A Low-Power Co-Processor to Predict Ventricular Arrhythmia for Wearable Healthcare Devices-Reference-Cited by-同舟云学术

A Low-Power Co-Processor to Predict Ventricular Arrhythmia for Wearable Healthcare Devices

Published:2024-09 Issue:9 Volume:32 Page:1672-1683
ISSN:1063-8210
Container-title:IEEE Transactions on Very Large Scale Integration (VLSI) Systems
language:
Short-container-title:IEEE Trans. VLSI Syst.

Author:

Janveja Meenali¹^ORCID,Parmar Rushik¹^ORCID,Dash Srichandan¹,Pidanic Jan²^ORCID,Trivedi Gaurav¹^ORCID

Affiliation:

1. Department of Electronics and Electrical Engineering, Indian Institute of Technology at Guwahati, Guwahati, Assam, India

2. Department of Electrical Engineering, University of Pardubice, Pardubice, Czech Republic

Funder

Inter-Excellence Funds of the Ministry of Education, Youth and Sports, Czech Republic, “Artificial Intelligence Enabled Smart Contactless Technology Development for Smart Fencing”

Electronics and ICT Academy, Indian Institute of Technology Guwahati, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/92/10648891/10589544.pdf?arnumber=10589544

Reference34 articles.

1. ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: A report of the American College of Cardiology/American Heart Association task force and the European society of cardiology committee for practice guidelines (writing committee to develop guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac Death) developed in collaboration with the European heart rhythm association and the heart rhythm society;Zipes,2006

2. SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification

3. Robust Arrhythmia Classification Based on QRS Detection and a Compact 1D-CNN for Wearable ECG Devices

4. Multilevel Classification and Detection of Cardiac Arrhythmias With High-Resolution Superlet Transform and Deep Convolution Neural Network