser. No. 17/410,297 was also a continuation in part of U.S. patent application Ser. No. 16/150,469 filed on 2018 Oct. 3.

U.S. patent application Ser. No. 16/598,514 claimed the priority benefit of U.S. provisional patent application 62/857,942 filed on 2019 Jun. 6. U.S. patent application Ser. No. 16/568,580 claimed the priority benefit of U.S. provisional patent application 62/857,942 filed on 2019 Jun. 6. U.S. patent application Ser. No. 16/150,469 was a continuation in part of U.S. patent application Ser. No. 15/418,620 filed on 2017 Jan. 27. U.S. patent application Ser. No. 16/150,469 was a continuation in part of U.S. patent application Ser. No. 14/459,937 filed on 2014 Aug. 14.

U.S. patent application Ser. No. 15/418,620 claimed the priority benefit of U.S. provisional patent application 62/439,147 filed on 2016 Dec. 26. U.S. patent application Ser. No. 15/418,620 claimed the priority benefit of U.S. provisional patent application 62/297,827 filed on 2016 Feb. 20. U.S. patent application Ser. No. 15/418,620 was a continuation in part of U.S. patent application Ser. No. 14/951,475 filed on 2015 Nov. 24 which issued as U.S. patent Ser. No. 10/314,492.

U.S. patent application Ser. No. 14/951,475 claimed the priority benefit of U.S. provisional patent application 62/245,311 filed on 2015 Oct. 23. U.S. patent application Ser. No. 14/951,475 was a continuation in part of U.S. patent application Ser. No. 14/623,337 filed on 2015 Feb. 16 which issued as U.S. Pat. No. 9,582,035. U.S. patent application Ser. No. 14/951,475 was a continuation in part of U.S. patent application Ser. No. 14/071,112 filed on 2013 Nov. 4. U.S. patent application Ser. No. 14/951,475 was a continuation in part of U.S. patent application Ser. No. 13/901,131 filed on 2013 May 23 which issued as U.S. Pat. No. 9,536,449. U.S. patent application Ser. No. 14/459,937 claimed the priority benefit of U.S. provisional patent application 61/866,583 filed on 2013 Aug. 16.

The entire contents of these related applications are incorporated herein by reference.

FEDERALLY SPONSORED RESEARCH

Not Applicable

SEQUENCE LISTING OR PROGRAM

Not Applicable

BACKGROUND

Field of Invention

This invention relates to methods and systems for human circulatory assistance.

INTRODUCTION

Proper cardiovascular functioning, including good blood circulation and tissue oxygenation, is important for human physiological functioning and tissue health. When cardiovascular functioning is significantly impaired, a cardiac device such as a pacemaker (to assist in cardiac pacing) or a ventricular assist device (to assist in blood pumping) can be implanted to improve cardiovascular functioning. A person's cardiovascular needs vary over time based on the person's activity level, as well as physiological and anatomical changes over time. Accordingly, it is important that the operation of an implanted cardiac device be automatically adjusted to respond to these changing needs.

However, many cardiovascular problems first appear in the extremities of a person's body, such as the person's feet and hands. Biometric information from those extremities is needed for accurate adjustment and optimization of the operation of a cardiac device, but this information is not available from sensors in a cardiac device which is implanted in a person's torso. This is the unmet clinical need which is addressed by this invention. A method or system for assisting human cardiovascular functioning which adjusts and optimizes the operation of an implanted cardiac device based on biometric information from body extremities can help to manage chronic heart conditions, improve tissue health (particularly in extremities such as feet and hands), promote wound healing, and potentially even avoid amputation.

REVIEW OF THE RELEVANT ART

U.S. patent application 20040131998 (Marom et al., Jul. 8, 2004, “Cerebral Programming”) and U.S. Pat. No. 7,499,894 (Marom et al., Mar. 3, 2009, “Cerebral Programming”) disclose training a biological neural network to control a pacemaker. U.S. patent applications 20050081847 (Lee et al., Apr. 21, 2005, Automatic Activation of Medical Processes”) and 20100106211 (Lee et al., Apr. 29, 2010, “Automatic Activation of Medical Processes”), as well as U.S. Pat. No. 7,668,591 (Lee et al., Feb. 23, 2010, “Automatic Activation of Medical Processes”) and U.S. Pat. No. 8,380,296 (Lee et al., Feb. 19, 2013, “Automatic Activation of Medical Processes”) disclose automatic modification of therapies or other medical processes based on brain state.

U.S. patent applications 20050115561 (Stahmann et al., Jun. 2, 2005, “Patient Monitoring, Diagnosis, and/or Therapy Systems and Methods”) and 20110061647 (Stahmann et al., Mar. 17, 2011, “Patient Monitoring, Diagnosis, and/or Therapy Systems and Methods”), as well as U.S. Pat. No. 7,787,946 (Stahmann et al., Aug. 31, 2010, “Patient Monitoring, Diagnosis, and/or Therapy Systems and Methods”), disclose systems and methods involving an implantable device configured to perform at least one cardiac-related function, a patient-external respiratory therapy device, and a communication channel configured to facilitate communication between the implantable device and the respiratory therapy device. U.S. patent application 20060195039 (Drew et al, Aug. 31, 2006, “Clustering with Combined Physiological Signals”) and U.S. Pat. No. 8,768,446 (Drew et al., Jul. 1, 2014, “Clustering with Combined Physiological Signals”) disclose a method of generating an extended cluster by an implanted medical device.

U.S. patent applications 20070260286 (Giftakis et al., Nov. 8, 2007, “System and Method for Utilizing Brain State Information to Modulate Cardiac Therapy”) and U.S. patent application 20070265677 (Giftakis et al., Nov. 15, 2007, “System and Method for Utilizing Brain State Information to Modulate Cardiac Therapy”) and U.S. Pat. No. 8,209,019 (Giftakis et al., Jun. 26, 2012, “System and Method for Utilizing Brain State Information to Modulate Cardiac Therapy”) and U.S. Pat. No. 8,214,035 (Giftakis et al., Jul. 3, 2012, “System and Method for Utilizing Brain State Information to Modulate Cardiac Therapy”) disclose a system for regulating or modulating cardiac therapy using brain state information.

