Optimal SSA‐based wideband digital differentiator design for cardiac QRS complex detection application

Abstract

AbstractIn this paper, a computationally efficient, highly accurate, wideband, stable, and minimum phase infinite impulse response type first‐order digital differentiator (DD) is designed by employing a swarm intelligence‐based search method called Salp Swarm Algorithm (SSA) for the QRS complex detection application. The optimal coefficients of the DD are computed by minimizing a suitable fitness function to meet the ideal differentiator magnitude response characteristics. The simulation results and the root mean square magnitude error metric justify the superiority of the proposed SSA‐based DD design as compared with all other differentiators employed in the QRS complex detection application, and the reported first‐order DDs based on the numerical methods and the other evolutionary algorithms. The electrocardiogram signal is preprocessed by the proposed DD to generate the feature signal corresponding to each QRS complex. The generated feature signal is used as a marker to identify the exact occurrence of the QRS complex by using an adaptive threshold‐based detection logic. The proposed DD‐based QRS detection approach achieves a sensitivity (Se), positive prediction (PP), detection error rate (DER), and accuracy (Acc) of 99.94%, 99.93%, 0.1279%, and 99.87%, respectively, when validated against MIT/BIH arrhythmia database. Also, against the QT database, the proposed QRS detector produces a Se of 99.93%, PP of 99.97%, DER of 0.09%, and Acc of 99.90%. The performance of the proposed QRS detection technique is compared with the methods already reported in the recent literature, and the superiority of the proposed approach is established with respect to different standard performance metrics. The noise tolerance capability of the proposed QRS detector is demonstrated against MIT/BIH noise stress test database.