Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-wave...

Area of Science:

• Cardiac Electrophysiology
• Biomedical Signal Processing
• Medical Imaging

Background:

• Electrocardiographic Imaging (ECGI) is a novel cardiac diagnostic tool with emerging clinical applications.
• Existing electrophysiological knowledge and signal processing techniques require adaptation for ECGI's unique spatial-temporal data.
• Previous work established spatial consistency in basic ECGI signal processing and demonstrated useful bipolar electrograms (EGMs).

Purpose of the Study:

• To demonstrate the utility of ECGI in cardiac electrophysiology through waveform and repolarization analysis.
• To analyze EGM waveform clustering in control and myocardial infarction subjects.
• To investigate T-wave alternans (TWA) properties in control and Long-QT syndrome (LQTS) subjects.

Main Methods:

• Analysis of unipolar and bipolar electrogram (EGM) waveform clustering.
• Assessment of T-wave alternans (TWA) in control and Long-QT syndrome (LQTS) subjects using three estimation methods.
• Spatial-temporal analysis of cardiac signals obtained via ECGI.

Main Results:

• Clustered EGM regions showed spatial consistency with infarcted tissue.
• Bipolar EGMs, with appropriate processing, maintained consistency and identified spatial-temporal regions.
• LQTS subjects exhibited higher spatial-temporal TWA variation compared to controls, consistent across multiple estimation methods.

Conclusions:

• Spatial-temporal analysis of EGMs using ECGI enhances clinical utility.
• Revisiting signal processing approaches is crucial for effectively utilizing ECGI data.
• ECGI provides valuable insights into cardiac electrophysiology and repolarization abnormalities.