EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning

Abstract

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 to evaluate the method. The continuous wavelet transform (CWT) was used to calculate a time-frequency map, which captures both transient and oscillatory patterns across different frequency bands relevant to sleep staging. Experimental results demonstrate that the proposed wavelet-based representation enhances classification accuracy compared to traditional approaches, underscoring the potential of wavelet analysis for robust and interpretable sleep state scoring.