Automatic Sleep Staging of Single-Channel Ear-EEG Signals with a Probabilistic Ensemble Learning Approach.

Accurate sleep staging is crucial for the early diagnosis of neurodegenerative diseases and the management of sleep disorders. To provide a user-friendly, non-intrusive, and long-term monitoring solution, we explored the potential clinical applications of ear-electroencephalogram (ear-EEG). This study proposes a probabilistic ensemble learning approach for automatic sleep staging using single-channel ear-EEG data. The proposed method integrates Extreme Gradient Boosting (XGBoost) with Linear Discriminant Analysis (LDA), augmented by transition matrix correction and probability weighting strategies, to capture temporal sleep patterns without compromising data integrity or requiring intensive preprocessing. An ear-EEG with polysomnography (ear-PSG) dataset collected from twenty subjects using our custom-developed ear-EEG sensor, was compared with two public datasets, ear-Feature and Sleep-EDF, to validate both the reliability of the data and the effectiveness of the proposed approach. The results indicate that transition matrix correction is particularly effective when training and testing are conducted using single-epoch inputs, whereas model weighting demonstrates greater stability as the number of epochs increases. When using seven-epochs input sequences, leave-one-subject-out (LOSO) cross-validation achieved 0.814 accuracy with 0.749 kappa coefficient on ear-PSG (earL-R), and 0.841 accuracy with 0.779 kappa coefficient on the ear-Feature dataset. The design of a single-channel cross-ear intra-auricular ear-EEG configuration, combined with an ensemble learning framework, effectively balances device portability and classification performance, offering new insights for the clinical translation of wearable sleep monitoring technology and laying a foundation for the development of portable sleep monitoring devices.