U.S. Pat. No. 7,313,440 (Miesel, Dec. 25, 2007, “Collecting Posture and Activity Information to Evaluate Therapy”) discloses using posture to set therapy parameters for a medical device. U.S. Pat. No. 8,090,432 (Cinbis et al., Jan. 3, 2012, “Implantable Tissue Perfusion Sensing System and Method”) and U.S. Pat. No. 8,165,662 (Cinbis et al., Apr. 24, 2012, “Implantable Tissue Perfusion Sensing System and Method”) disclose using electrodes to sense cardiac signals to identify a cardiac event.

U.S. Pat. No. 8,108,038 (Giftakis et al., Jan. 31, 2012, “System and Method for Segmenting a Cardiac Signal Based on Brain Activity”) and U.S. Pat. No. 8,209,009 (Giftakis et al., Jun. 26, 2012, “System and Method for Segmenting a Cardiac Signal Based on Brain Stimulation”) disclose a medical device system with a brain monitoring element, cardiac monitoring element and a processor. U.S. Pat. No. 8,112,148 (Giftakis et al., Feb. 7, 2012, “System and Method for Monitoring Cardiac Signal Activity in Patients with Nervous System Disorders”) discloses a medical device system and method for monitoring cardiac signal activity in patients with nervous system disorders.

U.S. Pat. No. 8,428,729 (Schwartz et al., Apr. 23, 2013, “Cardiac Stimulation Apparatus and Method for the Control of Hypertension”) discloses a method that electrically stimulates a heart muscle to alter the ejection profile of the heart, to control the mechanical function of the heart and reduce the observed blood pressure of the patient. U.S. Pat. No. 8,463,345 (Kuhn et al., Jun. 11, 2013, “Device and Method for Monitoring of Absolute Oxygen Saturation and Total Hemoglobin Concentration”), U.S. Pat. No. 8,634,890 (Kuhn et al., Jan. 21, 2014, “Device and Method for Monitoring of Absolute Oxygen Saturation and Tissue Hemoglobin Concentration”) and U.S. Pat. No. 8,666,466 (Kuhn et al., Mar. 4, 2014, “Device and Method for Monitoring of Absolute Oxygen Saturation and Tissue Hemoglobin Concentration”) disclose an implantable oxygen saturation monitor.

U.S. Pat. No. 8,515,548 (Rofougaran et al., Aug. 20, 2013, “Article of Clothing Including Bio-Medical Units”) discloses an article of clothing with a plurality of bio-medical units integrated into the clothing fabric. U.S. Pat. No. 8,617,082 (Zhang et al., Dec. 13, 2013, “Heart Sounds-Based Pacing Optimization”) discloses an implantable medical device that receives both heart sounds and electrogram signals. U.S. Pat. No. 8,634,890 (Kuhn et al., Jan. 21, 2014, “Device and Method for Monitoring of Absolute Oxygen Saturation and Tissue Hemoglobin Concentration”) discloses a method and device for measuring tissue oxygen level.

U.S. patent application 20150246166 (Greatrex et al., Sep. 3, 2015, “Ventricular Assist Device and Method of Controlling Same”) discloses a method of controlling the speed of a ventricular assist device, in particular the rotational speed of a rotary blood pump, wherein at least temporarily the speed of the device is modulated around a mean speed and a response of the native heart to this modulation is measured. U.S. Pat. No. 9,131,865 (Thompson-Nauman et al., Sep. 15, 2015, “Method and Apparatus for Cardiac Function Monitoring”) and U.S. Pat. No. 9,138,157 (Thompson-Nauman et al., Sep. 22, 2015, “Method and Apparatus for Cardiac Function Monitoring”) disclose a method and medical device for monitoring cardiac function in a patient that includes a plurality of electrodes to deliver cardiac pacing therapy.

U.S. Pat. No. 9,283,341 (Ujhazy et al., Mar. 15, 2016, “Methods and Apparatus for Heart Failure Treatment”) discloses methods and an apparatus for assessing the condition of and treating patients for heart failure by the delivery of continuous positive airway pressure. U.S. Pat. No. 9,307,907 (Condurso et al., Apr. 12, 2016, “System and Method for Dynamically Adjusting Patient Therapy”) discloses a system and method of managing therapy that monitors all aspects of the medication delivery to a patient. U.S. Pat. No. 9,320,443 (Libbus et al., Apr. 26, 2016, “Multi-Sensor Patient Monitor to Detect Impending Cardiac Decompensation”) discloses a system for detecting impending acute cardiac decompensation.

U.S. patent application 20160143533 (Keenan et al., May 26, 2016, “Medical Device System Having an Implanted Medical Device and an External Device”) discloses an external device for wireless monitoring of an implanted device. U.S. patent application 20160228627 (Wiesener et al., Aug. 11, 2016, “Blood Pump Control System and Method for Controlling a Blood Pump”) discloses methods for controlling the speed of a pump based on a valve state index and/or for deriving a valve state from time-series signal representing a pressure difference or a flow rate. U.S. patent application 20180126053 (Zilbershlag, May 10, 2018, “Wristwatch for Monitoring Operation of an Implanted Ventricular Assist Device”) discloses a wristwatch wirelessly connected to an implanted medical device such as a VAD to monitors the operation of the VAD.

U.S. patent Ser. No. 10/010,662 (Wiesener et al., Jul. 3, 2018, “Blood Pump Control System and Method for Controlling a Blood Pump”) discloses methods for controlling the speed of a blood pump based on a valve state index. U.S. patent Ser. No. 10/258,798 (Panken et al., Apr. 6, 2019, “Patient Directed Therapy Control”) discloses patient control of therapy by changing their mental activity. U.S. patent application 20190298987 (Freeman et al., Oct. 3, 2019, “Garments for Wearable Cardiac Monitoring and Treatment Devices”) discloses a garment with ECG monitors.

