Paper Info
Title
Arrhythmia Recognition And Classification Through Deep Learning-Based Approach
Abstract
Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, which can be life-threatening. Electrocardiogram (ECG) is the principal diagnostic tool used to detect arrhythmias or heart abnormalities. It contains information about the different types of arrhythmias. However, due to the complexity and nonlinearity of ECG signals, such as the presence of noise, the time dependence of ECG signals and the irregularity of the heartbeat, it is troublesome to analyse ECG signals manually. Moreover, the interpretation of ECG signals is subjective and might vary among experts in the field. Therefore, an automatic, high-precision ECG recognition method is important to arrhythmia detection. For such, a method is proposed in this paper for arrhythmia classification, which is based on deep learning-based approach long short-term memory (LSTM), where five classes of arrhythmias as recommended by the Association for Advancement of Medical Instrumentation (AAMI) are analysed. The method has been tested on the MIT-BIH arrhythmia database with a number of useful performance evaluation measures, showing that is a promising and better performance than other artificial intelligence methods used.
Year
DOI
Venue
2019
10.1504/IJCSE.2019.101897
INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING
Keywords
DocType
Volume
electrocardiogram signal, long short-term memory, arrhythmia classification, artificial intelligence, deep learning
Journal
19
Issue
ISSN
Citations 
4
1742-7185
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Rui Zhou1206.92
Xue Li200.34
Binbin Yong3215.23
Zebang Shen4179.36
Chen Wang500.68
Qingguo Zhou610329.48
Yunshan Cao700.68
Kuan-ching Li8933122.44