کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6950892 | 1451638 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A convolutional neural network for sleep stage scoring from raw single-channel EEG
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
We present a novel method for automatic sleep scoring based on single-channel EEG. We introduce the use of a deep convolutional neural network (CNN) on raw EEG samples for supervised learning of 5-class sleep stage prediction. The network has 14 layers, takes as input the 30-s epoch to be classified as well as two preceding epochs and one following epoch for temporal context, and requires no signal preprocessing or feature extraction phase. We train and evaluate our system using data from the Sleep Heart Health Study (SHHS), a large multi-center cohort study including expert-rated polysomnographic records. Performance metrics reach the state of the art, with accuracy of 0.87 and Cohen kappa of 0.81. The use of a large cohort with multiple expert raters guarantees good generalization. Finally, we present a method for visualizing class-wise patterns learned by the network.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 42, April 2018, Pages 107-114
Journal: Biomedical Signal Processing and Control - Volume 42, April 2018, Pages 107-114
نویسندگان
Arnaud Sors, Stéphane Bonnet, Sébastien Mirek, Laurent Vercueil, Jean-François Payen,