Sleep Apnea Detection Based on ECG Signals Using Discrete Wavelet Transform and Artificial Neural Network

Abstract

Sleep apnea is a sleep disorder that can cause serious health problems. An Artificial Neural Network classifier to detect sleep apnea has been presented in this paper by utilizing the ECG signals. Moreover, the discrete wavelet transform is used to decompose the ECG signal and use the first decomposition for feature extraction; the extracted features were used to train the Artificial Neural Network for pattern detection using MATLAB tools. Also, the data-sets used contains both Apnea pat1ients and healthy volunteers’ ECG signals. The results achieve 92.3% accuracy in the testing records.