Detection of apnea events from ECG segments using Fourier decomposition method

Abstract

Absence of airflow in breathing during sleep for more than 10 s is known as sleep apnea. It causes low oxygen levels in the blood which may lead to many cardiovascular problems. Current methods of detection are rather time-consuming and expensive. Automated detection using electrocardiogram (ECG) signal is seen as a promising and efficient method for the identification of sleep apnea events. In this paper, the single-lead ECG signal is divided into 1-min segments, and separated into frequency bands using Fourier decomposition method. From these signal components, features like mean absolute deviation and entropy are computed to classify the ECG segments using machine learning algorithms. The proposed method yields an accuracy of 92.59%, sensitivity of 89.70%, specificity of 94.67% and precision of 91.27% on MIT PhysioNet Apnea-ECG dataset, using a support vector machine (SVM) classifier with the Gaussian kernel. The strength of the proposed method has been verified on two more datasets, namely MIT-BIH polysomnography and University College Dublin sleep apnea database (UCDDB). The classification results are compared with the existing state-of-the-art techniques to demonstrate the superior performance of the proposed method. Proposed methodology is implemented using the fast Fourier transform (FFT) which makes it computationally efficient and can be used for real-time sleep apnea detection.