EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning
This addresses sleep disorder diagnosis by providing an accurate and interpretable automated scoring method, though it is incremental as it builds on existing wavelet and deep learning techniques.
The study tackled automated sleep stage classification from EEG signals using a wavelet-based time-frequency analysis and ensemble learning, achieving 88.37% accuracy and a 73.15 F1 score, outperforming conventional methods and matching or exceeding recent deep learning approaches.
Accurate classification of sleep stages is crucial for the diagnosis and management of sleep disorders. Conventional approaches for sleep scoring rely on manual annotation or features extracted from EEG signals in the time or frequency domain. This study proposes a novel framework for automated sleep stage scoring using time-frequency analysis based on the wavelet transform. The Sleep-EDF Expanded Database (sleep-cassette recordings) was used for evaluation. The continuous wavelet transform (CWT) generated time-frequency maps that capture both transient and oscillatory patterns across frequency bands relevant to sleep staging. Experimental results demonstrate that the proposed wavelet-based representation, combined with ensemble learning, achieves an overall accuracy of 88.37 percent and a macro-averaged F1 score of 73.15, outperforming conventional machine learning methods and exhibiting comparable or superior performance to recent deep learning approaches. These findings highlight the potential of wavelet analysis for robust, interpretable, and clinically applicable sleep stage classification.