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GooglePatent applicationHealth & bodyInterest 7/10

Google patents heart risk check using light sensors and age

Google describes a system that uses a light-based sensor to measure blood flow pulses and combine them with a person's age and sex to estimate the risk of heart disease. The process uses a computer program to simplify the sensor data into a few key numbers that predict health risks without needing blood tests or expensive equipment.

Why it mattersThis could make heart health checks cheaper and easier to access in places without hospitals or labs.

In the filing’s own words

Accurate prediction of the risk of cardiovascular disease (CVD) can lead to significantly improved health outcomes by prompting earlier, more effective, and lower-cost interventions (like lifestyle and diet changes). Available long-term predictive tests for CVD risk include costly, clinically intensive diagnostics that include the efforts of highly trained clinical or laboratory staff like blood or other fluid draws and related laboratory testing, ECG, sphygmomanometer-based blood pressure measurement, or other tests. Such testing is not available in resource-constrained healthcare settings. Embodiments are provided that use a trained machine learning model to generate, from photoplethysmographic (PPG) signals, features that can be used, in combination with readily available information like age and sex and PPG-derived heart rate, an estimate of CVD risk. PPG sensors are readily available, low-cost, and are able to be used without training, allowing the embodiments herein to be used to predict CVD risk even in low-resource healthcare systems.

What exactly they claim (claim 1)

1. A method for training a composite model to predict cardiovascular disease risk, the method comprising: training a machine learning model of the composite model to output, based on an input photoplethysmographic waveform, a first set of features that are representative of the photoplethysmographic waveform; determining, based on the photoplethysmographic waveform, a heart rate; determining, based on the first set of features, a mapping of the composite model to project the first set of features to a second set of features, wherein the second set of features includes fewer features than the first set of features; determining a set of model coefficients of the composite model to predict, based on the second set of features, the heart rate, and a set of demographic information, a cardiovascular disease risk score; and outputting an indication of the machine learning model, mapping, and set of model coefficients of the composite model.

Facts

Company
Google (GOOGLE LLC)
Official title
DEEP LEARNING-BASED PHOTOPLETHYSMOGRAPHY MODEL FOR CARDIOVASCULAR RISK PREDICTION
Publication number
US20260283560A1
Application number
19477638
Filed
Apr 23, 2024
Published
Sep 24, 2026
Inventors
7
Claims
25

Classified under

A61B 5/7267 Human necessitiesA61B 5/02416 Human necessitiesA61B 5/7275 Human necessities
Read the full filing (official PDF) ↗ Open in the Patent Tracker

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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.