Automate Obstructive Sleep Apnea Diagnosis Using Convolutional Neural Networks

TL;DR

This paper presents a 1D CNN model that automatically classifies obstructive sleep apnea severity from PSG data, eliminating manual feature extraction and improving diagnostic efficiency.

Contribution

The study introduces a novel CNN architecture that directly processes raw PSG signals for OSA classification, streamlining the diagnostic process.

Findings

Achieved high classification accuracy on Cleveland Children's Sleep and Health Study data.

Eliminated the need for manual feature extraction and preprocessing.

Demonstrated effectiveness of CNN in sleep disorder diagnosis.

Abstract

Identifying sleep problem severity from overnight polysomnography (PSG) recordings plays an important role in diagnosing and treating sleep disorders such as the Obstructive Sleep Apnea (OSA). This analysis traditionally is done by specialists manually through visual inspections, which can be tedious, time-consuming, and is prone to subjective errors. One of the solutions is to use Convolutional Neural Networks (CNN) where the convolutional and pooling layers behave as feature extractors and some fully-connected (FCN) layers are used for making final predictions for the OSA severity. In this paper, a CNN architecture with 1D convolutional and FCN layers for classification is presented. The PSG data for this project are from the Cleveland Children's Sleep and Health Study database and classification results confirm the effectiveness of the proposed CNN method. The proposed 1D CNN model achieves excellent classification results without manually preprocesssing PSG signals such as feature extraction and feature reduction.