Real‐Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks

Real‐Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks

Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening...

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Bibliographic Details
Main Authors: Sandeep Chandra Bollepalli, Rahul K. Sevakula, Wan‐Tai M. Au‐Yeung, Mohamad B. Kassab, Faisal M. Merchant, George Bazoukis, Richard Boyer, Eric M. Isselbacher, Antonis A. Armoundas
Format: Article
Language:English
Published: Wiley 2021-12-01
Series:Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Subjects:
convolutional neural networks
false alarms
intensive care unit monitors
machine learning
multi‐class classification
Online Access:https://www.ahajournals.org/doi/10.1161/JAHA.121.023222

Internet

https://www.ahajournals.org/doi/10.1161/JAHA.121.023222

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