Functional data analytic approach of modeling ECG T-wave shape to measure cardiovascular behavior

TL;DR

This paper introduces a functional data analysis method to model ECG T-wave shapes, providing interpretable parameters that could improve detection of cardiac abnormalities over traditional interval measurements.

Contribution

It develops a novel statistical approach that extracts a common T-wave shape and models deviations as a four-dimensional vector, enhancing robustness and interpretability.

Findings

Model distinguishes beat differences effectively

Parameters characterize T-wave shape physically

Potential for more robust cardiac biomarkers

Abstract

The T-wave of an electrocardiogram (ECG) represents the ventricular repolarization that is critical in restoration of the heart muscle to a pre-contractile state prior to the next beat. Alterations in the T-wave reflect various cardiac conditions; and links between abnormal (prolonged) ventricular repolarization and malignant arrhythmias have been documented. Cardiac safety testing prior to approval of any new drug currently relies on two points of the ECG waveform: onset of the Q-wave and termination of the T-wave; and only a few beats are measured. Using functional data analysis, a statistical approach extracts a common shape for each subject (reference curve) from a sequence of beats, and then models the deviation of each curve in the sequence from that reference curve as a four-dimensional vector. The representation can be used to distinguish differences between beats or to model shape changes in a subject's T-wave over time. This model provides physically interpretable parameters characterizing T-wave shape, and is robust to the determination of the endpoint of the T-wave. Thus, this dimension reduction methodology offers the strong potential for definition of more robust and more informative biomarkers of cardiac abnormalities than the QT (or QT corrected) interval in current use.