A CNN-BiLSTM Deep Learning Model for Automatic Scoring of EEG Signals
Abstract: Recently, several automatic sleep stage classification methods have been proposed based on deep learning using convolutional (CNN) and recurrent (RNN) neural networks. However, the state of the art CNN methods are still complex which usually require significant time and considerable computational resources in order to set up and sufficiently train a deep CNN from scratch. This study eliminates the need to establish and train a deep CNN from scratch by leveraging a pre-trained deep architecture that has been previously trained from sufficient labeled data in a different context. A convolutional neural network (CNN) and a Bidrectional long short term memory network (BiLSTM) are integrated for automatic feature extraction and sleep stage scoring using only a singlechannel EEG signal. To demonstrate the generalizability of our results, the proposed model was evaluated using PSG records of 81 patients that were collected in different environments, through different recording hardware, and annotated with different sleep experts. The use of a single EEG source and a one-to-one classification scheme in the proposed model can allow further development towards wearable systems and online in home monitoring applications.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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A CNN-BiLSTM Deep Learning Model for Automatic Scoring of EEG Signals ; volume:9 ; number:1 ; year:2023 ; pages:642-645 ; extent:4
Current directions in biomedical engineering ; 9, Heft 1 (2023), 642-645 (gesamt 4)
- Creator
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ElMoaqet, Hisham
Eid, Mohammad
Ryalat, Mutaz
Penzel, Thomas
- DOI
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10.1515/cdbme-2023-1161
- URN
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urn:nbn:de:101:1-2023092214102442803078
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:49 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- ElMoaqet, Hisham
- Eid, Mohammad
- Ryalat, Mutaz
- Penzel, Thomas
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