U.S. patent Ser. No. 10/485,978 (Thakur et al., Nov. 26, 2019, “Pacing Site and Configuration Optimization Using a Combination of Electrical and Mechanical Information”) discloses using heart sounds to evaluate alternative cardiac pacing sites. U.S. patent application 20200179706 (Thakur et al., Jun. 11, 2020, “Hemodynamically Optimized Rate Response Pacing Using Heart Sounds”) disclose san apparatus comprising a stimulus circuit configured to deliver electrical pacing therapy to a subject when operatively coupled to a plurality of electrodes, including a heart sound sensing circuit.

U.S. patent application 20200215246 (Tal et al., Jul. 9, 2020, “Method and System for Ventricular Assistive Device Adjustment Using a Wearable Device”) discloses control of a VAD device using wearable sensors, such as blood pressure sensors. U.S. patent application 20210000349 (Goetz, Jan. 7, 2021, “Remote Titration of Therapy Delivered by an Implantable Medical Device”) discloses techniques for remotely titrating a therapy delivered using an implantable medical device system. U.S. patent application 20210177335 (Varadan et al., Jun. 17, 2021, “Wearable Congestive Heart Failure Management System”) discloses a non-invasive, wearable and portable medical device for evaluation and monitoring the heart condition for patients with congestive heart failure.

U.S. patent application 20210228886 (Pronovici et al., Jul. 29, 2021, “Adjustment of Mechanical Motion Sensing for Controlling Cardiac Pacing”) discloses sensing the mechanical motion of a heart for cardiac pacing therapy. U.S. patent application 20210236801 (Sarkisyan et al., Aug. 5, 2021, “Integrated Adjustable Multi-Pump Mechanical Circulatory Support Device”) discloses a mechanical circulatory support device with first and second blood pumps. U.S. patent application 20210252277 (Webster et al., Aug. 19, 2021, “Wearable Medical Device With Integrated Blood or tissue oxygen Saturation Level Device”) discloses a wearable medical device (WMD) with a blood or tissue oxygen level sensor.

U.S. patent application 20210298658 (Ghosh, Sep. 30, 2021, “Pacing Efficacy Determination Using a Representative Morphology of External Cardiac Signals”) discloses systems and methods for detecting the efficiency of cardiac pacing using external cardiac signals. U.S. patent application 20210315521 (Webster et al., Oct. 14, 2021, “Modular Cardiac Patient Treatment and Monitoring”) discloses a wearable healthcare system having a reconfigurable medical device. U.S. patent application 20220040487 (Burnam et al., Feb. 10, 2022, “An Intelligently, Continuously, and Physiologically Controlled Pacemaker and Method of Operation of the Same”) discloses a pacemaker control system with internal and external sensors.

U.S. patent application 20220072316 (Yoon et al., Mar. 10, 2022, “Dual Sensors to Control Pacing Rate”) discloses a medical device which uses acceleration and temperature to adjust the rate of cardiac pacing. U.S. patent application 20220088369 (Chopra et al., Mar. 24, 2022, “Systems and Methods for Pump-Assisted Blood Circulation”) discloses systems, devices, and methods that use a pump to assist blood flow.

U.S. patent application 20220152397 (Kelley et al., May 19, 2022, “Neurostimulation Evaluation, Programming and Control Based on Sensed Blood Flow”) discloses a neurostimulation device that receives blood flow information.

U.S. patent application 20220160250 (Anderson et al., May 26, 2022, “Detection and Mitigation of Inaccurate Sensing by an Implanted Sensor of a Medical System”) discloses techniques for detecting and mitigating inaccurate sensing in a medical system. U.S. patent application 20220168576 (Ghosh, Jun. 2, 2022, “Evaluation and Adjustment of Left Bundle Branch (LBB) Pacing Therapy”) discloses systems and methods for evaluation and adjustment of left bundle branch pacing therapy. U.S. patent application 20220192605 (Webster et al., Jun. 23, 2022, “Managing Cardiac Risk Based on Physiological Markers”) discloses a method to track the cardiac health of a patient using a motion sensor.

U.S. patent application 20220211282 (Rowland et al., Jul. 7, 2022, “Hemodynamic Monitoring System and Method and Harness for Same”) discloses a harness-based hemodynamic monitoring system. U.S. patent application 20220211332 (Demmer et al., Jul. 7, 2022, “Medical Device System for Monitoring Patient Health”) discloses a method of monitoring a patient with a medical device and a peripheral device. U.S. patent application 20220225949 (Umberger et al., Jul. 21, 2022, “Wearable Device Network System”) discloses a method to track cardiac health using a sensor. U.S. patent application 20220240832 (Kim et al., Aug. 4, 2022, “Wearable Medical Device with Zoneless Arrhythmia Detection”) discloses a wearable monitoring device to zonelessly detect arrhythmias from physiological signals.

U.S. patent application 20220265209 (Panken et al., Aug. 25, 2022, “Medical Device Using Spectral Activity Processing”) discloses a stimulation system with accelerometers. U.S. patent application 20220280047 (Stadler et al., Sep. 8, 2022, “Automatic Alert Control for Acute Health Event”) discloses an antenna to wirelessly receive communication from a medical device. U.S. patent application 20220296906 (Westphal et al., Sep. 22, 2022, “Indirect Sensing Mechanism for Cardiac Monitoring”) discloses systems and techniques with a mechanosensor for detecting changes in patient health. U.S. patent application 20220314013 (Sullivan, Oct. 6, 2022, “Wearable Cardioverter Defibrillator (WCD) System Making Shock/No Shock Determinations From Multiple Patient Parameters”) discloses a wearable cardioverter defibrillator (WCD) system which senses patient parameters from different parts of a body.

