Kernel Dimensionality Reduction on Sleep Stage Classification using ECG Signal
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.
Keywords: dimensionality reduction, KDR, polysomnography, ECG, sleep stage
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