A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging
Sleep stages are determined firstly for the evaluation of sleep quality and the diagnosis of sleep diseases. The signals, recorded from sensors connected to various parts of the body, such as electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are use...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Sakarya University
2020-08-01
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| Series: | Sakarya University Journal of Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1252883 |
| _version_ | 1797351834629701632 |
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| author | Goksu Özen Rayımbek Sultanov Yunus Özen Zahide Yılmaz Güneş |
| author_facet | Goksu Özen Rayımbek Sultanov Yunus Özen Zahide Yılmaz Güneş |
| author_sort | Goksu Özen |
| collection | DOAJ |
| description | Sleep stages are determined firstly for the evaluation of sleep quality and the diagnosis of sleep diseases. The signals, recorded from sensors connected to various parts of the body, such as electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are used for this purpose. After the production of affordable wearable EEG devices for individual use, studies have begun to detect sleep stages from a single channel EEG signal. This paper presents an automated system that can perform sleep staging using a single-channel raw EEG signal. A Convolutional Neural Network (CNN) model was trained with the raw EEG signal for sleep stage detection. The use of CNN does not require any feature extraction. The developed CNN model classifies the sleep data sampled at 250 Hz, divided into 30-second segments according to the 5-class sleep staging system. According to the test results, the performance of the proposed system was found to be 93% macro F1 score and 92% accuracy. |
| first_indexed | 2024-03-08T13:06:14Z |
| format | Article |
| id | doaj.art-7de7989191804f9c9341006d8797d3d2 |
| institution | Directory Open Access Journal |
| issn | 2636-8129 |
| language | English |
| last_indexed | 2024-03-08T13:06:14Z |
| publishDate | 2020-08-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj.art-7de7989191804f9c9341006d8797d3d22024-01-18T16:44:35ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292020-08-013214915810.35377/saucis.03.02.73162828A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep StagingGoksu Özen0Rayımbek Sultanov1Yunus Özen2Zahide Yılmaz Güneş3KYRGYZ - TURKISH MANAS UNIVERSITYKYRGYZ - TURKISH MANAS UNIVERSITYYALOVA UNIVERSITYDerince Education and Research Hospital, Clinic of NeurologySleep stages are determined firstly for the evaluation of sleep quality and the diagnosis of sleep diseases. The signals, recorded from sensors connected to various parts of the body, such as electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are used for this purpose. After the production of affordable wearable EEG devices for individual use, studies have begun to detect sleep stages from a single channel EEG signal. This paper presents an automated system that can perform sleep staging using a single-channel raw EEG signal. A Convolutional Neural Network (CNN) model was trained with the raw EEG signal for sleep stage detection. The use of CNN does not require any feature extraction. The developed CNN model classifies the sleep data sampled at 250 Hz, divided into 30-second segments according to the 5-class sleep staging system. According to the test results, the performance of the proposed system was found to be 93% macro F1 score and 92% accuracy.https://dergipark.org.tr/tr/download/article-file/1252883convolutional neural networkeegsleep scoringeegevrişimsel sinir ağıuyku evrelemesi |
| spellingShingle | Goksu Özen Rayımbek Sultanov Yunus Özen Zahide Yılmaz Güneş A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging Sakarya University Journal of Computer and Information Sciences convolutional neural network eeg sleep scoring eeg evrişimsel sinir ağı uyku evrelemesi |
| title | A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging |
| title_full | A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging |
| title_fullStr | A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging |
| title_full_unstemmed | A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging |
| title_short | A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging |
| title_sort | convolutional neural network based on raw single channel eeg for automatic sleep staging |
| topic | convolutional neural network eeg sleep scoring eeg evrişimsel sinir ağı uyku evrelemesi |
| url | https://dergipark.org.tr/tr/download/article-file/1252883 |
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