Methods and systems for arrhythmia tracking and scoring

Patent metadata

Field	Value
Publication number	US-9420956-B2
Application number	US-201414569513-A
Country	US
Kind code	B2
Filing date	Dec 12, 2014
Priority date	Dec 12, 2013
Publication date	Aug 23, 2016
Grant date	Aug 23, 2016

Abstract

A dashboard centered around arrhythmia or atrial fibrillation tracking is provided. The dashboard includes a heart or cardiac health score that can be calculated in response to data from the user such as their ECG and other personal information and cardiac health influencing factors. The dashboard also provides to the user recommendations or goals, such as daily goals, for the user to meet and thereby improve their heart or cardiac health score. These goals and recommendations may be set by the user or a medical professional and routinely updated as his or her heart or cardiac health score improves or otherwise changes. The dashboard is generally displayed from an application provided on a smartphone or tablet computer of the user.

First claim

What is claimed is:

1. A method of evaluating health of a heart of a user, the method comprising
receiving heart rate information from a heart rate sensor located on a surface of a wearable computing device worn by a user;
transmitting said heart rate information to a processor of said wearable computing device;
determining an irregular heart rate variability (HRV) value, with said processor, in response to said received heart rate information; and
sensing an electrocardiogram of said user with said wearable computing device in response to said irregular HRV value.

2. The method of claim 1 , comprising generating a Heart Health Score based on said determined HRV value and on or more of said sensed electrocardiogram, a determined presence of an arrhythmia, a number of premature beats, a frequency of said premature beats, and a Heart Rate Turbulence (HRT) value.

3. The method of claim 2 , further comprising displaying said Heart Health Score with a display of said wearable computing device.

4. The method of claim 1 , wherein said heart rate information is measured by said heart rate sensor continuously for at least 2 hours.

5. The method of claim 4 , wherein said heart rate information is measured by said heart rate sensor continuously for at least 7 days.

6. The method of claim 2 , wherein said Heart Health Score ranges from a low of 1 to a high of 100.

7. The method of claim 1 , wherein said wearable computing device comprises a smartband or a smartwatch.

8. The method of claim 2 , wherein said heart health score is transmitted to a remote server or cloud server, and wherein said heart health score is accessible on said remote server or said cloud server to other users.

9. The method of claim 1 , wherein said step of sensing said electrocardiogram comprises providing an indication to said user to sense said electrocardiogram.

10. The method of claim 9 , wherein said indication comprises an alert.

11. The method of claim 1 , comprising providing said user with an indication to input physiologic information associated with said received heart rate information into said wearable computing device.

12. A method of determining a presence of an arrhythmia of a user, said method comprising
receiving heart rate information from a heart rate sensor located on a surface of a wearable computing device worn by a user;
transmitting said heart rate information to a processor of said wearable computing device;
determining an heart rate variability (HRV) value, with said processor, in response to said received heart rate information;
determining a presence of an atrial fibrillation of said user in response to said determined heart rate variability (HRV) value; and
sensing an electrocardiogram with said wearable computing device in response to said presence of said atrial fibrillation.

13. The method of claim 12 , further comprising displaying to said user an alert on said display of said wearable device if said presence of said atrial fibrillation is determined.

14. The method of claim 12 , comprising receiving heart rate information from a plurality of heart rate sensors coupled to a plurality of users.

15. The method of claim 14 , further comprising training a machine learning algorithm to recognize a presence of an atrial fibrillation of an individual user of the plurality of users in response to said heart rate information from the plurality of heart rate sensors.

16. The method of claim 15 , wherein said plurality of users have been previously identified as having atrial fibrillation or as having no atrial fibrillation.

17. The method of claim 15 , wherein determining said presence of said atrial fibrillation of said user in response to said determined heart rate (HRV) value comprises determining said presence of said atrial fibrillation of said user using said trained machine learning algorithm.

18. The method of claim 12 , comprising receiving activity level data associated with said received heart rate information from an activity level sensor on said wearable computing device.

19. The method of claim 12 , wherein said presence of said atrial fibrillation is determined in part by comparing said determined HRV value with said received activity level.

20. The method of claim 19 , wherein said step of sensing said electrocardiogram comprises providing an indication to said user to sense said electrocardiogram.

21. The method of claim 20 , wherein said indication comprises an alert.

Assignees

• Alivecor Inc

Inventors

• Gopalakrishnan Ravi
• Korzinov Lev
• Wang Fei
• Thomson Euan
• Srivastava Nupur
• Dawood Omar
• Abuzeid Iman
• Albert David E

Classifications

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title

• G16H10/60

  for patient-specific data, e.g. for electronic patient records · CPC title

• A61B5/746

  Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms · CPC title

• G16H40/67

  for remote operation · CPC title

• A61B5/7264

  Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems · CPC title