Sleep Apnea Prediction Using Electrocardiograms and Machine Learning

Smart overview of the Invention

Sleep apnea prediction leverages electrocardiograms (ECGs) and machine learning to detect and classify sleep apnea using wearable technology. A monitoring device captures electrical potential measurements of the heart over several days. These measurements are analyzed by machine learning models trained on historical data to predict sleep apnea classifications, which can then be communicated via health reports or notifications.

Background

Sleep apnea is a disorder marked by repeated breathing interruptions during sleep, classified as apneas or hypopneas. Diagnosing it is challenging due to its nocturnal nature and symptoms that overlap with other conditions. Traditional diagnostic methods like polysomnography are cumbersome and expensive, while home tests often lack the detail needed for accurate diagnosis. This innovation aims to overcome these limitations by providing an accessible and comprehensive diagnostic tool.

Detailed Description

The system involves a wearable device, such as a patch, that collects ECG data continuously over multiple days. This data is processed by machine learning models that detect patterns indicative of sleep apnea. The wearable device is designed to maintain consistent skin contact, ensuring reliable data capture. Additional sensors may collect accelerometer data to identify sleep periods and SpO2 data to further validate apnea events.

Data Processing

The prediction system processes collected physiological data to generate sleep apnea classifications. These classifications can determine the presence and type of sleep apnea, its severity, and potential health impacts like fatigue or cardiac issues. The machine learning model is trained on historical ECG data and clinical outcomes, enabling it to recognize patterns linked to various sleep apnea classifications.

Output and Advantages

The system outputs sleep apnea classifications in real-time or at the end of the observation period, providing insights into the user's sleep patterns and health status. This approach offers early detection of sleep apnea, allowing for timely intervention and prevention of adverse health effects. By utilizing wearable technology and machine learning, the system provides a more accessible and detailed analysis compared to conventional methods.