U.S. patent application 20220323012 (Pool et al., Oct. 13, 2022, “Sensor Integration in Cardiac Implant Devices”) discloses a sensor-retention structure with a sensor-support strut. U.S. patent application 20220369937 (Cho et al., Nov. 24, 2022, “Acute Health Event Monitoring”) discloses a system to detect sudden cardiac arrest. U.S. patent application 20230046704 (Yoder et al., Feb. 16, 2023, “Remote Monitoring and Support of Medical Devices”) discloses systems and techniques for detecting a change in patient health and modifying settings of a medical device. U.S. patent application 20230077146 (Muse et al., Mar. 9, 2023, “Artificial Heart Control Systems and Methods”) discloses a controller for an artificial heart for activity-specific adjustments to the operation of the artificial heart by obtaining sensor data.

U.S. patent application 20230114876 (Brincat et al., Apr. 13, 2023, “Body Area Network Having Sensing Capability”) discloses systems and methods for monitoring health of a user including a plurality of sensors worn by or implanted in a user. U.S. patent application 20230138882 (Webster et al., May 4, 2023, “Integrative Wearable Health Monitoring”) discloses an integrative wearable health monitoring capable of displaying information about devices and sensors. U.S. patent application 20230149694 (Taskin et al., May 18, 2023, “Mechanical Circulatory Support Device”) discloses a pump which provides pulsating blood flow.

U.S. patent application 20230154304 (Ship et al., May 18, 2023, “Systems and Methods for Activity Tracking and Physiological Sensing for Cardiac Recovery Assessment and Support in Mechanical Blood Pumps”) discloses a system and method for supporting and monitoring mechanical blood pumps using input from the one or more sensors. U.S. patent application 20230181916 (Zhang et al., Jun. 15, 2023, “Modular Defibrilator System”) discloses an implantable cardiac monitoring device (ICMD) which monitors physiological signals and, in response to detecting a current or imminent arrhythmia in the patient, transmits data to an external user device.

U.S. patent application 20230277100 (Meeker et al., Sep. 7, 2023, “Oximetry Monitoring in a Wearable Medical Device”) discloses technologies and implementations for oximetry monitoring in a wearable medical device (WMD). U.S. patent application 20230285764 (Rowbotham et al., Sep. 14, 2023, “Wearable Medical System (WMS) Implementing Wearable Cardioverter Defibrillator (WCD) and Recording ECG of Patient in Regular Mode and in Rich Mode”) discloses a wearable medical system (WMS) with a wearable cardioverter defibrillator (WCD) that senses ECG signals. U.S. patent application 20230309861 (Galarneau et al., Oct. 5, 2023, “Motion Sensor-Modulated Cardiac Episode Detection and/or Alerting”) discloses a medical device system which stores information relating to a cardiac event.

SUMMARY OF THE INVENTION

This invention is a method or system for controlling (e.g. adjusting or managing) the operation of one or more implanted medical devices. This method or system uses machine learning and/or artificial intelligence (AI) to identify the lagged effects of operating parameters of the one or more implanted medical devices on a person's biometric indicators. This method or system then adjusts the operating parameters of the one or more implanted medical devices to change the values of the biometric indicators in a desired direction and/or by a desired amount. In an example, an implanted medical device can be a cardiac pacemaker. In an example, biometric indicators can include blood or tissue oxygenation level and peripheral blood pressure.

In an example, values of a person's biometric indicators can be obtained from a combination of internal (e.g. implanted) sensors and external (e.g. wearable) sensors. In an example, values of biometric indicators can be obtained from sensors at different locations in and/or on a person's body. Controlling the operation of one or more implanted medical devices based on biometric indicator values from different locations can help to ensure the tissue health of body extremities better than only using values from a sensor in a single location (e.g. only a sensor in a single implanted medical device). For example, there can be variation in blood or tissue oxygenation levels and peripheral blood pressures between different body extremities. A method or system which recognizes and responds to this variation can adjust the operation of one or more implanted medical devices more effectively to maintain good tissue health for all body extremities.

INTRODUCTION TO THE FIGURES

FIG. 1 shows a method of managing the operation of an implanted medical device comprising: using machine learning and/or artificial intelligence to identify the effects of operating parameters of an implanted medical device on biometric indicators during a first time period; and using those identified effects to adjust the operating parameters of the implanted medical device to change the biometric indicators during a second time period.

FIG. 2 shows an example of a method of managing the operation of an implanted medical device comprising: receiving data during a first time period concerning operating parameters of an implanted medical device; receiving data during a second time period concerning biometric indicators; using machine learning and/or artificial intelligence to identify lagged effects of the operating parameters of the implanted medical device on the biometric indicators; receiving data during a third time period concerning the biometric indicators; and using the previously-identified lagged effects to determine how to change the operating parameters of the implanted medical device in order to change the biometric indicators in a desired direction and/or by a desired amount.

DETAILED DESCRIPTION OF THE FIGURES

FIG. 1 shows an example of a method of managing the operation of an implanted medical device comprising: using machine learning and/or artificial intelligence to identify the effects of one or more operating parameters of an implanted medical device implanted in a person on values of one or more biometric indicators concerning the person during a first time period 101 ; and using the identified effects to adjust the operating parameters of the implanted medical device to change the values of the one or more biometric indicators in a desired direction and/or by a desired amount during a second time period 102 .

FIG. 2 shows an example of a method of managing the operation of an implanted medical device comprising: receiving a first set of data collected during a first time period concerning one or more operating parameters of an implanted medical device which is configured to be implanted in a person 201 ; receiving a second set of data collected during a second time period concerning the values of one or more biometric indicators for the person 202 ; using machine learning and/or artificial intelligence to analyze the first set of data and the second set of data to identify lagged effects of the one or more operating parameters of the implanted medical device on the values of the one or more biometric indicators 203 ; receiving a third set of data collected during a third time period concerning the values of the one or more biometric indicators for the person 204 ; and using the third set of data and the previously-identified lagged effects to determine how to change one or more operating parameters of the implanted medical device in order to change the values of the one or more biometric indicators in a desired direction and/or by a desired amount 205 .

