Sleep Stage Classification Using EEG Signals: A Empirical Wavelet Transform-Based Decomposition Approach

The objective of signal processing using transformation techniques, is to accurately detect the frequency decomposition and extract small variations within the signals. Previous studies have explored various algorithms aiming to decompose signals and classify sleep stages based on EEG signals, offering valuable insights. However, there is still scope for improvement in handling highly non-stationary signals across all sleep stages. The article proposes employing spectrum partitioning from six sleep phases and the Empirical Wavelet Transform (EWT) to properly decompose extremely non-stationary EEG data. This approach uses an adaptive filter bank to control dynamic data in the frequency domain to accurately distinguish sleep stages. Our experiments demonstrate that, the Support Vector Machines (SVM) classifier outperforms Random Forest (RF) and k-Nearest Neighbors (KNN) classifiers, and it achieves notable classification accuracy for all sleep stages. This method enhances the performance of medical devices and benefits for their use in healthcare.