Persistent, unchanging, and non-reactive focal or generalized abnormal Slow Wave (SW) activities in an awake adult patient are examined pathologically. Although these waves in Electroencephalogram (EEG) are much less prominent than transient activities in some areas, it is not possible to understand them easily by looking at the EEG. For this reason, reliable computer programs that can sort out Slow Waves (SWs) correctly are needed. In this study, a new method based on MinPeakProminence that can detect abnormal SW activities was developed. To test the performance of the study, the data collected from Selcuk University Hospital (22 subjects - epilepsy and various neurological diseases) and Bonn Hospital (only normal A dataset) were used. Various statistical performance measurement methods were used to search the results. The results of this analysis revealed that the classification success, sensitivity and specificity values obtained with the SUH dataset were 96.5%, 93.3% and 96.1%, respectively. In the results of the experiments made with the Bonn dataset, 100% classification success was achieved. Besides, according to the analyses, it was found that SWs are frequently seen in the posterior regions of the brain, especially in the parietal and occipital regions in the SUH dataset.
Artificial Neural Networks (ANNs) that are the ability to learn from their environment in order to improve their performance are widely used in numerous applications. The Backpropagation (BP) Algorithm is one of the most popular and effective model of ANNs. However, since it uses gradient descent algorithm, which attempts to minimize the error of the network by moving gradient of the error curve, easily get trapped at local minima. In order to avoid this problem and to obtain a better classifier, we proposed an ANNs and Swarm Intelligence (SI) method where Artificial Bee Colony and Particle Swarm Optimization algorithms were operated for the Multilayer Perceptron Neural Network. Two Electroencephalogram (EEG) datasets were used to test to test the accuracy and success of the study performed. Compared to conventional-MLPNN, higher success values were obtained on each dataset with the proposed methods. Experimental results demonstrate that combined SI and MLPNN algorithm has been increased the success of BP algorithm by avoiding local minima. For ABC data, respectively, ABC-MLPNN and PSO-MLPNN methods, 79.00% and 75.50% respectively for Boston data and 91.67% and 88.33% respectively for Selcuk data were obtained. On the other hand, with the MLPNN algorithm, the success rate was 68.50% for Boston data and 81.67% for Selcuk data. These results show that the success of MLPNN algorithm significantly increases with the weights obtained by using SI. In addition to this, this study showed that the SI-MLPNN algorithm can be used on non-linear and highly complex EEG data.
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