A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis using ECG Signal and Deep Learning
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Akash Kirodiwal et al., A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis using ECG Signal and Deep Learning, Computing in Cardiology 2020, https://www.cinc.org/2020/Program/accepted/225_CinCFinalPDF.pdf
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Abstract
Automated cardiac abnormality detection from an everexpanding 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 of 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 ”PhysioNet/Computing in Cardiology Challenge 2020” and obtained a challenge metric score of 0.337.