Automatic identification of different arrhythmias helps cardiologists better diagnose patients with cardiovascular diseases. Deep learning algorithms are used for the classification of multichannel ECG signals into different heart rhythms. The study dataset includes a cohort of 43101 12-lead ECG recordings with various lengths. Two options are tested to standardize the recordings length: zero padding and signal repetition. Downsampling the recordings to 100 Hz allow handling the problem of different sampling frequencies of data coming from different sources. We design a deep one-dimensional convolutional neural network (CNN) called VGG-ECG, a 13-layer fully CNN for multilabel classification. Our team is called MIndS and our approach achieved a challenge validation score of 0.368, and full test score of-0.128, placing us 38 out of 41 in the official ranking.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.