Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms

Juan Miguel Lopez Alcaraz, Ebenezer Oloyede, David Taylor, Wilhelm Haverkamp, Nils Strodthoff
2/7/2025
eess.SPcs.LG

No official code found. Best implementation: AI4HealthUOL/CardioDiag (7 stars, last commit 2025-03-22).

Abstract

Background: Electrocardiogram (ECG) analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders. Given the close connection between cardiovascular and neurocognitive health, ECG abnormalities may be present in individuals with co-occurring neurocognitive conditions. This highlights the potential of ECG as a biomarker to improve detection, therapy monitoring, and risk stratification in patients with neurocognitive disorders, an area that remains underexplored. Methods: We aim to demonstrate the feasibility to predict neurocognitive disorders from ECG features across diverse patient populations. We utilized ECG features and demographic data to predict neurocognitive disorders defined by ICD-10 codes, focusing on dementia, delirium, and Parkinson's disease. Internal and external validations were performed using the MIMIC-IV and ECG-View datasets. Predictive performance was assessed using AUROC scores, and Shapley values were used to interpret feature contributions. Results: Significant predictive performance was observed for disorders within the neurcognitive disorders. Significantly, the disorders with the highest predictive performance is F03: Dementia, with an internal AUROC of 0.848 (95% CI: 0.848-0.848) and an external AUROC of 0.865 (0.864-0.965), followed by G30: Alzheimer's, with an internal AUROC of 0.809 (95% CI: 0.808-0.810) and an external AUROC of 0.863 (95% CI: 0.863-0.864). Feature importance analysis revealed both known and novel ECG correlates. ECGs hold promise as non-invasive, explainable biomarkers for selected neurocognitive disorders. This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications, including early detection and personalized monitoring.

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Code Implementations(1)

AI4HealthUOL/CardioDiagCommunity100%

This is the official repository for CardioDiag. A machine learning framework for the diagnosis of non-cardiac conditions.

73PythonAug 21, 20241 years ago

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Cite this paper

@article{alcaraz2025explainable,
  title  = {Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms},
  author = {Juan Miguel Lopez Alcaraz and Ebenezer Oloyede and David Taylor and Wilhelm Haverkamp and Nils Strodthoff},
  year   = {2025},
  eprint = {2502.04918},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.04918v2}
}

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