Abstract
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 ( p values < ). Another contribution of this article is an extension to both DeepFEIVR and DeepFEIVR-RI to accommodate a large number of IVs. A comparison of results from DeepFEIVR and DeepFEIVR-RI, based on various choices of IVs, was conducted. Furthermore, we applied a recent algorithm called dnn-loc, enabling a visual examination on specific ECG components as extracted causal features for AF, thus advancing the understanding of the etiology of AF.