[1]LI Qianyu,WANG Bei,JIN Jing,et al.Automatic sleep staging model based on the bi-directional LSTM convolutional network and attention mechanism[J].CAAI Transactions on Intelligent Systems,2022,17(3):523-530.[doi:10.11992/tis.202103013]
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Automatic sleep staging model based on the bi-directional LSTM convolutional network and attention mechanism
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 3
Page number:
523-530
Column:
学术论文—机器感知与模式识别
Public date:
2022-05-05
- Title:
- Automatic sleep staging model based on the bi-directional LSTM convolutional network and attention mechanism
- Keywords:
- sleep staging; electroencephalogram; convolutional neural network; residual network; Bi-directional long short-term memory network; attention mechanism; class imbalance; oversampling
- CLC:
- TP391.4
- DOI:
- 10.11992/tis.202103013
- Abstract:
- Owing to the problems of gradient disappearance and the weak learning ability of time series information in the current deep sleep staging model, we propose an automatic sleep staging model based on the bi-directional long short-tern memory convolutional network and attention mechanism. After the sleep EEG data of minority classes are enhanced by oversampling, a convolutional neural network with a residual block is designed to learn the data feature representation, and then, an attention layer is combined with the BiLSTM network to extract the deep time sequence information, a softmax layer is adopted to realize the automatic discrimination of sleep stages. A total of 19-night single-channel EEG signals from the Sleep-EDF dataset are analyzed to cross-verify the proposed model. The obtained classification accuracy and macro-F1-score (MF1) are more satisfied than the comparison methods. The effect of low classification performance of minority classes in sleep staging is reduced effectively. The overall classification performance by the proposed deep sleep staging model is sufficiently improved.
- References:
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1900-01-01