A Residual Based Attention Model for EEG Based Sleep Staging
| Autor: | David Dagan Feng, Ronald R. Grunstein, Hong Hong, Zhiyong Wang, Christopher J. Gordon, Zheru Chi, Wei Qu |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male medicine.medical_specialty Databases Factual Computer science 0206 medical engineering 02 engineering and technology Electroencephalography Machine learning computer.software_genre Convolutional neural network Sleep medicine 03 medical and health sciences Young Adult 0302 clinical medicine Deep Learning Health Information Management medicine Humans Electrical and Electronic Engineering Aged Context model Sleep Stages Artificial neural network medicine.diagnostic_test business.industry Deep learning Signal Processing Computer-Assisted Middle Aged 020601 biomedical engineering Computer Science Applications Recurrent neural network Female Artificial intelligence Neural Networks Computer business computer 030217 neurology & neurosurgery Biotechnology |
| Zdroj: | IEEE journal of biomedical and health informatics. 24(10) |
| ISSN: | 2168-2208 |
| Popis: | Sleep staging is to score the sleep state of a subject into different sleep stages such as Wake and Rapid Eye Movement (REM) . It plays an indispensable role in the diagnosis and treatment of sleep disorders. As manual sleep staging through well-trained sleep experts is time consuming, tedious, and subjective, many automatic methods have been developed for accurate, efficient, and objective sleep staging. Recently, deep learning based methods have been successfully proposed for electroencephalogram (EEG) based sleep staging with promising results. However, most of these methods directly take EEG raw signals as input of convolutional neural networks (CNNs) without considering the domain knowledge of EEG staging. Apart from that, to capture temporal information, most of the existing methods utilize recurrent neural networks such as LSTM (Long Short Term Memory) which are not effective for modelling global temporal context and difficult to train. Therefore, inspired by the clinical guidelines of sleep staging such as AASM (American Academy of Sleep Medicine) rules where different stages are generally characterized by EEG waveforms of various frequencies, we propose a multi-scale deep architecture by decomposing an EEG signal into different frequency bands as input to CNNs. To model global temporal context, we utilize the multi-head self-attention module of the transformer model to not only improve performance, but also shorten the training time. In addition, we choose residual based architecture which makes training end-to-end. Experimental results on two widely used sleep staging datasets, Montreal Archive of Sleep Studies (MASS) and sleep-EDF datasets, demonstrate the effectiveness and significant efficiency (up to 12 times less training time) of our proposed method over the state-of-the-art. |
| Databáze: | OpenAIRE |
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