Wearable defibrillation apparatus configured to apply a machine learning algorithm

Patent metadata

Field	Value
Publication number	US-11623102-B2
Application number	US-201816050928-A
Country	US
Kind code	B2
Filing date	Jul 31, 2018
Priority date	Jul 31, 2018
Publication date	Apr 11, 2023
Grant date	Apr 11, 2023

Abstract

In some examples, an apparatus configured to be worn by a patient for cardiac defibrillation comprises sensing electrodes configured to sense a cardiac signal of the patient, defibrillation electrodes, therapy delivery circuitry configured to deliver defibrillation therapy to the patient via the defibrillation electrodes, communication circuitry configured to receive data of at least one physiological signal of the patient from at least one sensing device separate from the apparatus, a memory configured to store the data, the cardiac signal, and a machine learning algorithm, and processing circuitry configured to apply the machine learning algorithm to the data and the cardiac signal to probabilistically-determine at least one state of the patient and determine whether to control delivery of the defibrillation therapy based on the at least one probabilistically-determined patient state.

First claim

What is claimed is:

1. An apparatus configured to be worn by a patient for cardiac defibrillation, the apparatus comprising:
sensing electrodes configured to sense a cardiac signal of the patient;
defibrillation electrodes;
therapy delivery circuitry configured to deliver defibrillation therapy to the patient via the defibrillation electrodes;
communication circuitry configured to receive data of at least one physiological signal of the patient from at least one sensing device separate from the apparatus;
a memory configured to store the data of the at least one physiological signal, the cardiac signal, and a machine learning algorithm; and
processing circuitry configured to:
apply the machine learning algorithm to the cardiac signal to probabilistically determine a preliminary characterization of at least one state of the patient;
determine, based on the machine learning algorithm being applied to the cardiac signal, the preliminary characterization of at least one state of the patient is not normal;
apply, based on the preliminary characterization being not normal, the machine learning algorithm to both the data of the at least one physiological signal and the cardiac signal to probabilistically determine the at least one state of the patient; and
determine whether to control delivery of the defibrillation therapy based on the at least one probabilistically-determined state of the patient,
wherein the machine learning algorithm is configured to automatically update itself based on at least one of the data of the at least one physiological signal or the cardiac signal.

2. The apparatus of claim 1 , wherein the at least one sensing device comprises a subcutaneously implantable cardiac monitor comprising a plurality of sensing electrodes to sense the physiological signal, wherein the cardiac signal comprises a first cardiac signal and the physiological signal comprises a second cardiac signal.

3. The apparatus of claim 1 , wherein the machine learning algorithm is configured to automatically update itself based on input from at least one of the patient or a healthcare provider.

4. The apparatus of claim 1 , wherein the data of the physiological signal comprises a determination, by the sensing device, of whether a tachyarrhythmia is indicated by the physiological signal, wherein the machine learning algorithm is configured to automatically update itself based on the determination by the sensing device.

5. The apparatus of claim 1 , wherein the at least one sensing device comprises at least one of:
a glucose monitor, wherein the physiological signal comprises a glucose concentration in the patient;
an electroencephalography (EEG) sensor, wherein the physiological signal comprises brain activity;
a pressure monitoring device, wherein the physiological signal comprises a cardiovascular pressure signal;
a pulse oximetry device, wherein the physiological signal comprises at least one of pulse rate, oxygen saturation, or respiration rate;
a patient activity tracker, wherein the physiological signal comprises physical activity of the patient; or
a location tracker configured to determine a location of the patient.

6. The apparatus of claim 5 ,
wherein the at least one sensing device comprises the patient activity tracker, and
wherein the patient activity tracker comprises a wearable sensing device configured to be worn on the patient.

7. The apparatus of claim 1 , wherein the apparatus comprises a wearable automated external defibrillator (WAED) comprising a garment configured to be worn by the patient, wherein the sensing electrodes and defibrillation electrodes are coupled to the garment.

8. The apparatus of claim 1 , wherein inputs to the machine learning algorithm from the cardiac signal include one or more of an R-R interval, an amplitude of an R wave, a QRS width, or an R-R interval variability.

9. The apparatus of claim 1 , wherein inputs to the machine learning algorithm from the cardiac signal and the data of the at least one physiological signal comprises one or more of a size of a feature of the signal, a frequency of the feature, a morphology of the feature, or a change in the size, frequency, or morphology over time.

10. The apparatus of claim 1 , wherein the processing circuitry is configured to control the therapy delivery circuitry to deliver the defibrillation therapy based on a result of the application of the machine learning algorithm to the data of the at least one physiological signal and the cardiac signal.

11. The apparatus of claim 1 , wherein the processing circuitry comprises a graphics processing unit (GPU) configured to apply the machine learning algorithm to the data of the at least one physiological signal and the cardiac signal to probabilistically determine the at least one state of the patient.

12. The apparatus of claim 1 , wherein the at least one probabilistically- determined patient state comprises at least one of:
whether the patient state normal; or
whether the patient state is treatable tachyarrhythmia.

13. The apparatus of claim 12 , wherein a probabilistically-determined patient state of not normal further comprises a sub-classification of one of bradycardia, treatable tachyarrhythmia, syncope, 60 Hertz noise, motion artifacts, or loss of signal.

14. The apparatus of claim 12 , wherein the at least one probabilistically-determined patient state comprises a predicted treatable tachyarrhythmia patient state, the therapy delivery circuitry is configured to deliver a therapy configured to prevent tachyarrhythmia, and the processing circuitry is further configured to determine whether to deliver the therapy configured to prevent tachyarrhythmia based on the predicted treatable tachyarrhythmia state.

15. The apparatus of claim 12 , wherein the at least one probabilistically-determined patient state comprises at least one comorbidity state of the patient.

16. The apparatus of claim 15 , wherein the at least one comorbidity state of the patient comprises a COPD state of the patient, and the at least one sensing device comprises:
a spirometer configured to generate a spirometer signal; and
an air quality sensor configured to generate an air quality signal,
wherein the processing circuitry is further configured to determine the COPD state of the patient based on application of the machine learning algorithm to the spirometer signal and the air quality signal.

17. The apparatus of claim 15 , wherein, based on the at least one comorbidity state of the patient, the processing circuitry is configured to provide an instruction to the patient.

18. A method for monitoring cardiac signals and determining whether to deliver defibrillation therapy by apparatus configured to be worn by a patient, the method comprising:
sensing, via sensing electrodes of the apparatus, a cardiac signal of the patient;
receiving, by communication circuitry of the apparatus, data of at least one physiological signal of the patient from at least one sensing device separate from the apparatus;
storing, by a memory of the apparatus, the cardiac signal, the data of the at least one physiological signal, and a machine learning algorithm;
applying, by processing circuitry of the apparatus, the machine learning algorithm to the data of the cardiac signal to probabilistically determine a preliminary characterization of at least one state of the patient;
determining, by processing circuitry of the apparatus and based on the machine learning algorithm being applied to the cardiac signal, the preliminary characterization of at least one stat

Assignees

• Medtronic Inc

Inventors

• Schulhauser Randal C
• Cao Jian
• Probst David
• Hahn Daniel
• Maass Eric C
• Kinzie Patrick W

Classifications

• A61B5/318

  Heart-related electrical modalities, e.g. electrocardiography [ECG] · CPC title

• A61B5/369

  Electroencephalography [EEG] · CPC title

• A61N1/3904 Primary

  External heart defibrillators [EHD] · CPC title

• Y02A90/10

  Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation · CPC title

• A61B5/1455

  using optical sensors, e.g. spectral photometrical oximeters · CPC title