Abstract
We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic classification of cardiac arrhythmia were proposed and to our knowledge none of them can distinguish between these two. We discuss reasons why deep learning may not yield satisfactory results for this task. We generate new and clinically interpretable features using mathematical optimization for subsequent use within a machine learning (ML) model. These features are generated from the same input data by solving an additional regression problem with complicated combinatorial substructures. The resultant can be seen as a novel machine learning model that incorporates expert knowledge on the pathophysiology of atrial flutter. Our approach achieves an unprecedented accuracy of 82.84% and an area under the receiver operating characteristic (ROC) curve of 0.9, which classifies as “excellent” according to the classification indicator of diagnostic tests. One additional advantage of our approach is the inherent interpretability of the classification results. Our features give insight into a possibly occurring multilevel atrioventricular blocking mechanism, which may improve treatment decisions beyond the classification itself. Our research ideally complements existing textbook cardiac arrhythmia classification methods, which cannot provide a classification for the important case of AFib↔AFlu. The main contribution is the successful use of a novel mathematical model for multilevel atrioventricular block and optimization-driven inverse simulation to enhance machine learning for classification of the arguably most difficult cases in cardiac arrhythmia. A tailored Branch-and-Bound algorithm was implemented for the domain knowledge part, while standard algorithms such as Adam could be used for training.
Highlights
Automatic classification of cardiac arrhythmiasThe recent success of machine learning (ML) algorithms to classify cardiac arrhythmias is impressive [1]
It is usually not clear if the classification results [2,3,4,5] were due to heart rate variability, the particular shape of the electrocardiogram (ECG) curve, or a mix of both
We propose to extend and complement the mentioned approaches with generated features based on a pathophysiological rationale allowing classification of atrial fibrillation (AFib)$atrial flutter (AFlu)
Summary
Automatic classification of cardiac arrhythmiasThe recent success of ML algorithms to classify cardiac arrhythmias is impressive [1]. The algorithms are not able to provide explanations for the pathophysiological basis of classification outcomes, as they are unable to reveal the functional dependencies between data inputs and classes.”. We agree with this point of view. Wavelets have been used to extract features automatically [6], but this approach is limited to easy classification cases and does not directly provide physiologically interpretable features. Parameters such as atrial cycle length are not provided, they may be relevant for treatment decisions [7]
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