Abstract
Abstract This research work aims to implement the automatic detection of heart abnormality type without doctor’s assessment using electrocardiogram (ECG) features. First, the ECG signal is acquired via Ag-AgCl electrodes, preprocessed using the adaptive Wiener filter to remove the noises and classified with the help of feature extraction techniques and fuzzy decision making (FDM) algorithm. The FDM also finds the type of heart abnormality based on ECG features, such as RRp interval, R peak amplitude detection, and QRS complex interval, and then sends the classified type to the doctor wirelessly via a ZigBee module. The virtual instrument software has been used to validate the proposed concept, and results of both software and hardware parts have been presented to show the effectiveness of the work.
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