Atrial Fibrillation Burden Estimation Using Multi-Task Deep Convolutional Neural Network

Abstract

Atrial fibrillation (AF) burden is defined as the percentage of time the patient is in AF rhythm during a certain monitoring period. The accurate AF burden estimation from the long-term electrocardiogram (ECG) recordings provides improved prognostic value compared to the traditional binary AF diagnosis (present or absent) using the snapshot ECG. However, the presence of frequent ectopic beats and different noise levels pose a challenge for precise AF burden estimation. For the first time, we hypothesized that a multi-task deep convolutional neural network (MT-DCNN) could accurately estimate the AF burden from the long-term ambulatory ECG recordings. The model consists of AF detection as a primary task and reconstruction of ECG sequence as an auxiliary task using DCNNs. The auxiliary task regularizes the model to learn robust feature representations for efficient AF detection, thereby aiding accurate AF burden estimation. The MT-DCNN is compared with the state-of-the-art rhythm-based, rhythm- and morphology-based approaches. The models are developed and evaluated on a large database of n=84 patients, totaling t=1,900 h of continuous ECG recordings from the LTAF database. The generalization performance is evaluated on three independent datasets (AFDB, NSRDB and LTNSRDB) of n=48 subjects, totaling t=761 h of continuous ECG recordings. On the LTAF test set, the proposed model exhibits a lesser mean absolute AF burden estimation error of 2.8 % over the rhythm-based and the rhythm- and morphology-based approaches. In addition, the MT-DCNN provides better generalization results on independent test datasets and at different noise levels. The results demonstrate that the MT-DCNN can accurately estimate the AF burden from long-term ECG recordings; thus, it has the potential to be used in remote patient monitoring applications for improved diagnosis, phenotyping, and management of AF.