[Scale invariance based analysis of pathological ECG signals]

Area of Science:

• Cardiology
• Complex Systems Analysis
• Signal Processing

Context:

• Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac arrhythmias.
• Distinguishing between different types of premature beats, such as premature ventricular contractions (PVCs) and atrial premature beats (APB), presents clinical challenges.
• Understanding the underlying signal dynamics of cardiac arrhythmias can improve diagnostic accuracy.

Purpose:

• To investigate and compare the multifractal characteristics of normal ECG signals, APB signals, and PVC signals.
• To apply the multifractal detrended fluctuation analysis (MF-DFA) method to quantify signal complexity and long-range correlations.
• To establish objective criteria for differentiating between APB and PVC based on their fractal properties.

Summary:

• The multifractal detrended fluctuation analysis (MF-DFA) was employed to examine normal ECG, APB, and PVC signals.
• Analyses of Hurst index, Renyi index, and multifractal spectrum revealed significant differences in long-range correlation and fractal properties among the three signal types.
• Normal ECG signals exhibited the strongest fractality, followed by PVC beats, and then APB beats, which showed the weakest fractality.

Impact:

• The distinct multifractal signatures identified for normal, PVC, and APB signals offer a novel approach for arrhythmia diagnosis.
• These findings provide a valuable reference for clinicians in distinguishing between PVC and APB signals, potentially improving patient management.
• The study highlights the utility of complex systems analysis in understanding cardiac electrophysiology and developing advanced diagnostic tools.