Automated Feature Extraction of Epileptic Seizures Using Wavelet Decomposition of EEG and Approximate Entropy
نویسندگان
چکیده
The disease epilepsy is characterized by a sudden and recurrent malfunction of the brain that is termed seizer. The electroencephogram (EEG) has a lot of information about brain and also used in several automated epilepsy detection systems. In this study, the wavelet subband decomposition and Approximate Entropy (ApEn) is used for epilepsy detection from EEG signals. In first stage, EEG signals are decomposed using four levels Discrete Wavelet Transform (DWT). EEG signals were decomposed into five subbands delta, theta, alpha beta and gamma. Approximate Entropy is used for the feature extraction. For each subband ApEn is calculated and it is observed that the value of ApEn drops during an epileptic seizures.
منابع مشابه
Automated Feature Extraction of Epileptic EEG Using Discrete Wavelet Transform and Approximate Entropy
The disease epilepsy is characterized by a sudden and recurrent malfunction of the brain that is termed seizer. The electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. Nonlinear analysis quantifies the EEG signal to address randomness and predictability of brain activity. In this study, the wavelet subband decomposition and Approximate Entropy (ApEn) is used ...
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