Affiliations: [a] Department of Computer Engineering, Fahad Bin Sultan University, Tabuk, KSA | [b] Department of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, Sultanate of Oman
Correspondence:
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Address for correspondence: A. Hossen, Department of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khoud, 123 Muscat, Sultanate of Oman. Fax: +968 24413454; E-mail: abhossen@squ.edu.om.
Abstract: A new technique for identification of patients with congestive heart failure (CHF) from normal controls is investigated in this paper using spectral analysis and neural networks. The identification system consists of two parts: feature extraction part and classification part. The feature extraction part uses the method of approximate spectral density estimation of R-R-Intervals (RRI) data by implementing the soft decision sub-band decomposition technique. In the classification part, two different methods of machine learning approaches with neural networks are implemented and compared in their performances. Those approaches are: supervised neural network (back-propagation) and unsupervised neural network (Kohonen self organizing maps). The data used in this work is obtained from Massachusetts Institute of Technology (MIT) databases. A data set of 17 CHF and 53 normal subjects is used as original training data set, while another set of 12 CHF and 12 normal subjects is used as original test data set. The classification features are the spectral density of 6 different regions covering the whole spectrum of the RRI data obtained by 32-bands soft decision algorithm. A larger training data set, which is obtained by simulating 1000 CHF and 1000 normal subjects according to the spectral features obtained from the original training data, is used to train the neural network. The neural network is used then to test another simulated data set of the same size of the training date set (simulated according to the spectral features obtained from the original test data set). The accuracy of the classification is found to be about 83.65% and 91.43% with supervised neural networks and unsupervised neural networks respectively.