In an example, a method of managing the operation of an implanted medical device can comprise: using machine learning and/or artificial intelligence to identify the effects of one or more operating parameters of an implanted medical device implanted in a person on values of one or more biometric indicators concerning the person during a first time period; and using the identified effects to adjust the operating parameters of the implanted medical device to change the values of the one or more biometric indicators in a desired direction and/or by a desired amount during a second time period.

In an example, the implanted medical device can be selected from the group consisting of: implanted cardiac pacemaker; implanted cardioverter defibrillator; implanted blood pump; implanted neurostimulator; and implanted drug pump. In an example, the one or more biometric indicators can be selected from the group consisting of: blood or tissue oxygen level; blood pressure; heart rate; blood glucose level; respiration rate; body posture; body motion; body temperature; chewing or swallowing motion; and brainwave pattern. In an example, data on the one or more biometric indicators can come from one or more sensors which are selected from the group consisting of: optical sensor; spectroscopic optical sensor; oximeter; PPG sensor; glucose sensor; inertial motion sensor; strain sensor; moisture sensor; electromagnetic energy sensor; EEG sensor; and EMG sensor.

In an example, at least some of the data on the one or more biometric indicators can come from one or more sensors on one or more wearable devices what are selected from the group consisting of: smart watch or watch band; wrist band; arm band; bracelet; finger ring; necklace; smart eyewear or an eyewear attachment; smart ear bud or ear ring; smart clothing; and sensor-enabled adhesive patch. In an example, the operating parameters of the implanted medical device can be adjusted based at least in part on the values of the one or more biometric indicators during the second time period. In an example, the operating parameters of the implanted medical device can be adjusted based at least in part on changes overtime and/or variation over time in the values of the one or more biometric indicators during the second time period. In an example, data can come at least partly from a plurality of wearable devices which are worn at different locations on the person's body. In an example, the operating parameters of the implanted medical device can be adjusted based at least in part on differences in values for the one or more biometric indicators between sensors in wearable devices at different locations on the person's body.

In an example, a method of managing the operation of an implanted medical device can comprise: receiving a first set of data collected during a first time period concerning one or more operating parameters of an implanted medical device which is configured to be implanted in a person; receiving a second set of data collected during a second time period concerning the values of one or more biometric indicators for the person; using machine learning and/or artificial intelligence to analyze the first set of data and the second set of data to identify lagged effects of the one or more operating parameters of the implanted medical device on the values of the one or more biometric indicators; receiving a third set of data collected during a third time period concerning the values of the one or more biometric indicators for the person; and using the third set of data and the previously-identified lagged effects to determine how to change one or more operating parameters of the implanted medical device in order to change the values of the one or more biometric indicators in a desired direction and/or by a desired amount.

In an example, the implanted medical device can be selected from the group consisting of: implanted cardiac pacemaker; implanted cardioverter defibrillator; implanted blood pump; implanted neurostimulator; and implanted drug pump. In an example, the one or more biometric indicators can be selected from the group consisting of: blood or tissue oxygen level; blood pressure; heart rate; blood glucose level; respiration rate; body posture; body motion; body temperature; chewing or swallowing motion; and brainwave pattern. In an example, data on the one or more biometric indicators can come from one or more sensors which are selected from the group consisting of: optical sensor; spectroscopic optical sensor; oximeter; PPG sensor; glucose sensor; inertial motion sensor; strain sensor; moisture sensor; electromagnetic energy sensor; EEG sensor; and EMG sensor.

In an example, at least some of the data on the one or more biometric indicators can come from one or more sensors on one or more wearable devices what are selected from the group consisting of: smart watch or watch band; wrist band; arm band; bracelet; finger ring; necklace; smart eyewear or an eyewear attachment; smart ear bud or ear ring; smart clothing; and sensor-enabled adhesive patch. In an example, the operating parameters of the implanted medical device can be changed based at least in part on the values of the one or more biometric indicators in the third set of data. In an example, the operating parameters of the implanted medical device can be changed based at least in part on changes over time and/or variation over time in the values of the one or more biometric indicators in the third set of data. In an example, machine learning and/or artificial intelligence can be used to analyze the first set of data and the second set of data to identify lagged effects of changes in the one or more operating parameters of the implanted medical device on changes in the values of the one or more biometric indicators.

In an example, the operating parameters of the implanted medical device can be changed based at least in part on the whether the values of the one or more biometric indicators are outside their normal ranges for the person in the third set of data. In an example, the second set of data can be at least partly from a plurality of wearable devices which are worn at different locations on the person's body. In an example, the operating parameters of the implanted medical device can be changed based at least in part on differences in values for the one or more biometric indicators between sensors in wearable devices at different locations on the person's body.

A method of adjusting operating parameters of an implanted medical device based on biometric data from implanted sensors and wearable sensors comprising: receiving device parameter data collected during a first time period, wherein the device parameter data comprises values for one or more operating parameters of an implanted medical device which is configured to be implanted in a person; receiving biometric data collected during a second time period, wherein the biometric data comes from a combination of implanted sensors which are configured to be implanted in the person and wearable sensors which are configured to be worn by the person, wherein the biometric data comprises values for one or more biometric statistics concerning the person, and wherein the second period time is after the first time period; using machine learning to analyze the device parameter data and the biometric data to identify lagged effects of the operating parameters of the implanted medical device during the first time period on the biometric statistics during the second time period; receiving biometric data collected during a third time period; using machine learning to identify whether biometric statistics measured during the third time period should be changed in a selected direction and/or by a selected amount; and using machine learning and the previously-identified lagged effects to determine how the operating parameters of the implanted medical device should be changed in order to change the biometric statistics in the selected direction and/or by the selected amount during a fourth time period.

In an example, a wearable device which is part of a system or method can comprise a pulse oximeter. In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on peripheral oxygen saturation (SpO2) level or arterial oxygen saturation (SaO2) level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oximeters. In an example, data concerning one or more biometric indicators can be obtained from a combination of internal (e.g. implanted) sensors and external (e.g. wearable) sensors on a pulse oximeter which is worn by the person. In an example, a closed-loop system for adjusting operating parameters of an implanted medical device can include an oxygen saturation sensor, SpO2 sensor, and/or pulse oximeter.

