Classification and Clustering of Brain Seizure Activity Using Wavelet Trans-form and Radial Basis Neural Network
نویسنده
چکیده
Electroencephalogram (EEG) is the record of the brain electrical activity and it contains valuable information related to the different physiological and pathological states of the brain. Epilepsy is known to be the most prevalent neurological disorder in humans and seizure discharge is the main characteristics of the epilepsy. EEG is an important clinical tool for the diagnosis and monitoring of seizures. Epileptic seizure occurs irregularly and unpredictably manner due to temporary electrical disturbance of the brain. The aim of this project is Epileptic seizure detection in multichannel EEG. This paper presents a novel method for automatic epileptic seizure detection, useing recurrent rates derived from discrete wavelet transform in combination with a radial basis function neural network for classification and clustering of the pattern feature of EEG signals. The output of the neural network aids in finding existence or absence of seizures in the EEG data. We have used discrete wavelet transform (DWT) of Daubechie’s wavelet order 4 to decompose the EEG signal at different levels in extracting approximation and detail coefficients. We have evaluated a unique dynamical parameter (recurrence rate) from the wavelet co-coefficients of EEG of different subjects (normal and epileptic). The recurrence rate has been used in a radial basis function neural network for seizure detection. The performance of the network has been evaluated in terms of the accuracy, specificity and sensitivity detecting in unknown EEG time series.
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