EP-4734824-A1 - DETECTING LOW EJECTION FRACTION USING PHOTOPLETHYSMOGRAPHY (PPG)

Abstract

A computer-implemented method for detecting a cardiac dysfunction in a user includes obtaining, from a sensor, photoplethysmogram (PPG) signals indicative of a cardiac rhythm of the user. The computer-implemented method further includes processing, in a computing device, the PPG signals to generate a cardiac dysfunction prediction for the user based, at least in part, on the PPG signals. The computer-implemented method further includes providing, via an annunciator, the cardiac dysfunction prediction for the user as an output.

Inventors

• POH, MING-ZHER
• HUGHES, John Weston
• DI ACHILLE, PAOLO
• LIAO, Shun

Assignees

• Google LLC

Dates

Publication Date

20260506

Application Date

20240529

Claims (20)

1. 1. A computer-implemented method for detecting a cardiac dysfunction in a user, the method comprising: obtaining, from a sensor, photoplethysmogram (PPG) signals indicative of a cardiac rhythm of the user; processing, in a computing device, the PPG signals to generate a cardiac dysfunction prediction for the user based, at least in part, on the PPG signals; and providing, via an annunciator, the cardiac dysfunction prediction for the user as an output.
2. 2. The computer-implemented method of claim 1, wherein processing the PPG signals to generate a cardiac dysfunction prediction for the user comprises: processing, via one or more machine-learned models, the PPG signals to generate the cardiac dysfunction prediction for the user.
3. 3. The computer-implemented method of claim 2, further comprising: obtaining, via the computing device, electrocardiogram (ECG) signals indicative of the cardiac rhythm of the user; processing, via the one or more machine-learned models, the PPG signals and the ECG signals to generate the cardiac dysfunction prediction for the user.
4. 4. The computer-implemented method of claim 3, wherein the ECG signals are obtained via a single-lead electrocardiogram.
5. 5. The computer-implemented method of claim 3, wherein the one or more machine- learned models are further configured to process demographic data relating to the user to generate the cardiac dysfunction prediction for the user.
6. 6. The computer-implemented method of claim 2, wherein the one or more machine- learned models are trained with PPG data, ECG data, and demographic data.
7. 7. The computer-implemented method of claim 2, wherein the one or more machine- learned models comprise a deep learning model.
8. 8. The computer-implemented method of claim 1, wherein the cardiac dysfunction prediction for the user comprises one or more predicted probabilities that the user is experiencing the cardiac dysfunction.
9. 9. The computer-implemented method of claim 1, wherein obtaining the PPG signals comprises: obtaining, via one or more biometric sensors of a wearable computing device, PPG signals indicative of the cardiac rhythm of the user.
10. 10. The computer-implemented method of claim 9, wherein the one or more biometric sensors comprise one or more PPG sensors.
11. 11. The computer-implemented method of claim 1, wherein obtaining PPG signals comprises: obtaining, via one or more pulse oximeters, PPG signals indicative of the cardiac rhythm of the user.
12. 12. The computer-implemented method of claim 1, wherein obtaining PPG signals comprises: obtaining, via one or more cameras of a mobile computing device, PPG signals indicative of the cardiac rhythm of the user.
13. 13. The computer-implemented method of claim 1, further comprising: surfacing, via the computing device, a notification to the user indicative of the cardiac dysfunction prediction via a user interface of a wearable computing device.
14. 14. The computer-implemented method of claim 13, wherein surfacing a notification to the user indicative of the cardiac dysfunction prediction further comprises: surfacing, via the computing device, a recommendation to the user to wear an ECG monitor in response to generating the cardiac dysfunction prediction.
15. 15. The computer-implemented method of claim 1, wherein the cardiac dysfunction is left ventricular systolic dysfunction (LVSD).
16. 16. A wearable computing device comprising: one or more sensors configured to obtain photoplethysmogram (PPG) data of a user wearing the wearable computing device; and one or more computing devices configured to: obtain PPG signals, from the sensors, indicative of a cardiac rhythm of the user; provide the PPG signals to one or more remote computing devices; and receive, from the one or more remote computing devices, an indication of a cardiac dysfunction of the user.
17. 17. The wearable computing device of claim 16, further including: electrocardiogram (ECG) sensors configured to obtain ECG data indicative of the cardiac rhythm of the user.
18. 18. The wearable computing device of claim 16, wherein: the one or more computing devices comprise a machine-learned model; and the wearable computing device is configured to determine the cardiac dysfunction of the user via the machine-learned model based, at least in part, on the PPG data of the user.
19. 19. The wearable computing device of claim 16, wherein the one or more computing devices are further configured to: surface, via a user interface of the wearable computing device, a notification to the user indicative of the cardiac dysfunction in response to receiving the indication of the cardiac dysfunction of the user.
20. 20. A computing system for detection of a cardiac dysfunction in a user, the computing system comprising: one or more processors; and one or more non -transitory computer-readable media that collectively store: one or more machine-learned cardiac dysfunction detection models configured to provide cardiac dysfunction predictions based, at least in part, on photoplethysmogram (PPG) recordings; and instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining PPG signals indicative of a cardiac rhythm of the user; processing the PPG signals with the one or more machine-learned cardiac dysfunction detection models to generate a cardiac dysfunction prediction for the user based, at least in part, on the PPG signals; and providing the cardiac dysfunction prediction for the user as an output.

