Ectopic beats and their influence on the morphology of subsequent waves in the electrocardiogram

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• Ventricular ectopic beats (VEBs) are associated with cardiac events.
• Heart rate turbulence (HRT) is a known predictor of sudden cardiac death (SCD) post-myocardial infarction (MI).
• Accurate detection and analysis of VEBs and HRT are crucial for risk stratification.

Purpose of the Study:

• To develop a reliable algorithm for detecting and classifying VEBs.
• To propose a novel approach for analyzing HRT using system theory.
• To investigate the influence of VEBs on T-wave morphology and introduce Morphological Heart Rate Turbulence (MHRT).

Main Methods:

• Electrocardiogram (ECG) processing using advanced filtering and artifact detection.
• Multichannel analysis for accurate beat annotation.
• Support vector machine (SVM) for ectopic beat classification.
• System identification techniques to analyze HRT.
• T-wave morphology analysis to quantify MHRT.

Main Results:

• An accurate algorithm for VEB detection and classification was developed.
• HRT was modeled as a second-order system, with parameters estimated.
• A significant influence of VEBs on T-wave morphology was observed.
• Morphological Heart Rate Turbulence (MHRT) was identified and quantified, showing an exponential dynamic process.

Conclusions:

• The developed algorithm provides accurate VEB detection and classification.
• The novel HRT analysis offers insights into cardiac system dynamics.
• MHRT analysis reveals the impact of VEBs on cardiac repolarization.
• These findings can improve SCD risk stratification in post-MI patients.