Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• Distinguishing ischemic from non-ischemic transient ST segment events in long-term ambulatory electrocardiograms remains a challenge for current detection systems.
• Traditional ST segment analysis relies on single-point measurements, which are imprecise and susceptible to noise, limiting diagnostic accuracy.

Purpose of the Study:

• To develop a robust, noise-resistant method for delineating ST segment morphology changes.
• To create feature-vector time series for analyzing transient ST segment morphology.
• To evaluate the classification power of new transformation-based feature vectors for differentiating ischemic and non-ischemic events.

Main Methods:

• Developed a noise-resistant orthogonal-transformation based delineation method for ST segment morphology.
• Introduced a new Legendre Polynomials based Transformation (LPT) for ST segment analysis.
• Generated Karhunen and Loève Transformation (KLT) ST segment basis functions using data from the Long Term ST Database (LTST DB).

Main Results:

• The KLT and LPT methods effectively represent transient ST segment morphology categories.
• Classification accuracy reached 90% with KLT and 82% with LPT using a k-Nearest Neighbors classifier.
• New feature-vector time series were derived and contributed to the LTST DB.

Conclusions:

• The KLT and LPT transformations offer improved capabilities for automated ischemia detection.
• These methods provide new avenues for both human expert diagnostics and automated analysis of transient ST segment changes.
• The developed techniques enhance the precision and robustness of ischemia detection systems.