Abstract
Epilepsy is a disease in which frequent seizures occur due to abnormal activity of neurons. Patients affected by this disease can be treated with the help of medicines or surgical procedures. However, both of these methods are not quite useful. The only method to treat epilepsy patients effectively is to predict the seizure before its onset. It has been observed that abnormal activity in the brain signals starts before the occurrence of seizure known as the preictal state. Many researchers have proposed machine learning models for prediction of epileptic seizures by detecting the start of preictal state. However, pre-processing, feature extraction and classification remains a great challenge in the prediction of preictal state. Therefore, we propose a model that uses common spatial pattern filtering and wavelet transform for preprocessing, principal component analysis for feature extraction and support vector machines for detecting preictal state. We have applied our model on 23 subjects and an average sensitivity of 93.1% has been observed for 84 seizures.
Highlights
Epilepsy is a disease in which frequent seizures occur due to abnormal activity of neurons
Epileptic seizures can be divided into four states i.e., interictal state, preictal state (Le Van Quyen et al, 2005), ictal and post-ictal stats
Lyapunov exponents (Blanco et al, 1995) are useful in differentiating between these states. It is quite evident by analyzing multiple EEG recordings that there is a significant change that occurs in all the state of epileptic seizure
Summary
Epilepsy is a disease in which frequent seizures occur due to abnormal activity of neurons Patients affected by this disease can be treated with the help of medicines or surgical procedures. Lyapunov exponents (Blanco et al, 1995) are useful in differentiating between these states It is quite evident by analyzing multiple EEG recordings that there is a significant change that occurs in all the state of epileptic seizure. An extensive preprocessing is required to remove the noise from EEG recordings Another problem in prediction is feature selection for classification of multiple states of seizure. If we are able to do effective pre-processing and select a suitable model after feature extraction for classification of preictal and interictal state prediction of epileptic seizure will be very useful in health sector
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