Identification of Congestive Heart Failure Using Soft Decision Sub- Band Decomposition and Artificial Neural Networks
نویسنده
چکیده
A new identification method for screening patients with congestive heart failure (CHF) from normal controls is investigated in this paper using spectral analysis and artificial neural networks. This method is based on approximate spectral density estimation of RRI data using the soft decision sub-band decomposition technique. The data used in this work is obtained from MIT databases. A trial data set of 17 CHF and 53 normal subjects is used to obtain the classification features. The classification features are the spectral density of 6 different regions covering the whole spectrum of the RRI data obtained by 32 bands of the soft decision algorithm. The trial data set is used to train the neural network, which then used to test another test set of MIT data consisting of 12 CHF and 12 normal subjects. The accuracy of the classification is about 92%.
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