Sleep Staging with Gradient Boosting and DWT-PSD Features from EEG/EOG Signals

Advances in machine learning (ML) and deep learning (DL) have led to automated sleep staging approaches that achieve high accuracy but often require extensive computational resources and/or highdensity electroencephalograms (EEG).This paper presents a method for sleep staging using features extracted via the Discrete Wavelet Transform (DWT) and Power Spectral Density (PSD), followed by the Gradient Boosting (GB) classifier.The study employs a private dataset and the sleep-EDF dataset, comprising EEG and electrooculograms (EOG) channels.The analysis includes configurations with varying numbers of subjects (75, 20, and 12), and the results demonstrate that the proposed method achieves competitive performance with existing approaches that use complex DL architectures, even with fewer subjects.Feature importance analysis highlights the importance of detail coefficients from DWT and PSD-based features from EEG signals.The findings suggest that simplified methods using low-density EEG and EOG with wellselected features and GB classification can offer a viable alternative to DL approaches for sleep staging.