Automated QRS complex detection using MFO-based DFOD

• Published in: IET signal processing 2018-12, Vol.12 (9), p.1172-1184
• Main Authors: Nayak, Chandan, Saha, Suman Kumar, Kar, Rajib, Mandal, Durbadal
• Format: Article
• Language: English
• Subjects: detection error rate; ECG signal; electrocardiogram signal; electrocardiography; evolutionary computation; feature signal; Hilbert transforms; IIR filters; infinite impulse response‐type digital first‐order differentiator; medical signal detection; medical signal processing; MFO‐based DFOD; moth‐flame optimisation; nature‐inspired evolutionary algorithm; QRS complex detection; QRS complex detector; QRS complexes; QRS detection rate; Research Article; R‐peak detection logic
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2018.5230

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.