ECG Feature Extraction and Detection of First Degree Atrioventricular Block

Electrocardiogram has been the most prominent diagnostic technique for cardiac disease detection. It helps cardiologist to obtain the waveform of heart electrical potential activates, which enables them in detecting several heart problems, such as arrhythmia, which is the major reason of death. As the ECG monitoring is widely used in diagnostic, many challenges have been faced by patients due to lack of availability of cardiac specialists at remote areas or rural areas. It is not possible that the cardiologist with entire system of diagnosis to visit again and again to patient for monitoring of ECG. Currently there are many computer based approach which use signal processing to diagnose heart diseases based on ECG recordings. The purpose of this research is to address in identifying the features of ECG and detection of 1
^st
degree atrioventricular block. Lab VIEW is used to extract the required information from the input data which are Mean and Standard Deviation value. Then the extracted features data is analyzed and classified using Lab VIEW. The proposed approach is implemented and also tested in Lab VIEW software. Using it, successfully extracted ECG features and classifies the first degree atrioventricular block with the rate of accuracy 97.22%.