ArticleStatistical analysis and data mining2025
Extracting Genetically-Imputed Causal Features From ECG Data.
Article in Statistical analysis and data mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Atrial fibrillation (AF), a cardiac arrhythmia characterized by an abnormal and rapid heartbeat, has the potential to develop into stroke, heart failure, and, ultimately, mortality. The electrocardiogram (ECG) is a pivotal tool in the diagnosis of AF, offering a quick, cost-effective, and non-invasive mean to record the heart's electrical activity. Recent studies are increasingly engaged in the implementation of deep learning techniques for ECG feature extraction for AF prediction. In addition, the application of Mendelian randomization (MR) methodologies has been investigated to identify causal associations between genetically imputed pre-defined ECG characteristics and cardiovascular diseases, such as AF. DeepFEIVR, a non-linear extension of the classical instrumental variable (IV) regression model, was designed with the objective of extracting disease-associated causal features from high-dimensional data, such as neuroimaging data. In this article, we applied DeepFEIVR as well as its variant (with residual inclusion), DeepFEIVR-RI, to the large UK Biobank dataset. The application of DeepFEIVR and DeepFEIVR-RI showed that the genetic components in ECGs could contribute to the development of AF statistically significantly (
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