Textile Department, Faculty of Industrial Education, Suez University, Egypt.
Biomed Phys Eng Express. 2020 Jan 20;6(1):015024. doi: 10.1088/2057-1976/ab6995.
Nowadays, with the increasing number of people who suffer from cardiovascular diseases such as irregular heartbeats (arrhythmia), there is a vital need to pay more attention to healthcare conditions. Therefore, the production of smart biomedical garments becomes of great necessity. The first step of manufacturing such smart garments is to build an electrocardiogram (ECG) analysis system. In this paper, the premature ventricular contraction (PVC), which is a serious life-threatening cardiovascular condition, is recognised. In addition, an improved template matching technique is developed, implemented, and evaluated to identify the irregularity of PVC beats in the QRS complex and T wave. The improvement in this technique is that a PVC recogniser is established by analysing the maximum and minimum correlation coefficient values instead of the maximum values only. Moreover, a sufficient number of features are relied upon for the accurate detection of PVC beats. The template matching algorithm is evaluated on the MIT-BIH arrhythmia, St. Petersburg Institute of Cardiological Technics (INCART), QT, MIT-BIH Supraventricular Arrhythmia, and Fantasia databases. The results show a valuable accuracy enhancement when compared with those of other recent approaches.
如今，由于越来越多的人患有心血管疾病，如心律失常，因此需要更加关注医疗保健状况。因此，生产智能生物医学服装变得非常必要。制造这种智能服装的第一步是构建心电图（ECG）分析系统。在本文中，识别出了室性早搏（PVC），这是一种严重的危及生命的心血管疾病。此外，还开发、实现和评估了一种改进的模板匹配技术，以识别 QRS 复合波和 T 波中 PVC 搏动的不规则性。该技术的改进之处在于，通过分析最大和最小相关系数值而不是仅最大值来建立 PVC 识别器。此外，还依赖足够数量的特征来准确检测 PVC 搏动。在 MIT-BIH 心律失常、圣彼得堡心血管技术研究所（INCART）、QT、MIT-BIH 室上性心律失常和 Fantasia 数据库上评估了模板匹配算法。与其他最近的方法相比，结果显示出有价值的准确性提高。
