Kernel Dimensionality Reduction on Sleep Stage Classification using ECG Signal

Abstract

The purpose of this study is to apply Kernel Dimensionality Reduction (KDR) to classify sleep stage from electrocardiogram (ECG) signal. KDR is supervised dimensionality reduction method that retains statistical relationship between input variables and target class. KDR was chosen to reduce dimensionality of features extracted from ECG signal because this method doesn’t need special assumptions regarding the conditional distribution, the marginal distribution, or both. In this study we extract 9 time and frequency domain heart rate variability (HRV) features from ECG signal of Polysomnographic Database from Physionet. To evaluate KDR performance, we perform sleep stage classification using kNN, Random Forest and SVM method, and then compare the classification performance before and after dimensionality reduction using KDR. Experimental result suggested KDR implementation on sleep stage classification using SVM could reduce dimensionality of feature vector into 2 without affecting the classification performance. KDR performance on Random Forest and k Nearest Neighbour classification only show slight advantage compared to without implementing KDR.