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Comparing ML and DL Sleep Staging Methods with the effect of PCA using EEG Signals

This article studies the usage of Machine Learning (ML) and Deep Learning (DL) for the automated classification of sleep stages. Sleep-EDF Database Expanded and CAP Sleep Database v1.0.0 were used. Electroencephalogram (EEG) signals were preprocessed using bandpass filters to extract α, δ, and θ waves. The dataset included statistical features extracted from 2650 recordings. Three ML models were used including Random Forest (accuracy = 85.2%), k-NN (accuracy = 75.4%) and SVM (accuracy = 36.1%). The models were further explored for improvement using PCA (accuracy = 72.1% with 10 numeric components) and Manual Feature Selection (accuracy = 82% with 7 features). The DL approach was achieved using two types of Neural Networks to create three models. The first was a Convolutional Neural Network (СNN), the second was a Long Short-Term Memory network (LSTM), and the third was a combination of both. The best performance was achieved using CNN (accuracy = 94.1%).