Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach

TL;DR

This paper presents a deep learning method for classifying obstructive sleep apnea severity using only 15-second single-channel ECG data, enabling easier, faster, and potentially real-time detection suitable for wearable devices.

Contribution

The study introduces a novel deep learning approach for OSA severity classification from single-channel ECG, eliminating the need for complex multi-signal analysis and domain-specific feature extraction.

Findings

Achieved 79.45% accuracy in classifying OSA severity.

Outperformed traditional SVM classifiers using RR intervals and ECG-derived respiration signals.

Demonstrated potential for real-time, at-home OSA severity detection with wearable devices.

Abstract

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI $<$ 5) and severe OSA patients (AHI $>$ 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of deep learning models. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the k-fold cross-validation technique. The accuracy of 79.45\% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near real-time alerting systems such as before SCD occurs.