ArticleDiagnostics (Basel, Switzerland)2023
Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- EEG Signal Classification with Data Augmentation for Epileptic Focus Localization and Deep Sleep Detection.Sensors (Basel, Switzerland) · 2026Article
- Temporally Continuous Automated Sleep-Wake Classification Using Deep Learning.medRxiv : the preprint server for health sciences · 2025Article
- Bimodal Transformer with Regional EEG Data for Accurate Gameplay Regularity Classification.Brain sciences · 2024Article
- Enhancing generalized anxiety disorder diagnosis precision: MSTCNN model utilizing high-frequency EEG signals.Frontiers in psychiatry · 2023Article
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2 authors.
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Abstract
Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data. Manual sleep staging requires human expertise, which is occasionally prone to error and variation. In recent times, availability of polysomnography data has aided progress in automatic sleep-stage classification. In this paper, a hybrid deep learning model is proposed for classifying sleep and wake states based on a single-channel electroencephalogram (EEG) signal. The model combines an artificial neural network (ANN) and a convolutional neural network (CNN) trained using mixed-input features. The ANN makes use of statistical features calculated from EEG epochs, and the CNN operates on Hilbert spectrum images generated during each epoch. The proposed method is assessed using single-channel Pz-Oz EEG signals from the Sleep-EDF database Expanded. The classification performance on four randomly selected individuals shows that the proposed model can achieve accuracy of around 96% in classifying between sleep and wake states from EEG recordings.
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