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Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE

Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis system minimizes the mortality rate of cardiac disease patients. Cardiac arrhythmia detection is one of the most challenging tasks, because the variations of electrocardiogram(ECG) signal are very small, which cannot be detected by human eyes. In this study, an 11-layer deep convolutional neural network model is proposed for classification of the MIT-BIH arrhythmia database into five classes according to the ANSI–AAMI standards. In this CNN model, we designed a complete end-to-end structure of the classification method and applied without the denoising process of the database. The major advantage of the new methodology proposed is that the number of classifications will reduce and also the need to detect, and segment the QRS complexes, obviated. This MIT-BIH database has been artificially oversampled to handle the minority classes, class imbalance problem using SMOTE technique. This new CNN model was trained on the augmented ECG database and tested on the real dataset. The experimental results portray that the developed CNN model has better performance in terms of precision, recall, F-score, and overall accuracy as compared to the work mentioned in the literatures. These results also indicate that the best performance accuracy of 98.30% is obtained in the 70:30 train-test data set. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Australasian Physical & Engineering Sciences in Medicine Springer Journals

Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE

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References (66)

Publisher
Springer Journals
Copyright
Copyright © 2019 by Australasian College of Physical Scientists and Engineers in Medicine
Subject
Biomedicine; Biomedicine, general; Biological and Medical Physics, Biophysics; Medical and Radiation Physics; Biomedical Engineering and Bioengineering
ISSN
0158-9938
eISSN
1879-5447
DOI
10.1007/s13246-019-00815-9
Publisher site
See Article on Publisher Site

Abstract

Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis system minimizes the mortality rate of cardiac disease patients. Cardiac arrhythmia detection is one of the most challenging tasks, because the variations of electrocardiogram(ECG) signal are very small, which cannot be detected by human eyes. In this study, an 11-layer deep convolutional neural network model is proposed for classification of the MIT-BIH arrhythmia database into five classes according to the ANSI–AAMI standards. In this CNN model, we designed a complete end-to-end structure of the classification method and applied without the denoising process of the database. The major advantage of the new methodology proposed is that the number of classifications will reduce and also the need to detect, and segment the QRS complexes, obviated. This MIT-BIH database has been artificially oversampled to handle the minority classes, class imbalance problem using SMOTE technique. This new CNN model was trained on the augmented ECG database and tested on the real dataset. The experimental results portray that the developed CNN model has better performance in terms of precision, recall, F-score, and overall accuracy as compared to the work mentioned in the literatures. These results also indicate that the best performance accuracy of 98.30% is obtained in the 70:30 train-test data set.

Journal

Australasian Physical & Engineering Sciences in MedicineSpringer Journals

Published: Nov 14, 2019

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