Inter- and intra-patient ECG heartbeat classification for arrhythmia detection: A sequence to sequence deep learning approach.

ABSTRACT: Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models. We evaluated the proposed method on the MIT-BIH arrhythmia database, considering the intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The evaluation results for both paradigms show that

SUBMITTER: Mousavi S 

PROVIDER: S-EPMC7570975 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature