Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals.

ABSTRACT: Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single- or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLS

SUBMITTER: Liang Y 

PROVIDER: S-EPMC7566908 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature