Automated detection of premature ventricular contraction in ECG signals using enhanced template matching algorithm.

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.