این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Frontiers in Biomedical Technologies، جلد ۷، شماره ۳، صفحات ۱۴۲-۱۵۰
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Automatic Sleep Stage Classification Using 1D Convolutional Neural Network
چکیده انگلیسی مقاله Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleep-related diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using single-channel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-learn the discriminative features from the EEG signal. Results: Applying the proposed method to sleep-EDF dataset resulted in overall accuracy, sensitivity, specificity, and Precision of 94.09%, 74.73%, 96.43%, and 71.02%, respectively, for classifying five sleep stages. Using single-channel EEG and providing a network with fewer trainable parameters than most of the available deep learning-based methods are the main advantages of the proposed method. Conclusion: In this study, a 13-layer 1D CNN model was proposed for sleep stage classification. This model has an end-to-end complete architecture and does not require any separate feature extraction/selection and classification stages. Having a low number of network parameters and layers while still having high classification accuracy, is the main advantage of the proposed method over most of the previous deep learning-based approaches.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله | Asma Salamatian
Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.


| Ali Khadem
Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
نشانی اینترنتی https://fbt.tums.ac.ir/index.php/fbt/article/view/266
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات