Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, India.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.
Comput Biol Med. 2018 Sep 1;100:100-113. doi: 10.1016/j.compbiomed.2018.06.011. Epub 2018 Jun 19.
Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption of breathing resulting in insufficient oxygen to the human body and brain. If the OSA is detected and treated at an early stage the possibility of severe health impairment can be mitigated. Therefore, an accurate automated OSA detection system is indispensable. Generally, OSA based computer-aided diagnosis (CAD) system employs multi-channel, multi-signal physiological signals. However, there is a great need for single-channel bio-signal based low-power, a portable OSA-CAD system which can be used at home. In this study, we propose single-channel electrocardiogram (ECG) based OSA-CAD system using a new class of optimal biorthogonal antisymmetric wavelet filter bank (BAWFB). In this class of filter bank, all filters are of even length. The filter bank design problem is transformed into a constrained optimization problem wherein the objective is to minimize either frequency-spread for the given time-spread or time-spread for the given frequency-spread. The optimization problem is formulated as a semi-definite programming (SDP) problem. In the SDP problem, the objective function (time-spread or frequency-spread), constraints of perfect reconstruction (PR) and zero moment (ZM) are incorporated in their time domain matrix formulations. The global solution for SDP is obtained using interior point algorithm. The newly designed BAWFB is used for the classification of OSA using ECG signals taken from the physionet's Apnea-ECG database. The ECG segments of 1 min duration are decomposed into six wavelet subbands (WSBs) by employing the proposed BAWFB. Then, the fuzzy entropy (FE) and log-energy (LE) features are computed from all six WSBs. The FE and LE features are classified into normal and OSA groups using least squares support vector machine (LS-SVM) with 35-fold cross-validation strategy. The proposed OSA detection model achieved the average classification accuracy, sensitivity, specificity and F-score of 90.11%, 90.87% 88.88% and 0.92, respectively. The performance of the model is found to be better than the existing works in detecting OSA using the same database. Thus, the proposed automated OSA detection system is accurate, cost-effective and ready to be tested with a huge database.
阻塞性睡眠呼吸暂停（OSA）是一种由呼吸中断引起的睡眠障碍，导致人体和大脑供氧不足。如果在早期发现并治疗 OSA，可以降低严重健康损害的可能性。因此，需要一个准确的自动化 OSA 检测系统。一般来说，基于计算机的 OSA 诊断（CAD）系统采用多通道、多信号生理信号。然而，需要一种基于单通道生物信号的低功耗、便携式 OSA-CAD 系统，可以在家中使用。在这项研究中，我们提出了一种基于单通道心电图（ECG）的 OSA-CAD 系统，该系统使用了一类新的最优双正交反对称子波滤波器组（BAWFB）。在这类滤波器组中，所有滤波器的长度都是偶数。滤波器组的设计问题转化为一个约束优化问题，目标是最小化给定时间扩展的频率扩展或给定频率扩展的时间扩展。该优化问题被表述为半定规划（SDP）问题。在 SDP 问题中，目标函数（时间扩展或频率扩展）、完美重建（PR）和零矩（ZM）的约束都被纳入它们的时域矩阵公式中。使用内点算法得到 SDP 的全局解。新设计的 BAWFB 用于使用来自 physionet 的 Apnea-ECG 数据库的 ECG 信号对 OSA 进行分类。将 1 分钟时长的 ECG 段通过采用所提出的 BAWFB 分解成六个子波带（WSB）。然后，从所有六个 WSB 中计算模糊熵（FE）和对数能量（LE）特征。FE 和 LE 特征使用最小二乘支持向量机（LS-SVM）和 35 折交叉验证策略分为正常和 OSA 组。所提出的 OSA 检测模型的平均分类准确率、灵敏度、特异性和 F 分数分别为 90.11%、90.87%、88.88%和 0.92。与使用相同数据库检测 OSA 的现有工作相比，该模型的性能更好。因此，所提出的自动化 OSA 检测系统准确、具有成本效益，并准备好使用大型数据库进行测试。
