ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2022
A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2022. 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.
- Neural network architectures and normalization techniques for automated sleep stage classification using rodent EEG and EMG signals.PloS one · 2026Article
- Automated sleep staging from single-channel electroencephalogram using hybrid neural network with manual features and attention.iScience · 2025Article
- Machine learning classifier solving the problem of sleep stage imbalance between overnight sleep.Biomedical engineering letters · 2025Article
- Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors.
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Abstract
backgroundSleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection.
objectiveThis paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory.
methodsThe algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 ∼ N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input into the deep learning algorithm to obtain the staging result.
resultsThe accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%.
conclusionThese results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction.
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