A Sleep Apnea Detection System Based on a One-Dimensional Deep Convolution Neural Network Model Using Single-Lead Electrocardiogram.

Hung-Yu Chang,Chun-Cheng Lin,Cheng-Yu Yeh,Chung-Te Lee

doi:10.3390/s20154157

Abstract

Many works in recent years have been focused on developing a portable and less expensive system for diagnosing patients with obstructive sleep apnea (OSA), instead of using the inconvenient and expensive polysomnography (PSG). This study proposes a sleep apnea detection system based on a one-dimensional (1D) deep convolutional neural network (CNN) model using the single-lead 1D electrocardiogram (ECG) signals. The proposed CNN model consists of 10 identical CNN-based feature extraction layers, a flattened layer, 4 identical classification layers mainly composed of fully connected networks, and a softmax classification layer. Thirty-five released and thirty-five withheld ECG recordings from the MIT PhysioNet Apnea-ECG Database were applied to train the proposed CNN model and validate its accuracy for the detection of the apnea events. The results show that the proposed model achieves 87.9% accuracy, 92.0% specificity, and 81.1% sensitivity for per-minute apnea detection, and 97.1% accuracy, 100% specificity, and 95.7% sensitivity for per-recording classification. The proposed model improves the accuracy of sleep apnea detection in comparison with several feature-engineering-based and feature-learning-based approaches.

Highlights

Obstructive sleep apnea (OSA) is a common sleep disorder that can cause shortness of breath or cessation of breathing, and is characterized by repeated pharyngeal collapse causing partial or complete obstruction of the upper airway, affecting ventilation during sleep [1]
PSG measures the apnea–hypopnea index (AHI), which is defined as the sum of apneas and hypopneas per hour of sleep and has been widely used for diagnosing patients with OSA
This study aims to further simplify the complexity of the signal preprocessing for the design of the convolutional neural network (CNN) model

Summary

Introduction

Obstructive sleep apnea (OSA) is a common sleep disorder that can cause shortness of breath or cessation of breathing, and is characterized by repeated pharyngeal collapse causing partial or complete obstruction of the upper airway, affecting ventilation during sleep [1]. It results in insufficient air entering the lungs and decreased blood oxygen concentration. Polysomnography (PSG) is considered to be the gold standard for diagnosing OSA. According to the AHI, the severity of OSA can be classified as none (AHI < 5), mild (5 ≤ AHI < 15), moderate (15 ≤ AHI < 30), or severe (AHI ≥ 30) [5]

Objectives

Methods

Results

Discussion

Conclusion