Rajput Jaypal Singh, Sharma Manish, Tan Ru San, Acharya U Rajendra
Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, India.
Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, India.
Comput Biol Med. 2020 Aug;123:103924. doi: 10.1016/j.compbiomed.2020.103924. Epub 2020 Jul 23.
Hypertension (HPT) is a serious risk factor for cardiovascular disease and if not controlled in the early stage, can lead to serious complications. Long-standing HPT can induce heart muscle hypertrophy which will be reflected on electrocardiography (ECG). However, early stage of HPT may have no clinically discernible ECG perturbations, and is difficult to diagnose manually from the standard ECG. Hence, we propose an automated ECG based system that can automatically detect the ECG changes in the early stages of HPT. This work is based on ECG signals obtained from 139 HPT patients (SHAREE database) and 52 healthy subjects (PTB database). The ECG signal is non-stationary with relatively short duration, and rhythmic. Two-band optimal bi-orthogonal wavelet filter bank (BOWFB) and machine learning are used to automatically diagnose low, high-risk hypertension, and healthy control using ECG signals. Five-level wavelet decomposition is used to produce six sub-bands (SBs) from each ECG signal using BOWFB. Sample and wavelet entropy features are calculated for all six SBs. The features calculated SBs are fed to the k-nearest neighbor (KNN), support vector machine (SVM), and ensemble bagged trees (EBT) classifiers. In this work, we have obtained the highest average classification accuracy of 99.95% and area under the curve of 1.00 using EBT classifier in classifying healthy control (HC), low-risk hypertension (LRHPT) and high-risk hypertension (HRHPT) classes with ten-fold cross validation strategy. Hence the developed system can be used in clinics, or even in remote detection of HPT stages using ECG signals.
高血压（HPT）是心血管疾病的严重风险因素，若早期未得到控制，可能导致严重并发症。长期高血压可诱发心肌肥厚，这将在心电图（ECG）上有所体现。然而，高血压早期可能没有临床上可察觉的心电图异常，且难以从标准心电图中手动诊断。因此，我们提出一种基于心电图的自动化系统，该系统能够自动检测高血压早期的心电图变化。这项工作基于从139名高血压患者（SHAREE数据库）和52名健康受试者（PTB数据库）获取的心电图信号。心电图信号是非平稳的，持续时间相对较短且有节律。使用双带最优双正交小波滤波器组（BOWFB）和机器学习，利用心电图信号自动诊断低、高风险高血压以及健康对照。使用BOWFB对每个心电图信号进行五级小波分解，以产生六个子带（SBs）。计算所有六个子带的样本熵和小波熵特征。将计算得到的子带特征输入到k近邻（KNN）、支持向量机（SVM）和集成袋装树（EBT）分类器中。在这项工作中，我们采用十折交叉验证策略，使用EBT分类器对健康对照（HC）、低风险高血压（LRHPT）和高风险高血压（HRHPT）类别进行分类时，获得了最高平均分类准确率为99.95%，曲线下面积为1.00。因此，所开发的系统可用于临床，甚至可用于通过心电图信号远程检测高血压阶段。