In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on machine learning (e.g. AI) and peripheral oxygen saturation (SpO2) level or arterial oxygen saturation (SaO2) level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oxygen sensors. In an example, the operating parameters of an implanted medical device can be automatically adjusted based on peripheral oxygen saturation (SpO2) level or arterial oxygen saturation (SaO2) level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oximeters. Working together in an integrated system, a wearable device for measuring oxygen level in body extremities and an implanted device for cardiac rhythm management can help to prevent oxygen deficiencies in body extremities. In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on blood or tissue oxygenation level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oximeters.

In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on machine learning (e.g. AI) and blood or tissue oxygenation level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oximeters. Without a wearable device component to measure body oxygen levels in body extremities, an implanted cardiac rhythm management device alone is not aware of oxygen deficiencies in body extremities. In an example, the operating parameters of an implanted medical device can be automatically adjusted based on blood or tissue oxygenation level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oximeters. In an example, a method can comprise receiving biometric data from an oxygen saturation sensor, SpO2 sensor, and/or pulse oximeter.

Without an implanted cardiac rhythm management device, a wearable device alone can provide information on oxygenation levels in body extremities, but does not provide automatic therapeutic correction for oxygenation deficiency in body extremities. In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on machine learning (e.g. AI) and blood or tissue oxygenation level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oxygen sensors. In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on blood or tissue oxygenation level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oxygen sensors.

In an example, a closed loop system for human circulatory assistance can increase blood circulation by adjusting the operation of a cardiac pacemaker in response to a low body oxygenation level. In an example, the biometric indicator which is measured and managed by a system can be selected from the group consisting of: oxygenation level, carbon dioxide level, lactate or lactic acid level, blood pressure, heart rate variability, pulsatile blood volume, pulsatile blood lag, hydration level, respiration rate, exhaled gas composition, body glucose level, troponin level, body motion or exercise level, and sleep status or stage. In an example, human circulatory assistance can be provided by an implanted cardiac pacemaker whose operation is adjusted in real time based on overall body oxygenation level.

In an example, a cardiac pacemaker can deliver electromagnetic energy pulses to the heart via wires and/or leads. In an example, a cardiac pacemaker can be implanted within the heart, wherein it directly delivers electromagnetic energy pulses to the heart walls. In an example, the system can decrease the frequency and/or magnitude of electromagnetic pulses delivered to the person's heart when analysis of data from the light receiver indicates a high level of oxygen in body tissue (and/or fluid). In an example, the system can change the frequency and/or magnitude of electromagnetic pulses delivered to a person's heart when analysis of data from the light receiver indicates a change in the level of oxygen in body tissue (and/or fluid). In an example, an implanted circulatory assistance device can be a cardiac pacemaker which delivers periodic electromagnetic energy pulses to a person's heart in order to stimulate and/or regulate contraction of heart muscles.

In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on peripheral oxygen saturation (SpO2) level or arterial oxygen saturation (SaO2) level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oxygen sensors. In an example, a method can comprise using a support vector machine (SVM) to adjust operating parameters of an implanted cardiac pacemaker based on biometric indicators measured by implanted and wearable sensors. In an example, the operating parameters of an implanted cardiac pacemaker which are adjusted by a system and/or method can comprise duty cycle, and/or left ventricular or thoracic surrogate electrical activation times. In an example, a system can monitor a person's systolic blood pressure and adjust the operating parameters of an implanted cardiac pacemaker in response to abnormal blood pressure values.

In an example, a method can comprise increasing the frequency and/or magnitude of electromagnetic pulses delivered from a pacemaker to a person's heart when analysis of data from an oxygen sensor indicates a low level of oxygen in blood and/or body tissue. In an example, a method can comprise increasing the frequency and/or magnitude of electromagnetic pulses delivered from a pacemaker to a person's heart when analysis of data from an implanted oxygen sensor indicates a low level of oxygen in blood and/or body tissue. In an example, a method can comprise increasing the frequency and/or magnitude of electromagnetic pulses delivered from a pacemaker to a person's heart when analysis of data from an external (e.g. wearable) oxygen sensor indicates a low level of oxygen in blood and/or body tissue.

In an example, the operating parameters of an implanted medical device can be automatically adjusted based on blood or tissue oxygenation level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oxygen sensors. In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on blood or tissue oxygenation levels measured by external oxygen sensors worn on two different locations on a person's body. In an example, the operating parameters of a person's implanted cardiac pacemaker can be automatically adjusted based on combined analysis of blood or tissue oxygenation levels measured by oxygen sensors on the person's wrist and torso. In an example, the operating parameters of a person's implanted cardiac pacemaker can be automatically adjusted based on combined analysis of blood or tissue oxygenation levels measured by oxygen sensors on the person's wrist and leg. In an example, the operating parameters of a person's implanted cardiac pacemaker can be automatically adjusted based on combined analysis of blood or tissue oxygenation levels measured by oxygen sensors on the person's right and left wrists.

In an example, a method can comprise using artificial intelligence (AI) to adjust operating parameters of an implanted cardiac pacemaker based on biometric indicators measured by implanted and wearable sensors. In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on differences in the blood or tissue oxygenation levels which are measured by external oxygen sensors on two different locations on a person's body. In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on machine learning (e.g. AI) and peripheral oxygen saturation (SpO2) level or arterial oxygen saturation (SaO2) level as measured by external (e.g. wearable) and/or internal (e.g. implanted) oximeters.

In an example, the operation of an implanted non-central (peripheral) blood pump can be adjusted based on multivariate analysis of data concerning biometric indicator levels from a plurality of wearable devices worn at different locations on a person's body. In an example, the operation of an implanted non-central (peripheral) blood pump can be adjusted based on the average of biometric indicator levels, the lowest biometric indicator level, the highest biometric indicator level, and/or the range or variability of biometric indicator levels measured by a plurality of wearable devices worn at different locations on a person's body. In an example, the operating parameters of an implanted cardiac pacemaker can be automatically adjusted based on differences over time in the blood or tissue oxygenation levels which are measured by external oxygen sensors on two different locations on a person's body.

