Real-time classification of EEG signals implemented on DSPIC for the diagnosis of epilepsy

In order to improve tools for understanding the complicated brain behavior of epilepsy disease, we analyze the ElectroEncephaloGram (EEG) signal which is a medical imaging technique for measuring the electrical activity of the brain. For the detection of the epileptic activity, several traditional methods are so tedious. In this context, we proposed an automated diagnostic system to examine cerebral epileptiform discharges. This dissertation divided into two steps: in the first step, we extracted features in time-domain features of the EEG signal, then we optimized features by the degree of characterization noted “J”, these attributes are ready to be integrated as input for the statistic classification method. In the second step, we implemented the results found previously on DSPIC in real time. In this paper, we used an EEG dataset collected from Bonn University open source database which contains 100 EEG signals for normal subjects and 100 for epileptic ones. The results show that the accuracy rate of statistic classification is 97.5% using optimized input features which are most performing than the results obtained by all extracted features with an accuracy rate equal 88%.

[1]  Ms. Ashwini D. Bhople.  Fast Fourier Transform Based Classification of Epileptic Seizure Using Artificial Neural Network , 2012 .

[2]  S. Dupont.  Les syndromes épileptiques de l’enfant et de l’adolescent, Roger. Éditions J Libbey (2003), 544 p, ISBN: 086196 632 5 , 2004 .

[3]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[4]  Kees Van Der Heijden,et al.  Image Based Measurement Systems , 2007 .

[5]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[6]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Belaid Bouikhalene,et al.  Epilepsy Seizure Detection Using Autoregressive Modelling and Multiple Layer Perceptron Neural Network , 2015 .

[8]  Andreas Schulze-Bonhage,et al.  Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification , 2015, Int. J. Neural Syst..

[9]  Yi Chai,et al.  Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM , 2014, Biomed. Signal Process. Control..

[10]  Sumithra J. Mandrekar,et al.  Statistical Methods in Diagnostic Medicine using SAS ® Software , 2005 .

[11]  Paul Gerrard,et al.  Mechanisms of modafinil: A review of current research , 2007, Neuropsychiatric disease and treatment.

[12]  Jorge Prendes,et al.  Multivariate Bayesian classification of epilepsy EEG signals , 2016, 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP).

[13]  G. Lightbody,et al.  EEG-based neonatal seizure detection with Support Vector Machines , 2011, Clinical Neurophysiology.