Classification of cardiac arrhythmia using a convolutional neural network and bi-directional long short-term memory.

ABSTRACT: Cardiac arrhythmia is a leading cause of cardiovascular disease, with a high fatality rate worldwide. The timely diagnosis of cardiac arrhythmias, determined by irregular and fast heart rate, may help lower the risk of strokes. Electrocardiogram signals have been widely used to identify arrhythmias due to their non-invasive approach. However, the manual process is error-prone and time-consuming. A better alternative is to utilize deep learning models for early automatic identification of cardiac arrhythmia, thereby enhancing diagnosis and treatment. In this article, a novel deep learning model, combining convolutional neural network and bi-directional long short-term memory, is proposed for arrhythmia classification. Specifically, the classification comprises five different classes: non-ec

SUBMITTER: Hassan SU 

PROVIDER: S-EPMC9152186 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature