[Extraction and recognition of attractors in three-dimensional Lorenz plot].

Area of Science:

• Cardiology
• Signal Processing
• Complex Systems

Context:

• Long-time electrocardiogram (ECG) signal analysis is crucial for diagnosing cardiac arrhythmias.
• The Lorenz plot (LP) is a visualization tool for ECG analysis, but lacks automated attractor extraction.
• Understanding attractor dynamics in ECG signals can reveal arrhythmia mechanisms.

Purpose:

• To develop a methodology for automatic attractor extraction and recognition from 3D Lorenz plots (3DLP).
• To utilize homogeneously statistical properties of scatter point locations in 3DLP for attractor analysis.
• To validate the method's effectiveness in identifying arrhythmias from RR-interval time series.

Summary:

• A novel methodology for attractor extraction and recognition in 3DLP, using statistical properties of RR intervals, is presented.
• The method successfully extracts and automatically recognizes attractors, with the azimuth parameter (A) proving effective for differential diagnosis of extrasystoles.
• Validation on Holter data with premature complexes and other arrhythmias demonstrates the method's efficacy and broad applicability.

Impact:

• This method facilitates automatic diagnosis of cardiac arrhythmias by enabling attractor recognition in ECG signals.
• The azimuth parameter (A) derived from 3DLP provides a powerful index for differentiating atrial and ventricular extrasystoles.
• Integration into conventional ECG monitoring systems offers broad application prospects for improved cardiac arrhythmia analysis.