光电容积脉搏波的睡眠呼吸暂停综合征筛查方法
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摘要
睡眠呼吸暂停综合征(SAS)素有"睡眠杀手"之称。由于其诊断金标准多导睡眠监测仪(PSG)的限制,诊断率一直偏低。由于呼吸暂停发生时会引发心率节奏的变化,因此利用心电图(ECG)通过心率变异性(HRV)分析可以实现SAS的自动筛查。但是, ECG-SAS方法所用电极穿戴繁琐、材质致敏性较高,影响睡眠安适度。鉴于脉率变异性(PRV)分析与HRV分析高度相关,并且光电容积脉搏波(PPG)信号相对ECG信号获取方式更加简单,不仅电极不易致敏,而且更易于穿戴,对睡眠干扰小。由此,提出利用同步采集的PPG信号和ECG信号,应用相同的建模方法,比较二者的疾病识别能力。应用反向传播(BP)神经网络,分别建立PPG-SAS与ECG-SAS自动筛查模型,并采用十折交叉验证法及受试者工作特征(ROC)曲线对模型进行对比与评估。实验数据来源于MIT-BIH Polysomnographic Database,共8 248个样本,其中正常样本6 227例。首先采用三层BP神经网络,默认参数下建立PPG-SAS与ECG-SAS模型,使用十折交叉验证法及ROC曲线进行模型分类准确性的对比;然后依次改变影响分类性能的隐层节点数、训练函数以及传递函数,建立多个PPG-SAS与ECG-SAS模型,从中选取各自的最优模型再进行对比。通过比较识别率、预测率以及ROC曲线面积,采用默认参数的PPG-SAS模型优于ECG-SAS模型。通过比较平均分类准确率,隐层节点数为50、训练函数为一步正割算法、隐含层传递函数为双曲正切S型函数时, PPG-SAS模型得到的最高识别率与预测率分别为80.30%和80.13%;隐层节点数为50、训练函数为一步正割算法、隐含层传递函数为径向基时, ECG-SAS模型的最高识别率与预测率分别为77.60%和77.67%。以上实验结果均表明PPG信号的SAS分类能力较ECG信号更具优越性,由此证明了PPG信号筛查SAS的可行性及可靠性,为临床SAS病症的早期发现及诊断率提升奠定理论基础。
Sleep Apnea Syndrome(SAS) is known as the "sleep killer". The diagnostic rate is low due to the limitations of the Polysomnography(PSG) diagnostic criteria. Studies have shown that heart rate rhythm changes when apnea occurs, so automatic screening of SAS can be achieved by measuring Electrocardiograph(ECG) signals based on Heart Rate Variability(HRV) analysis. However, the electrodes used in the ECG-SAS method are cumbersome, can easily cause skin allergy, and affect sleeping comfort. Due to Pulse Rate Variability(PRV) analysis being highly correlated with HRV analysis and photoplethysmography(PPG) signals being simpler to acquire than ECG signals, this study proposes using synchronously acquired PPG and ECG signals and applying the same modeling method to compare the recognition ability of the two methods. The benefits for acquiring PPG instead of ECG are that the electrode does not cause skin allergyand is easier to wear so that it has little interference with sleeping comfort. The Back-Propagation(BP) neural network is applied to establish the automatic screening models of PPG-SAS and ECG-SAS, respectively. The 10-fold cross validation method and the Receiver Operating Characteristic(ROC) curves are used to compare and to evaluate the models. The experimental data are from MIT-BIH Polysomnographic Data base that contains 8 248 samples, including 6 227 normal samples. First of all, we established PPG-SAS and ECG-SAS models using a three-layer BP neural network with the default parameters, and compare their classification performances through the 10-fold cross validation method and the ROC curves. And then, we successively adjusted the number of hidden layer nodes, training functions and transfer functions to establish corresponding PPG-SAS and ECG-SAS models, and compare the respective optimal models obtained by using the 10-fold cross validation method. Through the comparisons of the recognition and prediction accuracies and the area of the ROC curves, the results illustrate that the PPG-SAS model is better than the ECG-SAS model when default parameters were applied. By comparing the average classification performances, we obtained the optimal model of PPG-SAS with 50 hidden layer nodes, trained function based on one-step secant method, and transferred function based on hyperbolic tangent sigmoid. The optimal PPG-SAS model has the highest recognition accuracy of 80.30% and prediction accuracy of 80.13%. Similarly but with a different transfer function of radial basis, the optimalECG-SAS model has the highest recognition accuracy of 77.60% and prediction accuracy of 77.67%. The results showed that the optimal PPG-SAS model is better than the optimal ECG-SAS model. The above experimental results demonstrated that the SAS classification ability by using PPG signals is superior to that by using ECG signals, which proved the feasibility and reliability of the PPG-SAS screening method. Therefore, the PPG-SAS screening method will lay a theoretical foundation on early detection of SAS and improvement of its diagnostic rate.
