Performance comparison of CNN and LSTM algorithms for arrhythmia classification

One of the critical CVDs is cardiac arrhythmia and has caused significant fatalities. Recently, deep learning models are utilized for the classification of arrhythmia disease through electrocardiogram (ECG) signal analysis. Among the existing deep learning model, convolutional neural network (CNN) a...

Full description

Main Authors: Hassan, S.U., Zahid, M.S.M., Husain, K.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29886 /
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097558612&doi=10.1109%2fICCI51257.2020.9247636&partnerID=40&md5=8b994cd86204bda8dd6b6586894f43b9
http://eprints.utp.edu.my/29886/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.29886
recordtype eprints
spelling utp-eprints.298862022-03-25T03:05:34Z Performance comparison of CNN and LSTM algorithms for arrhythmia classification Hassan, S.U. Zahid, M.S.M. Husain, K. One of the critical CVDs is cardiac arrhythmia and has caused significant fatalities. Recently, deep learning models are utilized for the classification of arrhythmia disease through electrocardiogram (ECG) signal analysis. Among the existing deep learning model, convolutional neural network (CNN) and long short-term memory (LSTM) algorithms are extensively used for arrhythmia classification. However, there is a lack of study that analyzes the performance comparison of CNN and LSTM algorithms for arrhythmia classification. In this paper, the performance of CNN and LSTM algorithms for arrhythmia classification is compared for a publicly available dataset. Specifically, the MIT-BIH arrhythmia dataset is used and the performance is measured in terms of area under the curve (AUC) and receiver operating characteristic (ROC) curve. Analyzing the performance of these algorithms will further assist in the development of an enhanced deep learning model that offers improved performance. © 2020 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097558612&doi=10.1109%2fICCI51257.2020.9247636&partnerID=40&md5=8b994cd86204bda8dd6b6586894f43b9 Hassan, S.U. and Zahid, M.S.M. and Husain, K. (2020) Performance comparison of CNN and LSTM algorithms for arrhythmia classification. In: UNSPECIFIED. http://eprints.utp.edu.my/29886/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description One of the critical CVDs is cardiac arrhythmia and has caused significant fatalities. Recently, deep learning models are utilized for the classification of arrhythmia disease through electrocardiogram (ECG) signal analysis. Among the existing deep learning model, convolutional neural network (CNN) and long short-term memory (LSTM) algorithms are extensively used for arrhythmia classification. However, there is a lack of study that analyzes the performance comparison of CNN and LSTM algorithms for arrhythmia classification. In this paper, the performance of CNN and LSTM algorithms for arrhythmia classification is compared for a publicly available dataset. Specifically, the MIT-BIH arrhythmia dataset is used and the performance is measured in terms of area under the curve (AUC) and receiver operating characteristic (ROC) curve. Analyzing the performance of these algorithms will further assist in the development of an enhanced deep learning model that offers improved performance. © 2020 IEEE.
format Conference or Workshop Item
author Hassan, S.U.
Zahid, M.S.M.
Husain, K.
spellingShingle Hassan, S.U.
Zahid, M.S.M.
Husain, K.
Performance comparison of CNN and LSTM algorithms for arrhythmia classification
author_sort Hassan, S.U.
title Performance comparison of CNN and LSTM algorithms for arrhythmia classification
title_short Performance comparison of CNN and LSTM algorithms for arrhythmia classification
title_full Performance comparison of CNN and LSTM algorithms for arrhythmia classification
title_fullStr Performance comparison of CNN and LSTM algorithms for arrhythmia classification
title_full_unstemmed Performance comparison of CNN and LSTM algorithms for arrhythmia classification
title_sort performance comparison of cnn and lstm algorithms for arrhythmia classification
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097558612&doi=10.1109%2fICCI51257.2020.9247636&partnerID=40&md5=8b994cd86204bda8dd6b6586894f43b9
http://eprints.utp.edu.my/29886/
_version_ 1741197314871001088
score 11.62408

Similar Items