Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/55300
Title: Classification of cardiac patient states using artificial neural networks
Authors: Kannathal, N.
Acharya, U.R.
Lim, C.M.
Sadasivan, P.K. 
Krishnan, S.M.
Keywords: Electrocardiogram
Heart rate
Intensive care unit
Neural network
Radial basis function
Self-organizing map
Issue Date: 2003
Source: Kannathal, N.,Acharya, U.R.,Lim, C.M.,Sadasivan, P.K.,Krishnan, S.M. (2003). Classification of cardiac patient states using artificial neural networks. Experimental and Clinical Cardiology 8 (4) : 206-211. ScholarBank@NUS Repository.
Abstract: Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. © 2003 Pulsus Group Inc. All rights reserved.
Source Title: Experimental and Clinical Cardiology
URI: http://scholarbank.nus.edu.sg/handle/10635/55300
ISSN: 12056626
Appears in Collections:Staff Publications

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