1D Convolutional Neural Network for Atrial Fibrillation Detection

Abstract

Biomedical Informatics faces a paramount challenge: efficient and accurate Atrial Fibrillation detection from a single-lead electrocardiogram. A cutting-edge Convolutional Neural Network model is constructed on differences of consecutive heart rate samples and extracting datasets with specific arrhythmia in the training process, being unique in the feature engineering and model development.Our model resulted in an F1 Score of 98.14%, evaluated by 10-fold cross-validation using the label evaluation method, comparable to other best results. However, we have proven that label-based methods are substantially higher than those based on inter-patient training/testing data split with different datasets, such as those used as ECG benchmarks detecting AFIB and the corresponding evaluation methods based on duration instead labels, such as those used in standards evaluating ambulatory ECG monitoring devices. Evaluation of our model with the inter-patient data split and duration-based evaluation method showed a 94.16% F1 score, which complies with standards and is high above the standard recommendations. This affirms the high efficacy and potential of the developed solution.