Explainable artificial intelligence for heart rate variability in ECG signal

Explainable artificial intelligence for heart rate variability in ECG signal

Electrocardiogram (ECG) signal is one of the most reliable methods to analyse the cardiovascular system. In the literature, there are different deep learning architectures proposed to detect various types of tachycardia diseases, such as atrial fibrillation, ventricular fibrillation, and sinus tachy...

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Bibliographic Details
Main Authors: Sanjana K., Sowmya V., Gopalakrishnan E.A., Soman K.P.
Format: Article
Language:English
Published: Wiley 2020-12-01
Series:Healthcare Technology Letters
Subjects:
signal classification
learning (artificial intelligence)
cardiovascular system
diseases
electrocardiography
medical signal processing
convolutional neural nets
tachycardia disease
deep learning model
atrial fibrillation
ventricular fibrillation
sinus tachycardia
deep learning models
cu-ventricular tachycardia data
cardiac diseases
deep learning architectures
ecg signal
electrocardiogram signal
mit-bih malignant ventricular ectopy database
rcnn model
Online Access:https://digital-library.theiet.org/content/journals/10.1049/htl.2020.0033

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https://digital-library.theiet.org/content/journals/10.1049/htl.2020.0033

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