IP Library › Granted Patent US 12,471,786
Granted Patent B2
US 12,471,786 · App. 17/959,164 · Granted Nov 18, 2025

Methods and systems for arrhythmia tracking and scoring

Inventors: Ravi Gopalakrishnan (San Francisco, CA); Lev Korzinov (San Francisco, CA); Fei Wang (San Francisco, CA); Euan Thomson (Los Gatos, CA); Nupur Srivastava (San Francisco, CA); Omar Dawood (San Francisco, CA); Iman Abuzeid (San Francisco, CA); David E. Albert (Oklahoma City, OK)
Assignee: ALIVECOR, INC.
A61B5/02055A61B5/0022A61B5/02405A61B5/02416A61B5/0245A61B5/349A61B5/361A61B5/681A61B5/6898A61B5/7264A61B5/7275A61B5/746G16H10/60G16H40/67G16Z99/00A61B5/021A61B5/02438A61B5/1118G16H15/00G16H20/40G16H40/63G16H50/30
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Quick Facts
Patent No.
US 12,471,786
App. No.
17/959,164
Granted
Nov 18, 2025
Kind
B2
Abstract

A smart watch to detect a presence of an arrhythmia of a user is disclosed. The smart watch comprising: a processing device and a photoplethysmography (“PPG”) sensor operatively coupled to the processing device, an ECG sensor comprising two or more ECG electrodes and operatively coupled to the processing device, a display operatively coupled to the processing device, and a memory, operatively coupled to the processing device. The memory may have instructions stored thereon that, when executed by the processing device, cause the processing device to: receive PPG data from the PPG sensor, receive ECG data from the ECG sensor, detect, based on the PPG data and the ECG data simultaneously, the presence of an arrhythmia, and provide one or more recommendations to the user based on the PPG data and the ECG data.

Claims (66)

1 . An apparatus comprising:

a processing device;

a physiological parameter sensor operatively coupled to the processing device;

an electrocardiogram (ECG) sensor operatively coupled to the processing device; and

a memory, operatively coupled to the processing device, the memory having instructions stored thereon that, when executed by the processing device, cause the processing device to:

receive physiological parameter data of a user from the physiological parameter sensor;

receive ECG data of the user from the ECG sensor;

determine a subset of the ECG data corresponding to an event;

determine an RR interval based on the determined subset of the ECG data;

analyze the RR interval and the physiological parameter data using a machine learning (ML) model to detect a presence of an arrhythmia based on both the RR interval and the physiological parameter data; and

in response to detecting the presence of the arrhythmia, provide one or more recommendations to the user.

2 . The apparatus of claim 1 , wherein to analyze the RR interval, the processing device is to:

analyze the RR interval using one or more traditional heart rate variability (HRV) measurements to generate traditional HRV data; and

analyze the RR interval using one or more non-traditional HRV measurements to generate non-traditional HRV data.

3 . The apparatus of claim 1 , wherein the processing device is further to:

receive additional physiological parameters of the user, wherein to detect the presence of the arrhythmia, the processing device is to:

analyze the additional physiological parameters in addition to the RR interval and the physiological parameter data to detect the presence of the arrhythmia.

4 . The apparatus of claim 3 , wherein the additional physiological parameters comprise one or more of: an age, a gender, a weight, a height, a body type, a body mass index (BMI), a personal medical history, a family medical history, an exercise and activity level, a diet, a hydration level, an amount of sleep, a cholesterol level, an alcohol intake level, a caffeine intake level, and a smoking status of the user.

5 . The apparatus of claim 1 , wherein the processing device is further to:

in response to detecting the presence of the arrhythmia:

determine HRV data based on the ECG data;

determine heart rate turbulence (HRT) data based on the ECG data;

determine a number of premature beats; and

determine a heart health score based on the HRV data, the HRT data, and the number of premature beats.

6 . The apparatus of claim 3 , further comprising:

a motion sensor operatively coupled to the processing device, the motion sensor to measure motion data of the user, wherein to detect the presence of the arrhythmia, the processing device is to:

receive the motion data of the user; and

analyze the motion data of the user in addition to the RR interval and the physiological parameter data to detect the presence of the arrhythmia.

