Characterization of heart atrial fibrillation: proposal for a deterministic model

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• Atrial Fibrillation (AFib) is linked to increased stroke risk and reduced cardiac output.
• The prevailing theory suggests reentry mechanisms underlie AFib.
• Studying AFib requires effective cardiac signal characterization methods.

Purpose of the Study:

• To propose a deterministic model for characterizing cardiac signals in Atrial Fibrillation.
• To assess the utility of mathematical transforms for AFib signal analysis using the proposed model.

Main Methods:

• Developed a deterministic model by linearly combining "Atrial Flutter"-like signals to simulate AFib.
• Applied a power spectrum transform (scalar Fast Fourier Transform) to analyze the model's output.
• Evaluated the suitability and potential pitfalls of the transform for AFib signal interpretation.

Main Results:

• The proposed model successfully generated AFib-like signal characteristics.
• Standard power spectrum analysis, when applied to the model, was found to be potentially misleading.
• Direct application of scalar Fast Fourier Transform may lead to misinterpretation of AFib signals.

Conclusions:

• The novel deterministic model offers a platform for evaluating mathematical transforms in AFib research.
• Findings suggest a need for refined analytical approaches beyond standard power spectrum analysis for AFib.
• This work may contribute to improved AFib classification, patient management, and survival rates.