Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (2): 21-26.doi: 10.16180/j.cnki.issn1007-7820.2021.02.004

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The Algorithm Based on CNN and LSTM for Sleep Apnea Syndrome Detection

GE Jing,LIU Zilong   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2019-11-05 Online:2021-02-15 Published:2021-01-22
  • Supported by:
    National Natural Science Foundation of China(61573246)


Sleep apnea is a common sleep disorder, which is associated with multiple diseases. This study proposes deep learning methods to detect sleep apnea events. The two stages of ECG signal preprocessing are used as inputs to the CNN and LSTM models, respectively. The CNN model takes the original ECG signal as input and automatically extracts features through convolution to identify sleep apnea. The LSTM model uses ECG's indirect signal as input and automatically extractes features from RR intervals and respiratory signals. Experiments show that the LSTM model has a high accuracy of 87.4%, which is close to the performance of the traditional methods. The proposed method combines the advantages of artificial feature extraction and deep learning, and is more applicable than the traditional classification methods.

Key words: CNN, LSTM, sleep apnea, electrocardiograph signal, preprocessing, feature extraction

CLC Number: 

  • TP183