Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data

A Classification technique using Fuzzy Logic Inference System to identify and predict the partial seizure from the epileptic EEG data along with preliminary brain conditions in different scenarios is presented in this paper. This detection system can produce warning signals for epileptic seizures. E...

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Main Authors: Qidwai, U., Malik, A.S., Shakir, M.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.31764 /
Published: Springer Verlag 2014
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928238473&doi=10.1007%2f978-3-319-02913-9_105&partnerID=40&md5=0f3961f3fa0641a7d10be712ef4e98bd
http://eprints.utp.edu.my/31764/
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spelling utp-eprints.317642022-03-29T03:36:49Z Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data Qidwai, U. Malik, A.S. Shakir, M. A Classification technique using Fuzzy Logic Inference System to identify and predict the partial seizure from the epileptic EEG data along with preliminary brain conditions in different scenarios is presented in this paper. This detection system can produce warning signals for epileptic seizures. Electroencephalography (EEG) plays an important role, especially EEG based health diagnosis of brain disorder. However, the common clinical methods are insufficient when it comes to design an automated module to detect and predict partial seizure for epileptic patients. If the detection system is to be designed for ubiquitous applications, the system becomes even more complex if the patient is not confined to clinical environment when the device is monitoring continuously while the patient is involved in daily activities. Therefore, the work presented here includes embedded hardware system that works with classification algorithm on real EEG signals, in a ubiquitous setting. The performance of the system is shown under various conditions of daily activities. In order to make all this in a ubiquitous form factor, the algorithm for classification and detection of the pre-seizure conditions should be tremendously simple for processing the signal in a low cost ubiquitous microcontroller. This has been achieved in this work through the use of Fuzzy Classifiers based on the lookup table to empower system simplicity. The algorithm also utilizes certain statistical features from the EEG signal that are used as features to the classifier logic. While the clinical testing of the device is still awaited, various scenarios have been implemented using a custom-built hardware simulator based on empirical modeling of the real EEG signals. This shown various performance modes of the system and confirms the detection of pre-seizure state for a number of parameters related to the patients such as age, gender, etc� By using this type of fuzzy logic classifier, we were able to get over 90 accurate classifications for the partial seizure. © Springer International Publishing Switzerland 2014. Springer Verlag 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928238473&doi=10.1007%2f978-3-319-02913-9_105&partnerID=40&md5=0f3961f3fa0641a7d10be712ef4e98bd Qidwai, U. and Malik, A.S. and Shakir, M. (2014) Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data. In: UNSPECIFIED. http://eprints.utp.edu.my/31764/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description A Classification technique using Fuzzy Logic Inference System to identify and predict the partial seizure from the epileptic EEG data along with preliminary brain conditions in different scenarios is presented in this paper. This detection system can produce warning signals for epileptic seizures. Electroencephalography (EEG) plays an important role, especially EEG based health diagnosis of brain disorder. However, the common clinical methods are insufficient when it comes to design an automated module to detect and predict partial seizure for epileptic patients. If the detection system is to be designed for ubiquitous applications, the system becomes even more complex if the patient is not confined to clinical environment when the device is monitoring continuously while the patient is involved in daily activities. Therefore, the work presented here includes embedded hardware system that works with classification algorithm on real EEG signals, in a ubiquitous setting. The performance of the system is shown under various conditions of daily activities. In order to make all this in a ubiquitous form factor, the algorithm for classification and detection of the pre-seizure conditions should be tremendously simple for processing the signal in a low cost ubiquitous microcontroller. This has been achieved in this work through the use of Fuzzy Classifiers based on the lookup table to empower system simplicity. The algorithm also utilizes certain statistical features from the EEG signal that are used as features to the classifier logic. While the clinical testing of the device is still awaited, various scenarios have been implemented using a custom-built hardware simulator based on empirical modeling of the real EEG signals. This shown various performance modes of the system and confirms the detection of pre-seizure state for a number of parameters related to the patients such as age, gender, etc� By using this type of fuzzy logic classifier, we were able to get over 90 accurate classifications for the partial seizure. © Springer International Publishing Switzerland 2014.
format Conference or Workshop Item
author Qidwai, U.
Malik, A.S.
Shakir, M.
spellingShingle Qidwai, U.
Malik, A.S.
Shakir, M.
Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data
author_sort Qidwai, U.
title Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data
title_short Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data
title_full Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data
title_fullStr Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data
title_full_unstemmed Embedded fuzzy classifier for detection and classification of preseizure state using real EEG data
title_sort embedded fuzzy classifier for detection and classification of preseizure state using real eeg data
publisher Springer Verlag
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928238473&doi=10.1007%2f978-3-319-02913-9_105&partnerID=40&md5=0f3961f3fa0641a7d10be712ef4e98bd
http://eprints.utp.edu.my/31764/
_version_ 1741197631829311488
score 11.62408

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