In an example, a method for adjustment and/or control of an implanted medical device (e.g. pacemaker, ICD, neurostimulator, or drug pump) can comprise: using machine learning and/or artificial intelligence to adjust one or more operating parameters of the implanted medical device based on one or more aspects (e.g. level, change rate, variation or cycle frequency over time, variation by body location, minimum value, or maximum value) of one or more biometric indicators (e.g. blood or tissue oxygen level, blood pressure, heart rate, respiration rate, body posture and/or movement, or blood glucose level) measured by a combination of implanted and wearable sensors.

In an example, a method for adjustment and/or control of an implanted medical device (e.g. pacemaker, ICD, neurostimulator, or drug pump) can comprise: using machine learning and/or artificial intelligence to identify the relationships between one or more operating parameters of the implanted medical device and lagged values (e.g. level, change rate, variation or cycle frequency over time, variation by body location, minimum value, or maximum value) of one or more biometric indicators (e.g. blood or tissue oxygen level, blood pressure, heart rate, respiration rate, body posture and/or movement, or blood glucose level).

In an example, a method for adjustment and/or control of an implanted medical device (e.g. pacemaker, ICD, neurostimulator, or drug pump) can comprise: using machine learning and/or artificial intelligence to identify the relationships between changes in one or more operating parameters of the implanted medical device and lagged changes in the values (e.g. level, change rate, variation or cycle frequency over time, variation by body location, minimum value, or maximum value) of one or more biometric indicators (e.g. blood or tissue oxygen level, blood pressure, heart rate, respiration rate, body posture and/or movement, or blood glucose level), wherein the lagged changes are measured between 1 and 10 minutes after the operating parameter changes; and subsequently adjusting the operating parameters of the implanted medical device based on measured values of these biometric indicators in light of these relationships.

In an example, a method for adjustment and/or control of an implanted medical device (e.g. pacemaker, ICD, neurostimulator, or drug pump) can comprise: using machine learning and/or artificial intelligence to identify the relationships between changes in one or more operating parameters of the implanted medical device and lagged changes in the values (e.g. level, change rate, variation or cycle frequency over time, variation by body location, minimum value, or maximum value) of one or more biometric indicators (e.g. blood or tissue oxygen level, blood pressure, heart rate, respiration rate, body posture and/or movement, or blood glucose level), wherein the lagged changes are measured between 5 and 30 minutes after the operating parameter changes; and subsequently adjusting the operating parameters of the implanted medical device based on measured values of these biometric indicators in light of these relationships.

In an example, a method for adjustment and/or control of an implanted medical device (e.g. pacemaker, ICD, neurostimulator, or drug pump) can comprise: using machine learning and/or artificial intelligence to identify the relationships between changes in one or more operating parameters of the implanted medical device and lagged changes in the values (e.g. level, change rate, variation or cycle frequency over time, variation by body location, minimum value, or maximum value) of one or more biometric indicators (e.g. blood or tissue oxygen level, blood pressure, heart rate, respiration rate, body posture and/or movement, or blood glucose level), wherein the lagged changes are measured between 25 and 100 minutes after the operating parameter changes; and subsequently adjusting the operating parameters of the implanted medical device based on measured values of these biometric indicators in light of these relationships.

In an example, a method for adjustment and/or control of an implanted medical device (e.g. pacemaker, ICD, neurostimulator, or drug pump) can comprise: using machine learning and/or artificial intelligence to identify the relationships between changes in one or more operating parameters of the implanted medical device and lagged changes in the values (e.g. level, change rate, variation or cycle frequency over time, variation by body location, minimum value, or maximum value) of one or more biometric indicators (e.g. blood or tissue oxygen level, blood pressure, heart rate, respiration rate, body posture and/or movement, or blood glucose level), wherein the lagged changes are measured between 1 and 10 minutes after the operating parameter changes.

In an example, a method for adjustment and/or control of an implanted medical device (e.g. pacemaker, ICD, neurostimulator, or drug pump) can comprise: using machine learning and/or artificial intelligence to identify the relationships between changes in one or more operating parameters of the implanted medical device and lagged changes in the values (e.g. level, change rate, variation or cycle frequency over time, variation by body location, minimum value, or maximum value) of one or more biometric indicators (e.g. blood or tissue oxygen level, blood pressure, heart rate, respiration rate, body posture and/or movement, or blood glucose level), wherein the lagged changes are measured between 5 and 30 minutes after the operating parameter changes.

In an example, a method for adjustment and/or control of an implanted medical device (e.g. pacemaker, ICD, neurostimulator, or drug pump) can comprise: using machine learning and/or artificial intelligence to identify the relationships between changes in one or more operating parameters of the implanted medical device and lagged changes in the values (e.g. level, change rate, variation or cycle frequency over time, variation by body location, minimum value, or maximum value) of one or more biometric indicators (e.g. blood or tissue oxygen level, blood pressure, heart rate, respiration rate, body posture and/or movement, or blood glucose level), wherein the lagged changes are measured between 25 and 100 minutes after the operating parameter changes.

In an example, measurement of one or more biometric indicators from one or more peripheral locations (such as a person's ears, fingers, or feet) can provide information on cardiac function and peripheral tissue health which is not available from a central location such as a person's heart or abdomen. In an example, the operating parameters of an implanted cardiac pacemaker can be automati

CLAIMS

Claims ( 20 )

I claim:

1 . A method of managing the operation of an implanted medical device comprising:

using machine learning and/or artificial intelligence to identify the effects of one or more operating parameters of an implanted medical device implanted in a person on values of one or more biometric indicators concerning the person during a first time period; and using the identified effects to adjust the operating parameters of the implanted medical device to change the values of the one or more biometric indicators in a desired direction and/or by a desired amount during a second time period.

