Automatic Classification of Sleep Stages Using EEG Records.
Mowafy, M., Obayya, M., Abou-Chadi, F., saad, M. (2020). Automatic Classification of Sleep Stages Using EEG Records.. EKB Journal Management System, 38(3), 1-8. doi: 10.21608/bfemu.2020.106742
Mohamed Mowafy; Marwa Obayya; F. E. Z. Abou-Chadi; Mohamed saad. "Automatic Classification of Sleep Stages Using EEG Records.". EKB Journal Management System, 38, 3, 2020, 1-8. doi: 10.21608/bfemu.2020.106742
Mowafy, M., Obayya, M., Abou-Chadi, F., saad, M. (2020). 'Automatic Classification of Sleep Stages Using EEG Records.', EKB Journal Management System, 38(3), pp. 1-8. doi: 10.21608/bfemu.2020.106742
Mowafy, M., Obayya, M., Abou-Chadi, F., saad, M. Automatic Classification of Sleep Stages Using EEG Records.. EKB Journal Management System, 2020; 38(3): 1-8. doi: 10.21608/bfemu.2020.106742

Automatic Classification of Sleep Stages Using EEG Records.

MEJ- Mansoura Engineering Journal
Article 8, Volume 38, Issue 3, September 2013, Page 1-8  XML PDF (299.54 K)
Document Type: Research Studies
DOI: 10.21608/bfemu.2020.106742
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Authors
Mohamed Mowafy email 1; Marwa Obayya2; F. E. Z. Abou-Chadi3; Mohamed saad4
1Prof. of Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University
2Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University.
3Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University
4Head of Dept. Neurology, Faculty of Medicine, Mansoura University
Abstract
Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG, EOG are used in sleep labs among other for diagnosis and treatment of sleep relayed disorders. The usual method for sleep stages classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stages classification can facilitate this process. In this work an attempt was made to classify six sleep stages consisting of Awake, Stage 1, Stage 2, Stage3, Stage 4, and REMS. Spectral analysis, Wavelet transform and artificial neural networks were deployed for this purpose. Twenty-four recordings of a healthy six stages studied per 30s epochs. The results demonstrated that the performance for automatically discriminated for these six sleep stages from each other when using wavelet packet with Sym3 where the classification was with average 81.94%. Data fusion improves the accuracy of classification results using fusion at the feature extraction level to 87.7%.
Keywords
Artificial Neural Networks; Sleep Analysis; Electroencephalogram (EEG); Data fusion
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