Motif-based morphology signatures for interpretable ECG screening and monitoring

Researchers propose a motif-based framework for ECG analysis that identifies interpretable cardiac signatures through beat-aligned morphology patterns, enabling early detection of cardiovascular abnormalities. Using Dynamic Time Warping to extract representative cardiac cycles, the method quantifies morphological drift across short and long-term monitoring with three metrics: deviation from normal sinus rhythm, personalized baseline deviation, and motif instability. Testing on standard ECG datasets demonstrates significant separation between normal and arrhythmic subjects with high statistical significance.

This research addresses a critical limitation in cardiovascular medicine: the inability to detect subtle morphological changes before they manifest as clinical abnormalities. Current ECG screening relies primarily on manual interpretation of brief resting recordings, leaving progressive cardiac drift undetected until symptoms emerge. The proposed motif-based framework shifts this paradigm by establishing quantifiable signatures of normal cardiac morphology and tracking deviations over time.

The technical approach leverages Dynamic Time Warping to identify representative beats that capture dominant morphology patterns, creating interpretable cardiac signatures rather than black-box model outputs. This distinction matters significantly for clinical adoption, as physicians can directly visualize morphological changes through motif overlays and fiducial-based visualizations. The three proposed metrics address complementary monitoring needs: NSR deviation for absolute abnormality detection, personalized baseline deviation for individual-level change tracking, and motif instability for detecting inconsistency in cardiac patterns.

Validation on two standard datasets—PTB-XL for short-duration ECGs and MIT-BIH for long-duration ambulatory recordings—demonstrates clinical relevance. The statistical separation between normal and abnormal populations (p<1e-4 with effect sizes up to 0.93) exceeds typical diagnostic thresholds. This positions the framework as potentially transformative for longitudinal cardiac monitoring, particularly relevant as remote monitoring and wearable devices generate increasing volumes of ECG data requiring automated screening.

The work's impact extends beyond academia into clinical practice infrastructure. Healthcare systems struggling with ECG interpretation bottlenecks could implement these metrics as scalable pre-screening tools, reducing radiologist workload while enabling earlier intervention. Integration with continuous monitoring systems creates opportunities for detecting disease progression at subclinical stages.

• →Motif-based framework enables interpretable ECG analysis by extracting representative cardiac cycles and tracking morphological drift over time.
• →Three quantifiable metrics (NSR deviation, baseline deviation, instability index) provide complementary approaches to abnormality detection and longitudinal monitoring.
• →Statistical validation shows significant separation between normal and arrhythmic subjects with effect sizes exceeding 0.93 on standard datasets.
• →Interpretable visualization through motif overlays supports clinical adoption by allowing direct inspection of morphological changes rather than black-box predictions.
• →Framework scalability addresses growing demands from remote monitoring and wearable ECG devices generating high-volume longitudinal data.