| Title |
End-to-end Automatic Sleep Staging Algorithm using Convolution Neural Network and Bidirectional LSTM |
| Authors |
(Jaewoo Baek) ; (Suwan Baek) ; (HyunSu Yu) ; (JungHwan Lee) ; (Cheolsoo Park) |
| DOI |
https://doi.org/10.5573/IEIESPC.2021.10.6.464 |
| Keywords |
Sleep stages; Automatic classification; EEG; 1D-CNN; bi-LSTM |
| Abstract |
In order to measure sleep quality, sleep experts manually classify sleep stages through polysomnography (PSG) signals. However, it is time-consuming and labor-intensive work. Thus, automatic sleep stage classification methods are needed. In this study, we propose an end-to-end automatic sleep staging algorithm using a one-dimensional convolutional neural network (1D-CNN) based on an inception network and bidirectional long short-term memory (bi-LSTM). First, a feature map was extracted from input data using the 1D-CNN architecture without preprocessing. Secondly, bi-LSTM learned a stage transition rule using the feature maps. In addition, we used the sleep-EDF public dataset to evaluate our model, and only one channel of EEG signal was used to save computational cost. The accuracy and macro-averaged F1 score of the classification performance were 85.05% and 79.05%, respectively. These results demonstrate state-of-the-art performance compared to previous studies using the same dataset, yielding an effective method for an automatic sleep staging algorithm. |