Seizure Detection in Temporal Lobe Epileptic EEGs Using the Best Basis Wavelet Functions
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In this paper, we propose a novel method using best basis wavelet functions and double thresholding that are well suited for detecting and localization of important epileptic events from noisy recorded seizure EEG signals. Our technique is based on dyadic wavelet decomposition and is mainly concerned detection of single epileptic transients within the observation sequence, such as ictal and interictal epochs of EEG. In our experiment we use temporal lobe epileptic data recorded during 84 h from four patients diagnosed with epilepsy. We have achieved promising results that demonstrate efficiency and simplicity that can be used in clinical studies as an automatic decision support tool. Thus reduce the physician’s workload and provide accurate diagnosis of epileptic seizures.
KeywordsEEG Epileptic seizure detection Temporal lobe epilepsy Wavelet transforms Neural networks
We would like to thank the Dr. Sang Do, Lee at the department of Neurology at the Dongsan Medical Center of Keimyung University, Daegu, South Korea, for collaboration and access hospital laboratories for patient’s epileptic data. This research was supported by the Yeungnam University research grants.
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