Sleep Apnea Syndrome(SAS) is known as the "sleep killer". The diagnostic rate is low due to the limitations of the Polysomnography(PSG) diagnostic criteria. Studies have shown that heart rate rhythm changes when apnea occurs, so automatic screening of SAS can be achieved by measuring Electrocardiograph(ECG) signals based on Heart Rate Variability(HRV) analysis. However, the electrodes used in the ECG-SAS method are cumbersome, can easily cause skin allergy, and affect sleeping comfort. Due to Pulse Rate Variability(PRV) analysis being highly correlated with HRV analysis and photoplethysmography(PPG) signals being simpler to acquire than ECG signals, this study proposes using synchronously acquired PPG and ECG signals and applying the same modeling method to compare the recognition ability of the two methods. The benefits for acquiring PPG instead of ECG are that the electrode does not cause skin allergyand is easier to wear so that it has little interference with sleeping comfort. The Back-Propagation(BP) neural network is applied to establish the automatic screening models of PPG-SAS and ECG-SAS, respectively. The 10-fold cross validation method and the Receiver Operating Characteristic(ROC) curves are used to compare and to evaluate the models. The experimental data are from MIT-BIH Polysomnographic Data base that contains 8 248 samples, including 6 227 normal samples. First of all, we established PPG-SAS and ECG-SAS models using a three-layer BP neural network with the default parameters, and compare their classification performances through the 10-fold cross validation method and the ROC curves. And then, we successively adjusted the number of hidden layer nodes, training functions and transfer functions to establish corresponding PPG-SAS and ECG-SAS models, and compare the respective optimal models obtained by using the 10-fold cross validation method. Through the comparisons of the recognition and prediction accuracies and the area of the ROC curves, the results illustrate that the PPG-SAS model is better than the ECG-SAS model when default parameters were applied. By comparing the average classification performances, we obtained the optimal model of PPG-SAS with 50 hidden layer nodes, trained function based on one-step secant method, and transferred function based on hyperbolic tangent sigmoid. The optimal PPG-SAS model has the highest recognition accuracy of 80.30% and prediction accuracy of 80.13%. Similarly but with a different transfer function of radial basis, the optimalECG-SAS model has the highest recognition accuracy of 77.60% and prediction accuracy of 77.67%. The results showed that the optimal PPG-SAS model is better than the optimal ECG-SAS model. The above experimental results demonstrated that the SAS classification ability by using PPG signals is superior to that by using ECG signals, which proved the feasibility and reliability of the PPG-SAS screening method. Therefore, the PPG-SAS screening method will lay a theoretical foundation on early detection of SAS and improvement of its diagnostic rate.
引文
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[2] Yaggi H Klar,John Concato,Walter N Kernan,et al.The New England journal of medicine,2005,353(19):2034.
[3] Selim B J,Junna M R,Morgenthaler T I.Sleep Medicine Clinics,2014,9(1):37.
[4] Hang L W,Lin H H,Chiang Y W,et al.Applied Mathematics & Information Sciences,2013,7(1):227.
[5] Chazal P D,Heneghan C,Sheridan E,et al.Computers in Cardiology,2000,27:745.
[6] Mietus J E,Peng C K,ChIvanov P,et al.Computers in Cardiology,2000,27:753.
[7] Zywietz C W,Von E V,Widiger B,et al.Methods of Information in Medicine,2004,43(1):56.
[8] Chazal P D,Heneghan C,Sheridan E,et al.IEEE Transactions on Biomedical Engineering,2003,50(6):686.
[9] TONG Guang-ming,GUO Ji-hong,HAN Fang,et al(佟光明,郭继鸿,韩芳,等).National Medical Journal of China(中华医学杂志),2006,86(122):1545.
[10] Acharya U R,Chua E C,Faust O,et al.Physiological Measurement,2011,32(3):287.
[11] Khaldon Lweesy,Luay Fraiwa,Natheer Khasawneh,et al.Journal of Medical Systems,2011,35:723.
[12] Kesper K,Canisius S,Penzel T,et al.Medical & Biological Engineering & Computing,2012,50(2):135.
[13] Poupard Laurent,Philippe Carole,Goldman Michael David,et al.Sleep and Breathing,2012,16(2):419.
[14] Ahmet Resit Kavsaoglu,Kemal Polat,Mehmet Recep Bozkurt.Turkish Journal of Electrical Engineering & Computer Sciences,2016,24:1782.
[15] Sch?fer A,Vagedes J.International Journal of Cardiology,2013,166(1):15.
[16] Bulte C S E,Keet S W M,Boer C,et al.European Journal of Anaesthesiology,2013,68(7):775.
[17] Poh M Z,Swenson N C,Picard R W.IEEE Transactions Information Technology in Biomedicine,2010,14(3):786.
[18] Li Y,Gao H,Ma Y.Medicine,2017,96(18):e6755.
[19] LI Jun-feng,WANG Yue-le,HU Sheng,et al(李俊峰,汪月乐,胡升,等).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016,36(10):3261.