Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform

Performance Evaluation of Different Classifier for Eye State Prediction Using EEG Signal

“…Brain waves a usually slowest during sleep. The proposed work is applicable for finding eye state classification in the area of infant sleep walking state identification [12], driving drowsiness detection [13], epileptic seizure detection [14], classification of bipolar mood disorder (BMD) and attention deficit hyperactivity disorder (ADHD) patients [15], stress features identification [16], human eye blinking detection [17].…”

Performance of Classification Techniques on Medical Datasets

“…Yeo et al successfully used support vector machines (SVMs) to detect drowsiness during car driving by eye blink [8]. Moreover, a hybrid system based on decision tree classifier and fast Fourier transform was applied to the detection of epileptic seizure by Polat and Güneş [9]. Sulaiman et all.…”

Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm

“…Vasicek et al had a test for normality based on sample entropy [13]. Kemal detected epileptic seizure in EEG signals using a hybrid system based on a decision tree classifier and fast Fourier transform (FFT) and obtained 98.72% classification accuracy [14]. Suryannarayana et al introduced a most promising pattern recognition technique called crosscorrelation aided SVM based classifier and achieved classification accuracy on normal and epileptic EEGs as high as 95.96% [15].…”