Description

DETECTING LOW EJECTION FRACTION USING PHOTOPLETHYSMOGRAPHY (PPG) PRIORITY CLAIM [0001] The present application is based on and claims priority to United States Application 18/343,465 having a filing date of June 28, 2023, which is incorporated by reference herein. FIELD [0002] Example aspects of the present disclosure generally relate to wearable devices. BACKGROUND [0003] A wearable computing device can be worn, for instance, on a user’s wrist. The wearable computing device can include a plurality of sensors such as, for example biometric sensors. The biometric sensors can obtain data indicative of the user’s physiological state. For instance, such data can include data representative of, for example, a cardiac rhythm of the user. SUMMARY [0004] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments. [0005] In one aspect, a computer-implemented method for detecting a cardiac dysfunction in a user is provided. The method includes obtaining, from a sensor, photoplethysmogram (PPG) signals indicative of a cardiac rhythm of the user. The method further includes processing, in a computing device, the PPG signals to generate a cardiac dysfunction prediction for the user based, at least in part, on the PPG signals. The method further includes providing, via an annunciator, the cardiac dysfunction prediction for the user as an output. [0006] In some implementations, processing the PPG signals to generate a cardiac dysfunction prediction for the user may include processing, via one or more machine-learned models, the PPG signals to generate the cardiac dysfunction prediction for the user. [0007] In some implementations, the method may further include obtaining, via the computing device, electrocardiogram (ECG) signals indicative of the cardiac rhythm of the user. The method may further include processing, via the one or more machine-learned models, the PPG signals and the ECG signals to generate the cardiac dysfunction prediction for the user. Furthermore, in some implementations, the ECG signals may be obtained via a single-lead electrocardiogram. In some implementations, the one or more machine-learned models may be further configured to process demographic data relating to the user to generate the cardiac dysfunction prediction for the user. [0008] In some implementations, the one or more machine-learned models may be trained with PPG data, ECG data, and demographic data. Furthermore, in some implementations, the one or more machine-learned models may include a deep learning model. [0009] In some implementations, the cardiac dysfunction prediction for the user may include one or more predicted probabilities that the user is experiencing the cardiac dysfunction. [0010] In some implementations, obtaining the PPG signals may include obtaining, via one or more biometric sensors of a wearable computing device, PPG signals indicative of the cardiac rhythm of the user. Furthermore, in some implementations, the one or more biometric sensors may include one or more PPG sensors. [0011] In some implementations, obtaining the PPG signals may include obtaining, via one or more pulse oximeters, PPG signals indicative of the cardiac rhythm of the user. [0012] In some implementations, obtaining the PPG signals may include obtaining, via one or more cameras of a mobile computing device, PPG signals indicative of the cardiac rhythm of the user. [0013] In some implementations, the method may further include surfacing, via the computing device, a notification to the user indicative of the cardiac dysfunction prediction via a user interface of a wearable computing device. Furthermore, in some implementations, surfacing a notification to the user indicative of the cardiac dysfunction prediction may further include surfacing, via the computing device, a recommendation to the user to wear an ECG monitor in response to generating the cardiac dysfunction prediction. [0014] In some implementations, the cardiac dysfunction may be left ventricular systolic dysfunction (LVSD). [0015] In another aspect, a wearable computing device is provided. The wearable computing device includes one or more sensors configured to obtain photoplethysmogram (PPG) data of a user wearing the wearable computing device. The wearable computing device further includes one or more computing devices. The one or more computing devices are configured to obtain PPG signals, from the sensors, indicative of a cardiac rhythm of the user. The one or more computing devices are further configured to provide the PPG signals to one or more remote computing devices. The one or more computing devices are further configured to receive, from the one or more remote computing devices, an indication of a cardiac dysfunction of the user. [0016] In some implementations, the wearable computing device may further include electro