Abstract
The electroencephalograph (EEG) signals are most widely used for identification of neurological diseases like epilepsy, Alzheimer’s, and other brain diseases. Detection of epileptic activity requires a detailed analysis of the entire length of the EEG data. In this paper, we proposed an automated detection of epileptic seizure using energy distribution of wavelet coefficient in each sub-band frequencies of the EEG signals. The performance of the proposed method is investigated using signals obtained from public EEG database at the University Hospital Bonn, Germany. Initially, the EEG signals are de-noised and decomposed into sub-bands using discrete wavelet transform (DWT), Then wavelet energy distribution in each sub-band is calculated and used as a feature set. Finally, artificial neural network (ANN) used to classify the feature set with ANN. The method was tested on EEG data sets obtained from that belongs to three subject groups: (a) healthy, (b) seizure-free interval, and (c) epileptic syndrome during a seizure. The test result shows that the proposed method for detecting epileptic seizure can achieve an overall classification accuracy of 95%. The proposed method can be used efficiently for recognition of epileptic seizures.
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Wani, S.M., Sabut, S., Nalbalwar, S.L. (2019). Detection of Epileptic Seizure Using Wavelet Transform and Neural Network Classifier. In: Iyer, B., Nalbalwar, S., Pathak, N. (eds) Computing, Communication and Signal Processing . Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-13-1513-8_75
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DOI: https://doi.org/10.1007/978-981-13-1513-8_75
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