Machine learning-based information flow analysis of ECG signals for long QT syndrome

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Random Forest: 95.9% accuracy, 95.9% sensitivity, 92.9% specificity; SVM: 93.1% accuracy, 93.1% sensitivity, 92.0% specificity

• Primary Outcome: Diagnostic accuracy for LQTS detection using entropy-based ECG analysis

• Nnt Or Sensitivity: Sensitivity: 95.9% (RF), 93.1% (SVM); Specificity: 92.9% (RF), 92.0% (SVM)

• Confidence Interval: Not reported

Clinical Application

Requires specialized ECG signal processing and machine learning infrastructure, limiting immediate clinical implementation Would need validation in Australian population and integration with existing cardiac diagnostic pathways. TGA approval likely required for clinical use as diagnostic device. Patients suspected of having congenital long QT syndrome requiring diagnostic evaluation

Abstract

Objective. Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for non-invasive and cost-effective risk assessment tools. Biological systems, including the heart, exhibit complex nonlinear dynamics arising from interactions between their subsystems. Information-theoretic measures, particularly entropy-based methods, provide a framework to quantify these interactions. Using ECG recordings, we investigate information flow between heart rhythm and ventricular repolarization to identify potential markers of pathological alterations in cardiac electrical activity.Approach. Entropy-based measures of information transfer were derived from beat-to-beat ECG time series using a window-based approach and subsequently averaged at the subject level. These features were used as inputs to supervised machine learning models to discriminate patients with congenital long QT syndrome (LQTS) from healthy controls. Model performance was evaluated using repeated stratified train-test splits, and classification robustness was assessed across multiple runs using standard performance metrics, including the area under the receiver operating characteristic curve. The explainable artificial intelligence techniques were applied. SHapley Additive exPlanations were used to quantify the contribution of entropy-based features to the model predictions. This post-hoc explainability analysis enabled systematic assessment of feature importance while preserving the predictive performance of the models.Results. The proposed approach achieved high and stable classification performance across repeated validation runs. Both random forest (RF) and support vector machine (SVM) classifiers demonstrated high discrimination between LQTS patients and healthy controls, with consistently high area under the curve. For RF a mean accuracy of 95.9%, mean sensitivity of 95.9%, and mean specificity of 92.9% were achieved across repeated runs. For SVM the corresponding mean values were 93.1%, 93.1%, and 92.0%, respectively.Conclusions.Explainability analysis revealed a dominant contribution of multivariate and conditional information flow features compared with single-source entropy measures, highlighting the relevance of joint and conditional interactions in the classification patterns.

References

1. 1.Ozimek, M., Andrzejewska-Ozimek, M., & Petelczyc, M. (2026). Machine learning-based information flow analysis of ECG signals for long QT syndrome. Physiological Measurement. https://doi.org/10.1088/1361-6579/ae6969