🤖 AI Summary
This study addresses the limitations of conventional electrocardiogram (ECG) screening, which relies on manual interpretation and often fails to detect subtle or progressive morphological changes, leading to missed early abnormalities. The authors propose an interpretable, motif-based framework that extracts representative cardiac cycles within a fixed window using dynamic time warping (DTW) and aligns them with fiducial points for morphological visualization. They introduce three novel metrics of morphological drift: deviation from normal sinus rhythm, divergence from an individual’s personalized baseline, and motif instability index, enabling longitudinal monitoring and early warning. Evaluated on the MIT-BIH and PTB-XL datasets, the method significantly discriminates between normal and abnormal ECGs (p<0.01 and p<1e-4, respectively), achieving a Cliff’s delta as high as 0.93, thereby demonstrating strong clinical potential and validity.
📝 Abstract
Electrocardiography (ECG) remains central to cardiovascular screening, yet interpretation remains largely manual and episodic. Clinical practice relies on brief resting ECGs and, when required, long-duration ambulatory recordings, both generating data that require resource-intensive review. Consequently, subtle morphological changes or progressive drift preceding clinically apparent abnormalities may go unnoticed. We propose a motif-based framework that defines beat-aligned ECG motifs as interpretable cardiac signatures and quantifies morphological drift and deviation across short and long-term monitoring. Motifs are representative cardiac cycles capturing dominant morphology. We introduce three interpretable drift metrics: deviation from a normal sinus rhythm (NSR), deviation from a personalised baseline, and a motif instability index. Motifs are extracted by selecting beats that minimise Dynamic Time Warping (DTW) distance within fixed windows. We evaluate these metrics on short (PTB-XL) and long-duration (MIT-BIH Arrhythmia) ECG datasets. Interpretability is achieved through representative motif overlays and fiducial-based visualisations, enabling direct inspection of morphological changes. In MIT-BIH, the proposed metrics significantly separated predominantly normal from arrhythmic subjects (p<0.01). In PTB-XL, NSR deviation distinguished normal from abnormal ECGs across major diagnostic subtypes (p<1e-4, Cliff's delta up to 0.93). ECG motifs provide an interpretable representation of cardiac morphology, supporting scalable longitudinal monitoring and early detection of morphology-driven change.