Sleep spindles and K-complexes in electroencephalo gram (EEG) signals are brief yet physiologically essential signatures of the sleeping brain, supporting memory consolidation, arousal regulation, and neurological assessment. However, existing detection methods often face severe class imbalance, temporal inconsistencies, and uncertain event boundaries. We proposed a unified, physiologically informed framework that integrates data preprocessing, multimodal feature extraction, temporal modeling, and event-level refinement. Context-aware negative filtering and segment-level oversampling effectively alleviated severe class imbalance; multimodal feature combination captured both spectral and morphological cues; a Bidirectional Long Short-Term Memory (BiLSTM) backbone enhanced by segment level dropout and bidirectional hard example mining improves robustness under imbalance; and a physiologically plausible post-processing enforced temporal coherence on the predicted sequences. Evaluations on the publicly available polysomnog raphy datasets DREAMS and MASS demonstrated consistent 3-10% F1-score improvements over state-of-the-art baselines. The proposed framework attained event-level F1-scores of 0.796 (sleep spindles on DREAMS), 0.938 (K-complexes on DREAMS), 0.882 (sleep spindles on MASS), and 0.903 (K-complexes on MASS), substantially outperforming recent deep-learning-based detectors.
Hu et al. (Thu,) studied this question.