Fractional calculus integration for improved ECG modeling: A McSharry model expansion.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Applied Mathematics

Background:

• Electrocardiogram (ECG) waveform modeling is crucial for diagnosing cardiac conditions.
• Traditional Integer Differential Equation (IDE) models, like the McSharry model, have limitations in accurately representing diverse ECG signals.
• Fractional calculus offers a potential avenue for enhancing the fidelity of dynamic system modeling.

Purpose of the Study:

• To introduce a novel method for ECG waveform modeling using Fractional Differential Equations (FDEs).
• To integrate fractional calculus into the existing McSharry model to improve ECG signal representation.
• To evaluate the performance of the proposed fractional-order model against the traditional IDE model.

Main Methods:

• Incorporation of fractional derivatives into the McSharry IDE model.
• Utilization of the Predictor-Corrector method for solving FDEs.
• Application of genetic algorithms for optimizing model parameters.
• Assessment of model effectiveness using distortion metrics (e.g., Mean Squared Error - MSE).

Main Results:

• The fractional-order model demonstrated superior performance over the traditional McSharry IDE model.
• Significant improvements were observed in modeling quality (48.40% reduction in MSE) and compression efficiency (23.18%).
• The model was validated on five beat types from the MIT/BIH arrhythmia database, with fractional orders (α) ranging from 0.96 to 0.99.

Conclusions:

• The proposed fractional-order model offers enhanced flexibility and preserves key characteristics of the McSharry model.
• FDE-based modeling provides a more accurate and efficient approach for ECG waveform analysis.
• This advanced modeling technique holds promise for improved cardiac diagnostics and signal compression.