Discordance monitoring

• US 10,537,250 B2
• Filed: 07/21/2017
• Issued: 01/21/2020
• Est. Priority Date: 05/13/2015
• Status: Active Grant

• Current Assignee: AliveCor Inc.
• Original Assignee: AliveCor Inc.
• Inventors: Albert, David E.
• Primary Examiner(s): Tejani, Ankit D
• Application Number:
  US15/656,745
• Publication Number:
  US 20170319080A1
• Time in Patent Office: 914 Days
• Field of Search: None
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/282 Holders for multiple electrodes for introduction into the body A61B5/287
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Described herein are systems, devices, and methods for cardiac monitoring. In particular, the systems, devices, and methods described herein may be used to conveniently sense the presence of an intermittent arrhythmia in an individual. The systems, devices, and methods described herein may be further configured to sense an electrocardiogram.

Claims

1. A computer-implemented system comprising:

a digital processing device comprising an activity level sensor configured to sense an activity level value and a heart rate sensor configured to sense a heart rate value;

a memory operatively coupled to the digital processing device, the memory device comprising executable instructions that cause the digital processing device to;

receive the activity level value that is measured by the activity level sensor and the heart rate value that is sensed by the heart rate sensor;

determine that a discordance is present between the activity level value and the heart rate value; and

apply, in response to determining that the discordance is present, a machine learning algorithm to determine that an arrhythmia is present.

2. The system of claim 1, wherein the digital processing device comprises a smartwatch.

3. The system of claim 1, wherein the digital processing device comprises a first electrode and a second electrode configured to sense an electrocardiogram.

4. The system of claim 1, wherein the machine learning algorithm is trained with data comprising an electrocardiogram showing the arrhythmia when the discordance is present.

5. The system of claim 3, wherein the digital processing device is further configured to determine a heart rate variability based on the heart rate value.

6. The system of claim 5, wherein the discordance is determined to be present between the activity level value and the heart rate value based on the heart rate variability value.

7. The system of claim 6, wherein the machine learning algorithm is configured to determine a presence of said arrhythmia on said electrocardiogram, and determine a correlation between the discordance and the arrhythmia when said arrhythmia is present on said electrocardiogram.

8. The system of claim 1, wherein the digital processing device is further configured to determine a duration of said arrhythmia.

9. A method for determining the presence of an arrhythmia, comprising:

sensing an activity level value of an individual with a first sensor of a digital processing device;

sensing a heart rate value of said individual with a second sensor of said digital processing device;

determining that a discordance is present between said activity level value and said heart rate value; and

applying, in response to determining that the discordance is present, a machine learning algorithm to determine that an arrhythmia is present.

10. The method of claim 9, wherein the digital processing device comprises a smartwatch.

11. The method of claim 9, wherein the digital processing device comprises a first electrode and a second electrode configured to sense an electrocardiogram.

12. The method of claim 10, wherein the machine learning algorithm is trained with data comprising an electrocardiogram showing the arrhythmia when the discordance is present.

13. The method of claim 11, further comprising determining a heart rate variability based on the heart rate value.

14. The method of claim 13, wherein the discordance is determined to be present between the activity level value and the heart rate value based on the heart rate variability value.

15. The method of claim 14, wherein the machine learning algorithm is configured to determine a presence of said arrhythmia on said electrocardiogram, and determine a correlation between the discordance and the arrhythmia when said arrhythmia is present on said electrocardiogram.

16. The method of claim 11, wherein the computer program comprises a software module configured to determine a duration of said arrhythmia.