Sharma Hemant, Sharma K K
Department of Electronics & Communication Engineering, Malaviya National Institute of Technology, Jaipur 302017, India.
Department of Electronics & Communication Engineering, Malaviya National Institute of Technology, Jaipur 302017, India.
Comput Biol Med. 2016 Oct 1;77:116-24. doi: 10.1016/j.compbiomed.2016.08.012. Epub 2016 Aug 13.
This paper introduces a methodology for the detection of sleep apnea based on single-lead electrocardiogram (ECG) of the patient. In the proposed technique, each QRS complex of the ECG signal is approximated using a linear combination of the lower order Hermite basis functions. The coefficients of the Hermite expansion are then used to discriminate the apnea and normal segments along with three features based on R-R time series (mean of R-R intervals, the standard deviation of R-R intervals) and energy in the error of the QRS approximation. To perform classification between the apnea and normal segments, four different types of classifiers (K-nearest neighbor (KNN), multilayer perceptron neural network (MLPNN), support vector machine (SVM), and least-square support vector machine (LS-SVM)) are used in this work. In total, 70 ECG recordings from Apnea-ECG dataset are used in this study and the performance of the proposed algorithm is evaluated based on the minute-by-minute (per-segment) classification, and per-recording (where the entire ECG recording of a subject is discriminated as the apnea or normal one) classification. By considering the events of apnea and hypopnea together, an accuracy of about 84% is achieved on the minute-by-minute basis classification using the LS-SVM classifier with the Gaussian radial basis function (RBF) kernel. On the other hand, an accuracy of about 97.14% is achieved for per-recording classification using the SVM, and LS-SVM classifiers. From the results, it is observed that the proposed methodology provides comparable accuracy with the methods existing in the literature at reduced computational cost due to the lesser number of features selected for the classification.
本文介绍了一种基于患者单导联心电图（ECG）检测睡眠呼吸暂停的方法。在所提出的技术中，ECG信号的每个QRS复合波都使用低阶埃尔米特基函数的线性组合进行近似。然后，埃尔米特展开的系数与基于R-R时间序列的三个特征（R-R间期均值、R-R间期标准差）以及QRS近似误差中的能量一起，用于区分呼吸暂停段和正常段。为了在呼吸暂停段和正常段之间进行分类，本研究使用了四种不同类型的分类器（K近邻（KNN）、多层感知器神经网络（MLPNN）、支持向量机（SVM）和最小二乘支持向量机（LS-SVM））。本研究总共使用了来自Apnea-ECG数据集的70份ECG记录，并基于逐分钟（每段）分类和每份记录（将受试者的整个ECG记录区分为呼吸暂停或正常记录）分类来评估所提出算法的性能。通过将呼吸暂停和呼吸不足事件一起考虑，使用具有高斯径向基函数（RBF）核的LS-SVM分类器在逐分钟分类中实现了约84%的准确率。另一方面，使用SVM和LS-SVM分类器在每份记录分类中实现了约97.14%的准确率。从结果可以看出，由于为分类选择的特征数量较少，所提出的方法以较低的计算成本提供了与文献中现有方法相当的准确率。
