TY - GEN
T1 - A methodology for the automatic identification and classification of EEG waves based on clinical guidelines
AU - Ramírez-Fuentes, C. A.
AU - Tovar-Corona, B.
AU - Silva-Ramirez, M. A.
AU - Barrera-Figueroa, V.
AU - Garay-Jiménez, L. I.
N1 - Publisher Copyright:
© 2018 International Institute of Informatics and Systemics IIIS. All rights reserved.
PY - 2018
Y1 - 2018
N2 - In order to identify abnormal behaviors related to epileptic seizures and other neurological disorders, in this paper, it is described a methodology with a clinical approach to classify events shown in EEG recordings, based on the international standards and current guidelines. According to medical definitions, for this work the EEG signal was classified into suppressions, rhythms, frequencies and abnormal behaviors, obtained from several combinations of the signal's parameters: amplitude, frequency and patient's age. The mean of peak values was used to obtain the amplitude. The Fast Fourier Transform and high-pass filters were used to extract the dominant frequency. A set of 192 segments of one second duration, randomly selected from 7 patients, were evaluated and their behavior was identified and classified into 13 classes. It was obtained an efficiency of 96.35%. This method found abnormal behaviors related to epileptic seizures and other kinds of neurological disorders. This method identifies abnormal events, their timing and cortical distribution, making possible the extraction of seizure's segments for further feature analysis.
AB - In order to identify abnormal behaviors related to epileptic seizures and other neurological disorders, in this paper, it is described a methodology with a clinical approach to classify events shown in EEG recordings, based on the international standards and current guidelines. According to medical definitions, for this work the EEG signal was classified into suppressions, rhythms, frequencies and abnormal behaviors, obtained from several combinations of the signal's parameters: amplitude, frequency and patient's age. The mean of peak values was used to obtain the amplitude. The Fast Fourier Transform and high-pass filters were used to extract the dominant frequency. A set of 192 segments of one second duration, randomly selected from 7 patients, were evaluated and their behavior was identified and classified into 13 classes. It was obtained an efficiency of 96.35%. This method found abnormal behaviors related to epileptic seizures and other kinds of neurological disorders. This method identifies abnormal events, their timing and cortical distribution, making possible the extraction of seizure's segments for further feature analysis.
KW - Alpha
KW - Beta
KW - Delta
KW - Electroencephalographic waves
KW - Rhythm
KW - Suppressions
KW - Theta
UR - http://www.scopus.com/inward/record.url?scp=85050226195&partnerID=8YFLogxK
M3 - Contribución a la conferencia
AN - SCOPUS:85050226195
T3 - IMCIC 2018 - 9th International Multi-Conference on Complexity, Informatics and Cybernetics, Proceedings
SP - 134
EP - 138
BT - IMCIC 2018 - 9th International Multi-Conference on Complexity, Informatics and Cybernetics, Proceedings
A2 - Horne, Jeremy
A2 - Savoie, Michael
A2 - Callaos, Nagib C.
A2 - Horne, Jeremy
A2 - Sanchez, Belkis
A2 - Gill, T. Grandon
PB - International Institute of Informatics and Systemics, IIIS
T2 - 9th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2018
Y2 - 13 March 2018 through 16 March 2018
ER -