A novel data reduction method: Distance based data reduction and its application to classification of epileptiform EEG signals

ObjectiveData reduction methods are a crucial step affecting both performance and computation time of classification systems in pattern recognition applications such as medical decision making systems, intelligent control, and data clustering. The aim of this study is both to increase the classification accuracy and decrease the computation time of classifier system on the classification of epileptiform EEG signals.MethodsIn this study, we have proposed a novel data reduction method based on distances between groups data double in all dataset and applied this method to the classification of epileptiform EEG signals. The feature extraction methods including autoregressive (AR), discrete Fourier transform (DFT), and discrete wavelet transform (DWT), distance based data reduction, and C4.5 decision tree classifier have been combined to classify the epileptiform EEG signals. As feature extraction part AR, DFT, and DWT methods have been used to determine the features about EEG signals including epileptic seizure patients and eyes open volunteers. As data pre-processing part, distance based data reduction that is proposed firstly by us has been used to reduce data determined by spectral analysis methods (AR, DFT, and DWT). As final part called classification, C4.5 decision tree classifier has been used to classify reduced epileptiform EEG signals.ResultsTo validate and test the proposed data reduction, the classification accuracy, sensitivity, and specifity analysis, computation time, 10-fold cross-validation, and 95% confidence intervals have been used in this study. Six different combined methods have been used to classify the epileptiform EEG signal. These methods are (i) combining DFT and C4.5 decision tree classifier (DCT), (ii) combining DFT, distance based data reduction, and C4.5 DCT, (iii) combining AR and C4.5 DCT, (iv) combining AR, distance based data reduction, and C4.5 DCT, (v) combining DWT and C4.5 DCT, and (vi) combining DWT, distance based data reduction, and C4.5 DCT. The classification accuracies and computation times obtained by these methods are 99.02% – 79 s, 99.12% – 47 s, 99.32% – 65 s, 98.94% – 45 s, 92.00% – 52.06 s, and 89.50% – 29.9 s.ConclusionsThese results have shown that the proposed distance based data reduction method has produced very promising results with respect to both classification accuracy and computation time for classifying the epileptiform EEG signals. Also, proposed hybrid systems can be used to detect the epileptic seizure.