An Efficient and Robust Digital Fractional Order Differentiator Based ECG Pre-Processor Design for QRS Detection.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Computational Intelligence

Background:

• Accurate QRS complex detection is crucial for electrocardiogram (ECG) analysis in clinical settings.
• Existing QRS detection methods often struggle with ECG signal variability and noise.
• Digital fractional order differentiators (DFODs) offer potential for improved signal processing in biomedical applications.

Purpose of the Study:

• To develop and evaluate an efficient infinite impulse response (IIR) type digital fractional order differentiator (DFOD) for ECG preprocessing.
• To integrate the designed DFOD into a threshold-independent R-peak detection technique for robust QRS complex identification.
• To demonstrate the superiority of the proposed DFOD-based QRS detector against existing methods using diverse ECG databases.

Main Methods:

• Design of an efficient IIR-type DFOD using the Antlion optimization algorithm.
• Deployment of the designed DFOD in the preprocessing stage of an R-peak detection algorithm.
• Rigorous testing and validation on six standard ECG databases: MIT/BIH Arrhythmia, MIT/BIH ST Change, MIT/BIH Supraventricular Arrhythmia, European ST-T, QT, and T-Wave Alternans Challenge.

Main Results:

• The proposed DFOD achieved low root-mean-square magnitude error (RMSME) of -38.17 dB and average group delay (τ_DD) of 0.04 samples.
• A 15% improvement in RMSME was observed compared to the best-reported approaches.
• Exceptional performance metrics were achieved across all databases, with overall sensitivity of 99.89% and positive predictivity of 99.88%.

Conclusions:

• The study presents the first application of an evolutionary algorithm-based IIR-type DFOD for QRS complex detection, establishing its performance superiority.
• The DFOD-based ECG pre-processor robustly generates feature signals related to QRS complexes, irrespective of ECG morphology.
• The proposed DFOD-based QRS detector holds significant potential for enhancing QRS detection performance in clinical ECG monitoring devices.