Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

Seera, M. and Lim, C.P. and Liew, W.S. and Lim, E. and Loo, C.K. (2015) Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models. Expert Systems with Applications, 42 (7). pp. 3643-3652. ISSN 0957-4174

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Abstract

In this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets. (C) 2014 Elsevier Ltd. All rights reserved.

Item Type: Article
Additional Information: Cc2nt Times Cited:0 Cited References Count:67
Uncontrolled Keywords: Machine learning, data classification, medical signals, electrocardiogram, auscultatory blood pressure, support vector machines, feature-selection algorithm, neural-network model, arrhythmia classification, ecg arrhythmia, comparing performances, logistic-regression, biomedical signals, wavelet transform, decision tree.
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 17 Sep 2015 03:38
Last Modified: 17 Sep 2015 03:38
URI: http://eprints.um.edu.my/id/eprint/13942

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