Automated QRS complex detection using MFO-based DFOD

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.