Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features

作者：

Highlights：

• Our model is based on bidirectional LSTM, which can be trained to learn the sleep stage transition rules.

• The size of the parameter of our model is much smaller than the SOTA model (DeepSleepNet), but the performance is similar.

• The overall accuracy increases with the help of fractional Fourier transform-domain features.

摘要

•Our model is based on bidirectional LSTM, which can be trained to learn the sleep stage transition rules.•The size of the parameter of our model is much smaller than the SOTA model (DeepSleepNet), but the performance is similar.•The overall accuracy increases with the help of fractional Fourier transform-domain features.

论文关键词：Sleep stage classification,Fractional Fourier transform,Bidirectional LSTM

论文评审过程：Received 10 April 2021, Revised 28 February 2022, Accepted 7 March 2022, Available online 9 March 2022, Version of Record 22 March 2022.

论文官网地址：https://doi.org/10.1016/j.artmed.2022.102279