2 . The method in claim 1 wherein the implanted medical device is selected from the group consisting of: implanted cardiac pacemaker; implanted cardioverter defibrillator; implanted blood pump; implanted neurostimulator; and implanted drug pump.

3 . The method in claim 1 wherein the one or more biometric indicators are selected from the group consisting of: blood or tissue oxygen level; blood pressure; heart rate; blood glucose level; respiration rate;

body posture; body motion; body temperature; chewing or swallowing motion; and brainwave pattern.

4 . The method in claim 1 wherein data on the one or more biometric indicators comes from one or more sensors which are selected from the group consisting of: optical sensor; spectroscopic optical sensor;

oximeter; PPG sensor; glucose sensor; inertial motion sensor; strain sensor; moisture sensor;

electromagnetic energy sensor; EEG sensor; and EMG sensor.

5 . The method in claim 1 wherein at least some of the data on the one or more biometric indicators comes from one or more sensors on one or more wearable devices what are selected from the group consisting of: smart watch or watch band; wrist band; arm band; bracelet; finger ring; necklace; smart eyewear or an eyewear attachment; smart ear bud or ear ring; smart clothing; and sensor-enabled adhesive patch.

6 . The method in claim 1 wherein the operating parameters of the implanted medical device are adjusted based at least in part on the values of the one or more biometric indicators during the second time period.

7 . The method in claim 1 wherein the operating parameters of the implanted medical device are adjusted based at least in part on changes over time and/or variation over time in the values of the one or more biometric indicators during the second time period.

8 . The method in claim 1 wherein data comes at least partly from a plurality of wearable devices which are worn at different locations on the person's body.

9 . The method in claim 8 wherein the operating parameters of the implanted medical device are adjusted based at least in part on differences in values for the one or more biometric indicators between sensors in wearable devices at different locations on the person's body.

10 . A method of managing the operation of an implanted medical device comprising:

receiving a first set of data collected during a first time period concerning one or more operating parameters of an implanted medical device which is configured to be implanted in a person; receiving a second set of data collected during a second time period concerning the values of one or more biometric indicators for the person; using machine learning and/or artificial intelligence to analyze the first set of data and the second set of data to identify lagged effects of the one or more operating parameters of the implanted medical device on the values of the one or more biometric indicators; receiving a third set of data collected during a third time period concerning the values of the one or more biometric indicators for the person; and using the third set of data and the previously-identified lagged effects to determine how to change one or more operating parameters of the implanted medical device in order to change the values of the one or more biometric indicators in a desired direction and/or by a desired amount.

11 . The method in claim 10 wherein the implanted medical device is selected from the group consisting of: implanted cardiac pacemaker; implanted cardioverter defibrillator; implanted blood pump; implanted neurostimulator; and implanted drug pump.

12 . The method in claim 10 wherein the one or more biometric indicators are selected from the group consisting of: blood or tissue oxygen level; blood pressure; heart rate; blood glucose level; respiration rate;

body posture; body motion; body temperature; chewing or swallowing motion; and brainwave pattern.

13 . The method in claim 10 wherein data on the one or more biometric indicators comes from one or more sensors which are selected from the group consisting of: optical sensor; spectroscopic optical sensor;

oximeter; PPG sensor; glucose sensor; inertial motion sensor; strain sensor; moisture sensor;

electromagnetic energy sensor; EEG sensor; and EMG sensor.

14 . The method in claim 10 wherein at least some of the data on the one or more biometric indicators comes from one or more sensors on one or more wearable devices what are selected from the group consisting of: smart watch or watch band; wrist band; arm band; bracelet; finger ring; necklace; smart eyewear or an eyewear attachment; smart ear bud or ear ring; smart clothing; and sensor-enabled adhesive patch.

15 . The method in claim 10 wherein the operating parameters of the implanted medical device are changed based at least in part on the values of the one or more biometric indicators in the third set of data.

16 . The method in claim 10 wherein the operating parameters of the implanted medical device are changed based at least in part on changes over time and/or variation over time in the values of the one or more biometric indicators in the third set of data.

17 . The method in claim 10 wherein machine learning and/or artificial intelligence is used to analyze the first set of data and the second set of data to identify lagged effects of changes in the one or more operating parameters of the implanted medical device on changes in the values of the one or more biometric indicators.

18 . The method in claim 10 wherein the operating parameters of the implanted medical device are changed based at least in part on the whether the values of the one or more biometric indicators are outside their normal ranges for the person in the third set of data.

19 . The method in claim 10 wherein the second set of data is at least partly from a plurality of wearable devices which are worn at different locations on the person's body.

20 . The method in claim 19 wherein the operating parameters of the implanted medical device are changed based at least in part on differences in values for the one or more biometric indicators between sensors in wearable devices at different locations on the person's body.

US18/386,790

2013-05-23

2023-11-03

Method or System Using Machine Learning and/or Artificial Intelligence (AI) to Control the Operation of an Implanted Medical Device Based on Biometric Indicators

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US18/386,790

US20240065550A1

( en )

2013-05-23

2023-11-03

Method or System Using Machine Learning and/or Artificial Intelligence (AI) to Control the Operation of an Implanted Medical Device Based on Biometric Indicators

US19/028,686

US20250161692A1

( en )

2013-05-23

2025-01-17

System for Cardiac Rhythm Management Which Ensures Good Oxygenation of Body Extremities

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US18/386,790

US20240065550A1

( en )

2013-05-23

2023-11-03

Method or System Using Machine Learning and/or Artificial Intelligence (AI) to Control the Operation of an Implanted Medical Device Based on Biometric Indicators

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2013-05-23

2021-08-24

Integrated system to assist cardiovascular functioning with implanted cardiac device and sensor-enabled wearable device

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System for Cardiac Rhythm Management Which Ensures Good Oxygenation of Body Extremities

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Method or System Using Machine Learning and/or Artificial Intelligence (AI) to Control the Operation of an Implanted Medical Device Based on Biometric Indicators

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