Abstract
The present application relates to a method, algorithm, system, and platform for a common heart and sleep condition called sleep apnea. The continuous heart rate data of users are collected through wearable sensors, such as smart bracelets, armbands, chest straps, microphones, or other methods. The collected heart rate data are pre-processed, validated, and analyzed using new algorithms including the analysis of density maps and machine learning algorithms. This application provides personalized analysis of the data during long-term sleep and ensures the high accuracy of data analysis. Sleep apnea monitoring results enable screening for sleep apnea, detection of sleep apnea, and determination of severity.
| Translated title of the contribution | A Method for Detecting Sleep Apnea by Machine Learning with Time-series Heart Rate Data from Consumer Wearable Sensors |
|---|---|
| Original language | Chinese (Simplified) |
| Patent number | CN202310117331.2 |
| Publication status | Submitted - 15 Feb 2023 |