Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.

ABSTRACT:

Background and objective

Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database.

Methods

Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed p

SUBMITTER: Yildirim O 

PROVIDER: S-EPMC7477611 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature