• Archana
• Himani Sikarwar

Abstract—Epilepsy is a neurological disease with a prevalence of about 1-2% of the global population. Epilepsy is a neurological disorder that is caused by a chronic abnormality of brain discharge. Monitoring brain activity through electroencephalography (EEG) has become an important tool for the diagnosis of epilepsy. EEG recordings in epileptic patients show two types of abnormal activity: abnormal signals recorded during the epileptic seizure; and seizures, activities recorded during the epileptic seizure. The main goal of our research is to analyze the acquired EEG signals using signal processing tools (such as wavelet transforms) and classify them into different categories. The features from the EEG are extracted using statistical analysis of parameters obtained by wavelet transform.Total 300 EEG data subjects were analyzed. These data were grouped in three classes’i.e, Normal patient class, Epileptic patient class and epileptic patient during non-seizure zone respectively. In order to achieve this we have applied a backpropgation based neural network classifier.After feature extraction secondary goal is to improve the accuracy of classification. 100 subjects from each set were analysed for feature extraction and classification and data were divided in training, testing and validation of proposed algorithm. Index Terms—EEG, Epilepsy, Wavelet transform; Feature Extraction, Neural network, Backpropogation Neural Network.

"Development of Soft Computing Based Expert system for Diagnosis and Analysis of Epileptic Patients", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.20-23, June 2019, Available :http://www.jetir.org/papers/JETIR1907308.pdf

"Development of Soft Computing Based Expert system for Diagnosis and Analysis of Epileptic Patients", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp20-23, June 2019, Available at : http://www.jetir.org/papers/JETIR1907308.pdf