On the diagnosis of epileptic seizures using wavelet transform and artificial neural networks in EEG signals

E. S. Guido *, P. R. Maciel

Department of Electrical Engineering, School of Engineering at São Carlos and Institute of Physics at Sao Carlos, University of São Paulo, SP, Brazil


In this research, the depth of Anesthesia has been estimated using electroencephalogram (EGG) signals, wavelet transform, and adaptive Neuro Fuzzy inference system (ANFIS). ANFIS can estimate the depth of Anesthesia with high accuracy. A set of EEG signals regarding consciousness, moderate Anesthesia, deep Anesthesia, and iso-electric point were collected from the American Society of Anesthesiologists (ASA) and PhysioNet. First, the extracted features were combined using wavelet and spectral analysis after which the target features were selected. Later, the features were classified into four categories. The results obtained revealed that the accuracy of the proposed method was 98.45%.Since the visual analysis of EEG signals is difficult, the proposed method can significantly help anesthesiologists estimate the depth of Anesthesia. Further, the results showed that ANFIS could significantly increase the accuracy of Anesthesia depth estimation. Finally, the system was deemed to be advantageous since it was also capable of updating in real-time situations as well.


Anesthesia, Electroencephalogram, Wavelet, Classification, Adaptive Neuro fuzzy inference system

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Article history

Received 20 October 2017, Received in revised form 10 January 2018, Accepted 15 January 2018

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Guido ES and Maciel PR (2018). On the diagnosis of epileptic seizures using wavelet transform and artificial neural networks in EEG signals. Annals of Electrical and Electronic Engineering, 1(1): 1-4

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