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Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.

The present study aims to examine the effect of acupuncture stimulation of an acupuncture point (PC-6) and nonacupuncture point on electroencephalograms (EEGs) and electrocardiograms (ECGs). We used EEG in 10 healthy subjects to investigate cortical activation during stimulation of acupuncture points (neiguan: PC-6) and nonacupuncture points. Our most interesting finding was the marked differences of amplitude of EEG power between acupuncture points and nonacupuncture points stimulation. Wavelet transform was used as the EEG signal processing method, because it has advantages in a time domain and frequency domain characteristics analysis. EEGs were collected from 16 channels, and the α-wave (8–13 Hz), β-wave (13–30 Hz), θ-wave (4–8 Hz) and δ-wave (0.5–4 Hz) were used as standards for frequency bands. According to the experiment results, EEG signals increased considerably after acupuncture stimulation; in each frequency band, the average amplitude was higher after acupuncture stimulation; ECG heart rates were faster by at least 10% after acupuncture stimulation. Consequently, it will be possible to verify the function of acupuncture stimulation on neiguan (acupuncture points; PC-6) more effectively.