Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143836
Title: Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types
Authors: Oh, Shu Lih
Ng, Eddie Yin Kwee
Tan, Ru San
Acharya, U. Rajendra
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Oh, S. L., Ng, E. Y. K., Tan, R. S., & Acharya, U. R. (2019). Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types. Computers in Biology and Medicine, 105, 92–101. doi:10.1016/j.compbiomed.2018.12.012
Journal: Computers in biology and medicine
Abstract: Abnormality of the cardiac conduction system can induce arrhythmia - abnormal heart rhythm - that can frequently lead to other cardiac diseases and complications, and are sometimes life-threatening. These conduction system perturbations can manifest as morphological changes on the surface electrocardiographic (ECG) signal. Assessment of these morphological changes can be challenging and time-consuming, as ECG signal features are often low in amplitude and subtle. The main aim of this study is to develop an automated computer aided diagnostic (CAD) system that can expedite the process of arrhythmia diagnosis, as an aid to clinicians to provide appropriate and timely intervention to patients. We propose an autoencoder of ECG signals that can diagnose normal sinus beats, atrial premature beats (APB), premature ventricular contractions (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB). Apart from the first, the rest are morphological beat-to-beat elements that characterize and constitute complex arrhythmia. The novelty of this work lies in how we modified the U-net model to perform beat-wise analysis on heterogeneously segmented ECGs of variable lengths derived from the MIT-BIH arrhythmia database. The proposed system has demonstrated self-learning ability in generating class activations maps, and these generated maps faithfully reflect the cardiac conditions in each ECG cardiac cycle. It has attained a high classification accuracy of 97.32% in diagnosing cardiac conditions, and 99.3% for R peak detection using a ten-fold cross validation strategy. Our developed model can help physicians to screen ECG accurately, potentially resulting in timely intervention of patients with arrhythmia.
URI: https://hdl.handle.net/10356/143836
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2018.12.012
Rights: © 2019 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

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