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access icon free Automated QRS complex detection using MFO-based DFOD

© The Institution of Engineering and Technology
Received 15/11/2017, Accepted 16/07/2018, Revised 13/06/2018, Published 17/08/2018

This study proposes a heuristic approach for designing highly efficient, infinite impulse response (IIR) type Digital First-Order Differentiator (DFOD) by employing a nature-inspired evolutionary algorithm called Moth-Flame Optimisation (MFO) for the detection of the QRS complexes in the electrocardiogram (ECG) signal. The designed DFOD is used in the pre-processing stage of the proposed QRS complex detector, to generate feature signals corresponding to each R-peak by efficiently differentiating the ECG signal. The generated feature signal is employed to detect the precise instants of the R-peaks by using a Hilbert transform-based R-peak detection logic. The performance efficiency of the proposed QRS complex detector is evaluated by using all the first channel records of the MIT/BIH arrhythmia database (MBDB), regarding the standard performance evaluation metrics. The proposed approach has resulted in Sensitivity (Se) of 99.93%, Positive Predictivity (PP) of 99.92%, Detection Error Rate (DER) of 0.15%, and QRS Detection Rate (QDR) of 99.92%. Performance comparison with the recent works justifies the superiority of the proposed approach.

Inspec keywords: IIR filters; Hilbert transforms; electrocardiography; medical signal processing; medical signal detection; evolutionary computation

Other keywords: nature-inspired evolutionary algorithm; QRS detection rate; QRS complex detector; ECG signal; infinite impulse response-type digital first-order differentiator; QRS complex detection; feature signal; electrocardiogram signal; MFO-based DFOD; R-peak detection logic; detection error rate; moth-flame optimisation; QRS complexes

Subjects: Bioelectric signals; Electrodiagnostics and other electrical measurement techniques; Biology and medical computing; Digital signal processing; Filtering methods in signal processing; Electrical activity in neurophysiological processes

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