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Auto-Regressive based Feature Extraction and Classification of Epileptic EEG using Artificial Neural Network

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Author(s): Ashish Raj | Manoj Kumar Bandil | D B V Singh | Dr.A.K Wadhwani

Journal: International Journal of Advanced Research in Computer Science
ISSN 0976-5697

Volume: 04;
Issue: 09;
Start page: 124;
Date: 2013;
Original page

Keywords: EEG | Spike | detection | Wavelet | transform | Neural | network | Auto-Regression | Inter-Quartile | Range | Epilpepsy | Seizure

ABSTRACT
Epilepsy is a neurological condition in which is due to chronic abnormal bursts of electrical discharge in the brain. Monitoring brain activity through the electroencephalogram (EEG) has become an important tool in the diagnosis of epilepsy. The EEG recordings of patients suffering from epilepsy show two categories of abnormal activity: inter-ictal, abnormal signals recorded between epileptic seizures; and ictal, the activity recorded during an epileptic seizure. The EEG signature of an inter-ictal activity is occasional transient waveforms, as isolated spikes, spike trains, sharp waves or spike-wave complexes. EEG signature of an epileptic seizure (ictal period) is composed of a continuous discharge of polymorphic waveforms of variable amplitude and frequency, spike and sharp wave complexes, rhythmic hyper synchrony, or electro cerebral inactivity observed over a duration longer than the average duration of these abnormalities during inter-ictal periods. Generally, the detection of epilepsy can be achieved by visual scanning of EEG recordings for inter-ictal and ictal activities by an experienced neurophysiologist. However, visual review of the vast amount of EEG data has serious drawbacks. Visual inspection is very time consuming and inefficient, especially in the case of long-term recordings. In addition, disagreement among the neurophysiologists on the same recording is possible due to the subjective nature of the analysis and due to the variety of inter-ictal spikes morphology. The main objective of our research is to analyze the acquired EEG signals using auto-regressive features and classify them into different classes. After feature extraction secondary goal is to improve the accuracy of classification. In order to achieve this we have applied a backpropgation based neural network classifier.100 subjects from each set were analysed for feature extraction and classification and data were divided in training, testing and validation of proposed algorithm.
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