A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• Electrocardiogram (ECG) signal analysis provides noninvasive insights into cardiac electrophysiology and autonomic nervous system activity.
• Both linear and nonlinear methods are used for ECG analysis, with nonlinear approaches gaining prominence.
• Nonlinear methods demonstrate robustness in feature extraction and classification of ECG signals.

Purpose of the Study:

• To review various nonlinear signal analysis methods.
• To discuss their recent applications in electrocardiogram (ECG) signal analysis.
• To highlight the advantages of nonlinear techniques in ECG interpretation.

Main Methods:

• Reconstructed phase space analysis
• Lyapunov exponents
• Correlation dimension
• Detrended fluctuation analysis (DFA)
• Recurrence plot
• Poincaré plot
• Approximate entropy
• Sample entropy

Main Results:

• Nonlinear methods offer advanced capabilities for ECG signal analysis.
• These methods provide deeper insights into complex cardiac and autonomic functions.
• The reviewed techniques show significant potential for improved diagnostic accuracy.

Conclusions:

• Nonlinear analysis methods are crucial for comprehensive ECG interpretation.
• Their application enhances the understanding of heart electrophysiology and autonomic function.
• Further research into these methods promises advancements in noninvasive cardiac diagnostics.