A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ECG Signal and Deep Learning

Automated cardiac abnormality detection from an ever-expanding number of electrocardiogram (ECG) records has been widely used to assist physicians in the clinical diagnosis of a variety of cardiovascular diseases. Over the last few years, deep learning (DL) architectures have achieved state-of-the-art performances in various biomedical applications. In this work, we propose a bio-toolkit based on the DL framework comprising stacked convolutional and long short term memory neural network blocks for multi-label ECG signal classification. Our team participated under the name “Cardio-Challengers” in the “Phy-sioNet/Computing in Cardiology Challenge 2020” and obtained a challenge metric score of 0.337 in the validation data set and 0.258 in the full test data, placing us 16^th out of 41 teams in the official ranking.