EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning

Accurate classification of sleep stages is crucial for the diagnosis and management of sleep disorders. Conve ntional 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 wavele t 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 s leep staging. Experimental results demonstrate that the proposed wavelet - based representation, combined with ensemble learning, achieves an overall accuracy of 88.37% and a macro - averaged F1 score of 73.15%, outperforming conventional machine learning meth ods and exhibiting comparable or superior performance to recent deep learning approaches. ABSTRACT MUST Journal of Research and Development (MJRD) Volume 6 Issue 3, September 2025 e ISSN 2683 - 6467 & p ISSN 2683 - 6475 429 1.0 Introduction Sleep is a vital physiological process essential for memory consolidation, learning, and overall brain health. Sleep disruptions are strongly associated with a wide range of neurological and psychiatric conditions, including epilepsy, Alzheimer's disease, depression, and t raumatic brain injury (Kang et al .