Key result
The proposed MFO-based DFOD QRS complex detector achieved a sensitivity of 99.93%, positive predictivity of 99.92%, and detection error rate of 0.15% on the MIT/BIH arrhythmia database.
The proposed MFO-based DFOD algorithm demonstrates highly accurate QRS complex detection on the MIT/BIH arrhythmia database.
May improve automated ECG analysis if validated; leaves open prospective trials before clinical adoption.
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.
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Nayak et al. (2018) studied Arrhythmia (ECG signals). MFO-based DFOD QRS complex detector vs. Recent works was evaluated on Sensitivity, Positive Predictivity, Detection Error Rate, and QRS Detection Rate. The proposed MFO-based DFOD QRS complex detector achieved a sensitivity of 99.93%, positive predictivity of 99.92%, and detection error rate of 0.15% on the MIT/BIH arrhythmia database.
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