MULTI-head self-attention-based recurrent neural network with dwarf mongoose optimization algorithm-espoused QRS detector design

QRS detection is the essential electrocardiogram (ECG) analysis procedure. A reliable QRS recognition system that achieves high accuracy despite a typical QRS morphologies and significant noise is required for ECG data gathered by wearable devices. Most contemporary systems seek fast execution durations and minimal energy consumption while attaining high prediction rates. To minimize these issues, this research article presents an approach of multi-head self-attention-based recurrent neural network with dwarf mongoose optimization algorithm-espoused QRS detector design (MHSARNN-QRS) is proposed. Initially, ECG data are taken from MIT/BIH arrhythmia dataset (MIT-AD). Every disintegrated signal is transmitted to multi-head self-attention-based recurrent neural network (MHSARNN) to examine morphologies and predict QRS like correct and incorrect. Then, the QRS wave is located through dwarf mongoose optimization algorithm by reducing probability of neglected identification improves the detection performance. The performance of proposed MHSARNN-QRS method is evaluated using accuracy, sensitivity, specificity, detection error rate, computation time, f1-score, positive prediction, and time for processing a single record (s) and single beat (ms) are analyzed. Performance of the MHSARNN-QRS approach attains high sensitivity, lower single record, lower single beat, and greater accuracy compared with existing methods.