ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (12): 2547-2555.doi: 10.7544/issn1000-1239.2020.20190583

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Multi-Feature Information Fusion LSTM-RNN Detection for OSA

Zhu Zhaokun1,2,3, Li Jinbao1   

  1. 1(Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Science), Jinan 250014);2(School of Computer Science and Tecnology, Heilongjiang University, Harbin 150080);3(Software Technology Institute, Heilongjiang University, Harbin 150080)
  • Online:2020-12-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61370222) and the Key Program of the Natural Science Foundation of Heilongjiang Province of China (ZD2019F003).

Abstract: Obstructive sleep apnea (OSA) is the most common sleep respiratory disorder, and it is a potential threat to many physiological systems, especially the cardiovascular system. Most of the previous methods for OSA detection extracted the shallow features from electrocardiograph (ECG) which would be used in classifiers, and they failed to achieve excellent performances on the ECG signal with high noise and large datasets. To solve this kind of problem, this paper proposes a long short-term memory recurrent neural network (LSTM-RNN) based on combination of multiple kinds of feature signals. The method fuses multiple kinds of shallow feature signals that are extracted from ECG signals and learns the deep feature from the fused signals. The accuracy of OSA detection model in long ECG segments is increased and the generalization ability on large datasets is improved. An effective preprocessing method is propesed for shallow feature signals to highlight the variation of OSA time sequences. The preprocessing method may improve the convergence of training neural networks, reduce the impact of outlier noise, and further improve the detection accuracy of the model for the ECG segments with high noise. The experimental results indicate that our method is superior to the existing methods in the accuracy of per-segment OSA detection.

Key words: obstructive sleep apnea, electrocardiograph, preprocessing, long short-term memory, recurrent neural network

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