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

Simple item page
dc.contributor.authorKirodiwal, Akash
dc.contributor.authorSrivastava, Apoorva
dc.contributor.authorDash, Ashutosh
dc.contributor.authorSaha, Ayantika
dc.contributor.authorPenaganti, Gopi Vamsi
dc.contributor.authorPratiher, Sawon
dc.contributor.authorAlam, Sazedul
dc.contributor.authorPatra, Amit
dc.contributor.authorGhosh, Nirmalya
dc.contributor.authorBanerjee, Nilanjan
dc.date.accessioned2020-11-03T19:56:10Z
dc.date.available2020-11-03T19:56:10Z
dc.description.abstractAutomated 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.en_US
dc.description.urihttps://www.cinc.org/2020/Program/accepted/225_CinCFinalPDF.pdfen_US
dc.format.extent4 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2w8wp-bnsu
dc.identifier.citationAkash 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.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/20008
dc.language.isoen_USen_US
dc.publisherComputing in Cardiologyen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectbio-toolkiten_US
dc.subjectcardiacen_US
dc.subjectcardiac abnormality diagnosisen_US
dc.subjectDeep Learningen_US
dc.titleA Bio-toolkit for Multi-Cardiac Abnormality Diagnosis using ECG Signal and Deep Learningen_US
dc.typeTexten_US

Files

License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection