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| Sleep Apnea Detection Based on Auto-encoder and Hidden Markov Model |
| Qin Hengji1,2,3, Liu Guanzheng1,2,3#* |
1(School of Biomedical Engineering,Sun Yat-sen University,Guangzhou 510006,China)
2(Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province,Guangzhou 510006,China)
3(Guangdong Provincial Engineering and Technology Centre of Advanced and Portable Medical Device,Guangzhou 510006,China) |
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Abstract Obstructive sleep apnea (OSA) is prone to cardiovascular complications. As a gold standard for the diagnosis of sleep apnea,polysomnography is expensive and affects the sleep quality of patients. Because of the high coupling between heart and lung,electrocardiogram (ECG) signals are widely used in sleep apnea detection. However,most of the studies based on ECG signals focus on the design of artificial features,relying on the prior knowledge of experts. Methods based on deep learning can reduce human factors during feature extraction. In this study,we proposed a sleep apnea detection method based on auto-encoder and hidden Markov model (HMM). Firstly,a stacked sparse auto-encoder was used to perform semi-supervised feature learning directly from the RR interval sequence. Unsupervised learning was performed during the pre-training phase,and labels were then introduced for supervised learning during the fine-tuning phase. Then,a decision fusion classifier based on support vector machine (SVM) and artificial neural network (ANN) combined with HMM was constructed. HMM introduced the temporal dependence between segments. Decision fusion integrated the advantages between different classifiers. Experimental results based on the sleep data of 70 cases of all-night in PhysioNet′s apnea-ECG database showed that the accuracy,sensitivity and specificity of per-segment OSA detection was 84.7%,88.9% and 82.1% respectively,and per-subject detection accuracy was 100%. Compared with feature engineering,the feature extraction method based on auto-encoder could reduce the limitation of prior knowledge and make the feature extraction process more automatic and intelligent. In addition,compared with the single classifier,the decision fusion classifier not only improved the accuracy of per-segment OSA detection,but also alleviated the imbalance between sensitivity and specificity in detection results.
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Received: 11 October 2019
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