Open AccessAutomated QRS complex detection using MFO‐based DFOD
Author(s) -
Nayak Chandan,
Saha Suman Kumar,
Kar Rajib,
Mandal Durbadal
Publication year - 2018
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2018.5230
Subject(s) - qrs complex , computer science , artificial intelligence , pattern recognition (psychology) , detector , feature (linguistics) , hilbert transform , infinite impulse response , sensitivity (control systems) , speech recognition , digital filter , bandwidth (computing) , computer vision , electronic engineering , telecommunications , medicine , linguistics , philosophy , filter (signal processing) , engineering , cardiology
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.