Detection of Deep Sleep Stages in Multi-Channel EEG Signals Based on Spectral Feature Quantification Methods - Liu, Jinsha; Shen, Chengyuan; Zhao, Liangyu; Maeda, Taiyo; Cao, Jianting (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2025)
Deep sleep is essential for physical recovery and cognitive function. Accurate detection is crucial for evaluating sleep quality and diagnosing related disorders. Electroencephalography (EEG) remains the most reliable tool for assessing deep sleep. However, while deep learning methods have shown high performance, they often suffer from limited interpretability and require substantial computational resources, which can hinder real-time clinical applications. This study proposes a novel multi-channel EEG analysis approach combining Turning Tangent Empirical Mode Decomposition (2T-EMD) and Multi-Taper Power Spectral Density (MT-PSD) to extract physiologically meaningful spectral features, followed by classification using a Random Forest (RF) model. The method was validated on 61 recordings from 41 participants, achieving 95.57% accuracy, 97.35% recall, 97.58% F1-score, and 99.00% AUC. Compared with existing approaches, our method demonstrates (1) physiologically interpretable feature extraction; (2) favorable computational efficiency under our experimental setup; and (3) robust generalizability across different demographics, indicating its potential utility in clinical sleep monitoring scenarios.