DRGCN-BiLSTM: An Electrocardiogram Heartbeat Classification Using Dynamic Spatial-Temporal Graph Convolutional and Bidirectional Long-Short Term Memory Technique

An automated cardiac rhythm classification using electrocardiograms is crucial for accurate and timely diagnosis of cardiovascular disease. Recent advances in deep learning have facilitated automated arrhythmias recognition, surpassing traditional ECG methods that depend on manual feature extraction...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on consumer electronics Vol. 71; no. 1; pp. 579 - 593
Main Authors:	Sharma, Neenu, Joshi, Deepak
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Arrhythmia arrhythmias classification Artificial neural networks Automation Bidirectional long short term memory BiLSTM Cardiac arrhythmia Classification Deep learning Diagnosis Electrocardiogram Electrocardiography Feature extraction graph convolutional network Heart Heart beat Machine learning Pattern recognition Support vector machines
ISSN:	0098-3063, 1558-4127
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!