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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Detecting Epilepsy in EEG Signals Using Time, Frequency and Time-Frequency Domain Features

verfasst von : D. E. Hernández, L. Trujillo, E. Z-Flores, O. M. Villanueva, O. Romo-Fewell

Erschienen in: Computer Science and Engineering—Theory and Applications

Verlag: Springer International Publishing

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Abstract

Seizures caused by epilepsy are unprovoked, they disrupt the mantel activity of the patient and impair their normal motor and sensorial functions, endangering the patient’s well being. Exploiting today’s technology it is possible toe create automatic systems to monitor and evaluate patients. An area of special interest is the automatic analysis of EEG signals. This paper presents extensive analysis of feature extraction and classification methods that have reported good results in other EEG based problems. Several methods are detailed to extract 52 features from the time, frequency and time-frequency domains in order to characterize the EEG signals. Additionally, 10 different classification models, together with a feature selection method, are implemented using these features to identify if a signal corresponds to an epileptic state. The experiments were performed using the standard BONN and the proposed method achieve results comparable to those in the state-of-the-art for the three and four classes problems.

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Vorheriges Kapitel An Approach to Fuzzy Inference System Based Fuzzy Cognitive Maps
Nächstes Kapitel Big Data and Computational Intelligence: Background, Trends, Challenges, and Opportunities
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Metadaten
Titel
Detecting Epilepsy in EEG Signals Using Time, Frequency and Time-Frequency Domain Features
verfasst von
D. E. Hernández
L. Trujillo
E. Z-Flores
O. M. Villanueva
O. Romo-Fewell
Copyright-Jahr
2018
Buch
Computer Science and Engineering—Theory and Applications

Print ISBN: 978-3-319-74059-1

Electronic ISBN: 978-3-319-74060-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-74060-7_9