Convolutional Neural Networks on Multiple Respiratory Channels to Detect Hypopnea and Obstructive Apnea Events

Abstract

Sleep apnea is a sleep disorder characterized by abnormal breathing patterns during sleep, and affecting 2-4% of the adult population. If left untreated, it increases the risk of heart attack, stroke, diabetes, depression and early death. There are two main types of abnormal breathing events: obstructive apnea and hypopnea. Detecting these events using the traditional machine learning approaches requires extraction and selection of suitable features from several respiratory channels, that are then used as inputs to a classification model. In this study, we present a new approach, based on convolutional neural networks, that automatically combines the raw data of three respiratory channels of polysomnography recordings (nasal airflow, thoracic and abdominal), without feature engineering, and classifies each 30s epoch as containing normal, obstructive apnea or hypopnea events. The evaluation was conducted on a large dataset from 1,507 subjects, containing 23,088 epochs from each of the three classes. We also studied the effectiveness of the individual channels for improving the classification accuracy by testing all channel combinations. Our results showed that the use of nasal airflow, thoracic and abdominal channels with a convolutional neural network was beneficial in detecting sleep apnea events. The combined use of the three channels outperformed all single and pair combinations of channels, achieving accuracy of 83.5%, which is sufficiently high for practical applications.