Intarawichian S.Thiennviboon P.Laothamatas J.Sungkarat W.Mahidol University2026-02-062026-02-062026-01-01IEEE Access (2026)https://repository.li.mahidol.ac.th/handle/123456789/114375Sleep disorders can significantly affect health, safety, and quality of life, making sleep stage classification essential for diagnosis and treatment. While deep learning-based automated classification has shown promise, multi-class classification with a single convolutional neural network (CNN) can be overly complex and prone to overfitting. Therefore, we propose a novel architecture, EnsembleNet, using a single electroencephalogram (EEG) channel. Our approach consists of an ensemble of per-epoch CNN-based binary classifiers for feature extraction, followed by an ensemble of per-sequence bidirectional long short-term memory (BiLSTM) models to capture temporal dependencies. A shared, relatively simple architecture for binary classifiers helps mitigate overfitting, while a two-step training approach was chosen to reduce computational workload by separately training CNN-based binary classifiers and BiLSTM models. Model performances were evaluated using k-fold cross validation on Sleep-EDF-20 (k = 20) and Sleep-EDF-78 (k = 10) datasets where an EEG signal is either Fpz-Cz or Pz-Oz channel. On Sleep-EDF-20, the model achieved F1-scores of 86.94% − 90.16% and 83.16% − 89.50% (non-N1), and 55.32% and 46.14% (N1) with overall accuracies of 87.07% and 85.18%, for the Fpz-Cz and Pz-Oz channels, respectively. Similarly, on Sleep-EDF-78, F1-scores reached 83.35% − 94.06% and 71.94% − 92.15% (non-N1), and 53.17% and 48.51% (N1) with overall accuracies of 85.95% and 82.86%, for the Fpz-Cz and Pz-Oz channels, respectively. Our model outperforms selected state-of-the-art methods, offering an efficient alternative to complex multi-class CNN architectures. Additionally, the ensemble of binary classifiers enhances explainability and facilitates performance optimization through systematic assessment.Materials ScienceComputer ScienceEngineeringArticleSCOPUS10.1109/ACCESS.2026.36558112-s2.0-10502821937121693536