7 . The apparatus of claim 6 , wherein the processing device determines

the subset of the ECG data based on one or more of the physiological parameter data, the additional physiological parameters, and the motion data of the user.

8 . The apparatus of claim 4 , wherein to provide the one or more recommendations to the user, the processing device is to:

use the ML model to determine one or more components of the additional physiological parameters that are associated with the presence of the arrhythmia; and

generate the one or more recommendations based on the determined one or more components.

9 . The apparatus of claim 8 , wherein the one or more recommendations comprise a set of goals for the user indicating changes in one or more of:

the exercise and activity level, the diet, the hydration level, the amount of sleep, the cholesterol level, the alcohol intake level, the caffeine intake level, and the smoking status of the user.

10 . The apparatus of claim 8 , further comprising:

a display, and wherein the processing device provides the one or more recommendations to the user via the display.

11 . A method comprising:

receiving physiological parameter data of a user from a physiological parameter sensor;

receiving ECG data of the user from an ECG sensor;

determine a subset of the ECG data corresponding to an event;

determining an RR interval based on the determined subset of the ECG data;

analyzing the RR interval and the physiological parameter data using a machine learning (ML) model to detect a presence of an arrhythmia based on both the RR interval and the physiological parameter data; and

in response to detecting the presence of the arrhythmia, providing one or more recommendations to the user.

12 . The method of claim 11 , wherein analyzing the RR interval comprises:

analyzing the RR interval using one or more traditional heart rate variability (HRV) measurements to generate traditional HRV data; and

analyzing the RR interval using one or more non-traditional HRV measurements to generate non-traditional HRV data.

13 . The method of claim 11 , further comprising:

receiving additional physiological parameters of the user, wherein detecting the presence of the arrhythmia comprises analyzing the additional physiological parameters in addition to the RR interval and the physiological parameter data.

14 . The method of claim 13 , wherein the additional physiological parameters comprise one or more of: an age, a gender, a weight, a height, a body type, a body mass index (BMI), a personal medical history, a family medical history, an exercise and activity level, a diet, a hydration level, an amount of sleep, a cholesterol level, an alcohol intake level, a caffeine intake level, and a smoking status of the user.

15 . The method of claim 11 , further comprising:

in response to detecting the presence of the arrhythmia:

determining HRV data based on the ECG data;

determining heart rate turbulence (HRT) data based on the ECG data;

determining a number of premature beats; and

determining a heart health score based on the HRV data, the HRT data, and the number of premature beats.

16 . The method of claim 13 , further comprising:

receiving motion data of the user from a motion sensor, wherein detecting the presence of the arrhythmia comprises analyzing the motion data of the user in addition to the RR interval and the physiological parameter data.

17 . The method of claim 16 , wherein

the subset of the ECG data is determined based on one or more of the physiological parameter data, the additional physiological parameters, and the motion data of the user.

18 . The method of claim 14 , wherein providing the one or more recommendations to the user comprises:

using the ML model to determine one or more components of the additional physiological parameters that are associated with the presence of the arrhythmia; and

generating the one or more recommendations based on the determined one or more components.

19 . The method of claim 18 , wherein the one or more recommendations comprise a set of goals for the user indicating changes in one or more of:

the exercise and activity level, the diet, the hydration level, the amount of sleep, the cholesterol level, the alcohol intake level, the caffeine intake level, and the smoking status of the user.

20 . The method of claim 18 , wherein the one or more recommendations are provided to the user via a display.

Continuity (12)
Continuation 16827310 · Mar 23, 2020
Continuation 16588201 · Sep 30, 2019
Continuation 16153446 · Oct 5, 2018
Continuation 15393077 · Dec 28, 2016
Continuation 14730122 · Jun 3, 2015
Continuation 14569513 · Dec 12, 2014
Provisional Application 62014516 · Jun 19, 2014
Provisional Application 61970551 · Mar 26, 2014
Provisional Application 61969019 · Mar 21, 2014
Provisional Application 61953616 · Mar 14, 2014
Provisional Application 61915113 · Dec 12, 2013
Related Publication 20230099854A1 · Mar 30, 2023
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