A Low Complex Algorithm for Detection of Sleep Apnea from ECG Signals using Sliding SSA Combined with PCA, KPCA, and SPPCA

Abstract

Sleep apnea is a potentially life-threatening sleep condition in which breathing stops and resumes repeatedly. It is caused by breathing pauses during sleep, which leads to frequent awakenings. As we all know, computational time and efficiency are essential in the healthcare industry; to address this issue, we proposed an algorithm that performs more computations in less time without compromising the machine learning model’s performance. This study employs a unique technique called Sliding Singular Spectrum Analysis (SSSA) to decompose and de-noise the ECG signals. To identify the significant apnea and non-apnea components from the pre-processed ECG data and to reduce the dimensionality, we used Principal Component Analysis (PCA), Kernal PCA (KPCA), and Sub-Pattern based PCA (SPPCA). These characteristics were then used to train and evaluate various machine learning models, including KNN, SVM, GaussianNB, SGD, and XGBoost, to distinguish between apnea and non-apnea ECG data. The publicly available Physionet Apnea-ECG database is used for the simulation of the proposed algorithm. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.