A CNN-BiLSTM Deep Learning Model for Automatic Scoring of EEG Signals

Elmoaqet H, Eid M, Ryalat M, Penzel T (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Current Directions in Biomedical Engineering De Gruyter

Book Volume: 9

Pages Range: 642-645

Journal Issue: 1

DOI: 10.1515/cdbme-2023-1161

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.

Involved external institutions

Charité - Universitätsmedizin Berlin DE Germany (DE) German-Jordanian University (GJU) / الجامعة الألمانيه الأردنية JO Jordan (JO)

How to cite

APA:

Elmoaqet, H., Eid, M., Ryalat, M., & Penzel, T. (2023). A CNN-BiLSTM Deep Learning Model for Automatic Scoring of EEG Signals. Current Directions in Biomedical Engineering, 9(1), 642-645. https://dx.doi.org/10.1515/cdbme-2023-1161

MLA:

Elmoaqet, Hisham, et al. "A CNN-BiLSTM Deep Learning Model for Automatic Scoring of EEG Signals." Current Directions in Biomedical Engineering 9.1 (2023): 642-645.

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