Explainable and externally validated machine learning for neurocognitive diagnosis via ECGs.

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.

Aims

We aimed to demonstrate the feasibility of predicting neurocognitive disorders from ECG features across diverse patient populations.

Methods

ECG features and demographic data were used to predict neurocognitive disorders, as defined by the International Classifi

SUBMITTER: Lopez Alcaraz JM 

PROVIDER: S-EPMC12557730 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature