An optimally designed digital differentiator based preprocessor for R-peak detection in electrocardiogram signal

Abstract

Abstract Globally the human death rate is accelerating day by day due to the cardiovascular diseases (CVDs), and it will be elevated shortly. In this scenario, the QRS complex detection of electrocardiogram (ECG) signal is considered as a simple, non-invasive, inexpensive, and preliminary diagnosis method used to assess the cardiac health of a patient. In this paper, an optimally designed Integer Order Digital Differentiator (IODD) based preprocessor is proposed for the accurate estimation of R-peak locations in the ECG signal. IODD, one of the major constituents of the preprocessor, is designed most proficiently by using a metaheuristic evolutionary optimization method called Gravitational Search Algorithm (GSA). In GSA, as the number of iteration increases the exploration capability fades out, and the exploitation capability fades in, which help it to avoid the local optima stagnation problem and results in faster convergence. The IODD based preprocessor accentuates the QRS complexes of the ECG signal irrespective of its abnormal morphology. The employed detector is a simple threshold independent R-peak decision logic designed by utilizing the properties of the Hilbert transform. In order to emphasize the superiority of the proposed research work the proposed IODD based QRS detection approach is validated on the first channel records of MIT/BIH Arrhythmia database (MBAD), QT database (QTDB), MIT/BIH noise stress test database (NSTDB), atrial fibrillation termination challenge database (AFTDB), and MIT/BIH ST change database (STDB). The sensitivity (Se) and positive Predictivity (+P) values for MBAD, QTDB, NSTDB, AFTDB, and STDB are Se=99.92% and +P=99.92%, Se=99.98% and +P=99.96%, Se=95.23% and +P=94.41%, Se=99.03% and +P=99.76%, and Se=99.93% and +P=99.90%, respectively. These performance metrics ensure the accuracy of the proposed R-peak detection technique for a wide variety of QRS morphologies and thereby affirm the applicability of the proposed IODD for the efficient detection of R-peak locations. The performance of the proposed R-peak detector significantly outperforms the reported methods in terms of all the performance metrics. The enhanced QRS detection accuracy of the proposed approach is due to the better feature signal generating capability of the proposed IODD.