Please use this identifier to cite or link to this item:
Title: Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats
Authors: Oh, Shu Lih
Ng, Eddie Yin Kwee
Tan, Ru San
Acharya, U. Rajendra
Keywords: Engineering::Mechanical engineering
Issue Date: 2018
Source: Oh, S. L., Ng, E. Y. K., Tan, R. S., & Acharya, U. R. (2018). Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Computers in biology and medicine, 102, 278-287. doi:10.1016/j.compbiomed.2018.06.002
Journal: Computers in Biology and Medicine 
Abstract: Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG.
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2018.06.002
Rights: © 2018 Elsevier Ltd. All rights reserved. This paper was published in Computers in Biology and Medicine and is made available with permission of Elsevier Ltd.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

Files in This Item:
File Description SizeFormat 
Automated diagnosis of arrhythmia.pdf1 MBAdobe PDFView/Open


Updated on Jan 15, 2021


Updated on Jan 13, 2021

Page view(s)

Updated on Jan 17, 2021

Download(s) 20

Updated on Jan 17, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.