ECG beat arrhythmia classification by using 1-d CNN in case of class imbalance
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Date
2019
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Open Access Color
Green Open Access
Yes
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
BIP! Indicators
Impulse
Top 10%
Influence
Average
Popularity
Top 10%
Abstract
In this study ECG arrhythmia types of non-ectopic (N) ventricular ectopic (V) unknown (Q) supraventricular ectopic (S) and fusion (F) were classified by using the convolutional neural network (CNN) architecture. QRS detection was performed on these ECG arrhythmias that downloaded from MIT-BIH database. An imbalanced number of beats was obtained for 5 different arrhythmia types. In order to reduce the effect of imbalance in statistical performance metrics data mining techniques such as recall of data were applied. It was aimed to increase the positive predictive value (PPV) rates of the classes which consist of a few instances. © 2020 Elsevier B.V. All rights reserved.
Description
Keywords
Arrhythmia, Cnn, Data Mining, Ecg, Imbalanced, Biomedical Engineering, Diseases, Electrocardiography, Neural Networks, Arrhythmia, Arrhythmia Classification, Class Imbalance, Convolutional Neural Network, Imbalanced, Mit-bih Database, Positive Predictive Values, Statistical Performance, Data Mining, Biomedical engineering, Diseases, Electrocardiography, Neural networks, Arrhythmia, Arrhythmia classification, Class imbalance, Convolutional neural network, Imbalanced, MIT-BIH database, Positive predictive values, Statistical performance, Data mining, ECG, CNN, Imbalanced, Arrhythmia, Data Mining
Fields of Science
03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Citation
WoS Q
Scopus Q
OpenCitations Citation Count
12
Source
2019 Medical Technologies Congress TIPTEKNO 2019
Volume
Issue
Start Page
1
End Page
4
PlumX Metrics
Citations
CrossRef : 12
Scopus : 14
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Mendeley Readers : 17
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