北京生物醫(yī)學工程

基于多通道的睡眠呼吸暫停檢測

Multichannel-based sleep apnea detection

作者：熊馨張亞茹吳迪馮建楠易三莉王春武劉瑞湘賀建峰

單位：昆明理工大學信息工程與自動化學院 (昆明650500)<br />韓山師范學院物理與電子工程學院 (廣東潮州521000)<br />云南省第二人民醫(yī)院臨床心理科(昆明650021)<br />通信作者：賀建峰，教授。E-mail: [email protected]

關鍵詞：睡眠呼吸暫停;心電信號;小波閾值; Relief算法;支持向量機

分類號：R318.04

出版年·卷·期（頁碼）：2023·42·2（152-157）

摘要：

目的為了提高檢測性能和驗證不同生理信號對睡眠呼吸暫停的檢測結果。本文提出一種信號疊加和通道相加檢測睡眠呼吸暫停的方法。方法首先對100例睡眠呼吸障礙患者的心電（electrocardiogram, ECG）和腦電（electroencephalogram, EEG）信號通過小波閾值方法進行預處理，其次進行通道相加和信號疊加，然后通過Relief特征選擇算法對30個特征進行分析，最后采用支持向量機（support vector machine, SVM）構建睡眠呼吸暫停分類模型，并驗證該模型的準確性。結果實驗結果表明，通道相加和信號疊加時睡眠呼吸暫停檢測的最高準確率分別為96.24%和96.18%。結論 ECG和EEG兩種信號疊加和通道相加的方法均可提高睡眠呼吸暫停檢測結果，且X2（ECG）和C3-A2（EEG）通道相加檢測結果最好。

Objective In order to improve the detection performance and verify the detection results of different physiological signals on sleep apnea. This paper presents a method of signal superposition and channel addition to detect sleep apnea. Methods The electrocardiogram (ECG) and electroencephalogram (EEG) signals of 100 patients with sleep apnea were pretreated by wavelet threshold method, followed by channel addition and signal stacking. Then, the Relief feature selection algorithm was used to analyze the 30 features. Finally, support vector machine (SVM) was used to construct the classification model of sleep apnea, and the accuracy of the model was verified. Results The experimental results showed that the highest accuracy of the detection of sleep apnea was 96.24% and 96.18% when the channels were added and the signals were added, respectively. Conclusions ECG and EEG signal combination and channel addition can improve the detection results of sleep apnea, and the X2 (ECG) and C3-A2 (EEG) channel addition results are the best.

參考文獻：

［1］Asghar Z, Asl BM. Automatic detection of obstructive sleep apnea using wavelet transform and entropy-based features from single-lead ECG signal [J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23 (3): 1011–1021.
［2］Song C, Liu K, Zhang X, et al. An obstructive sleep apnea detection approach using a discriminative hidden Markov model from ECG signals[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(7): 1532-1542.
［3］Xie B, Minn H. Real-time sleep apnea detection by classifier combination[J]. IEEE Transactions on Information Technology in Biomedicine, 2012, 16 (3): 469–477.
［4］Sharan RV, Berkovsky S, Xiong H, et al. End-to-end sleep apnea detection using single-lead ECG Signal and 1-D residual neural networks[J]. Journal of Medical and Biological Engineering, 2021, 41: 758–766.
［5］Li K, Pan W, Li Y, et al. A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal [J]. Neurocomputing, 2018, 294: 94-101.
［6］Sharma H, Sharma KK. An algorithm for sleep apnea detection from single-lead ECG using Hermite Basis functions [J]. Computers in Biology and Medicine, 2016, 77: 116-124.
［7］Hassan AR. Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting[J]. Biomedical Signal Processing and Control, 2016, 29: 22-30.
［8］Khalighi S, Sousa T, Santos JM, et al. ISRUC-sleep: a comprehensive public dataset for sleep researchers[J]. Computer Methods and Programs in Biomedicine, 2016, 124: 180-192.
［9］周靜,吳效明. 睡眠呼吸暫停綜合征腦電關聯(lián)維特性研究[J].生物醫(yī)學工程學雜志,2017,34(2): 168-172.
Zhou J, Wu XM. Study on the property of correlation dimension of sleep apnea syndrome electroencephalogram[J]. Journal of Biomedical Engineering, 2017, 34 (2): 168-172.
［10］康志欽,黃麗媚,陳昆龍,等.阻塞性睡眠呼吸暫停綜合征的心率變異性分析[J].心血管病防治知識(學術版), 2019(3): 41-43.
［11］梁曉花. 基于腦電心電數(shù)據(jù)融合的睡眠分期[D]. 鎮(zhèn)江：江蘇大學, 2008.
［12］Yücelba, Yücelba C, Tezel G, et al. Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal[J]. Expert Systems with Applications, 2018, 102: 193-206.
［13］Hassan AR, Haque A. An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting[J]. Neurocomputing, 2017, 235: 122-130.
［14］余曉敏, 涂岳文,黃超, 等. 基于心電信號的睡眠呼吸暫停綜合征檢測算法[J].生物醫(yī)學工程學雜志, 2013, 30(5): 999-1002.
Yu XM, Tu YW, Huang C, et al. An algorithm based on ECG signal for sleep apnea syndrome detection[J]. Journal of Biomedical Engineering, 2013, 30(5): 999-1002.
［15］董孝彤, 曲新亮, 魏守水. 用于睡眠呼吸暫停檢測的心電特征穩(wěn)定性分析[J]. 生物醫(yī)學工程研究, 2020, 39(1): 6-10.
Dong XT, Qu XL, Wei SS. Stability analysis of electrocardiogram features for sleep apnea detection[J]. Biomedical Engineering Research, 2020, 39(1): 6-10.
［16］李曉嵐. 基于Relief特征選擇算法的研究與應用[D].大連: 大連理工大學, 2013.
Li XL. The study and application of feature selection algorithms based on relief[D]. Dalian: Dalian University of Technology, 2013.
［17］Huang W, Guo B, Shen Y, et al. Sleep staging algorithm based on multichannel data adding and multifeature screening[J]. Computer Methods and Programs in Biomedicine, 2020, 187: 105253.
［18］Kumar TS, Kanhangad V. Automated obstructive sleep apnea detection using symmetrically weighted local binary patterns[J]. Electronics Letters, 2017, 53(4): 212-214.
［19］Bsoul M, Minn H, Tamil L. Apnea MedAssist: real-time sleep apnea monitor using single-lead ECG[J]. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(3): 416-427.
［20］Hassan AR, Haque MA. Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating [J]. Biocybernetics and Biomedical Engineering, 2016, 36(1): 256-266.

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