This paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the peaks falling into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5.
In this study, the effects of heart rate (HR) normalization in the analysis of the heart rate variability (HRV) were investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, the best accuracy performances of optimal combination of standard short-term HRV measures and of HR-normalized short-term HRV measures are compared. A genetic algorithm is used to select the best features from among all possible combinations of these measures. A k-nearest-neighbour (KNN) classifier is used to evaluate the performances of the feature combinations in classifying these two data groups. The results imply that using both min-max and HR normalization improves the performance of the classification. The maximum accuracy is achieved as 93.98 per cent using k = 3 and k = 5 for the KNN classifier with the perfect positive predictivity values.
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