Staff View: Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State

Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State

This paper describes a classification method is presented using an Fuzzy System to detect the occurrences of Partial Seizures from Epilepsy data, which can be implemented in any embedded system as a wearable detection system. The system distinguishes between 'Normal' and 'Seizure'...

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Main Authors:	Shakir, Mohamed, Malik, Aamir Saeed, Kamel , Nidal, Qidwai, Uvais
Format:	Conference or Workshop Item
Published:	2014
Subjects:	Q Science (General) T Technology (General)
Online Access:	http://eprints.utp.edu.my/11429/1/ISMS2014%20-%20Fuzzy%20Platform%20for%20Embedded%20Wearable%20EEG%20Seizure%20Detection%20in%20Ambulatory%20State.pdf http://eprints.utp.edu.my/11429/
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spelling	my.utp.eprints.114292015-04-28T02:54:15Z Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State Shakir, Mohamed Malik, Aamir Saeed Kamel , Nidal Qidwai, Uvais Q Science (General) T Technology (General) This paper describes a classification method is presented using an Fuzzy System to detect the occurrences of Partial Seizures from Epilepsy data, which can be implemented in any embedded system as a wearable detection system. The system distinguishes between 'Normal' and 'Seizure' state using on-the-fly calculated features representing the statistical measures for specifically filtered signals from the raw data. It was noticed that for a large number of cases, the seizure waveforms manifest higher energy components during the seizure episodes as compared to the normal brain activity in specific bands of frequencies. Same is also true in the reverse fashion for a separate band of frequency that changes the energy levels from higher to lower when a patient goes from Normal to a Seizure state. This fact has been exploited in this paper and filter has been developed to isolate the seizure band. The Fuzzy system has been developed on the calculated measures for the filtered signal from this band-filter and classification is performed on the basis of certain empirical thresholds. Since the complexity of calculations have been deliberately kept quite low, the algorithm is highly suitable for implementation in a small micro-controller environment with near-real-time operation. This gives an enhanced advantage over the existing EEG based seizure detection systems due to their complex pattern classification methodologies. Based on the presented technique, a wearable ubiquitous system can be easily developed with applications in personal healthcare and clinical usage. In this case, the users are not necessarily restricted to the clinical environment in which many devices are connected to the patient externally. The wearable devices allow the user to continue daily activities while being monitored for seizure activities. Once seizure is detected, a number of possible usages can be employed such as alerting the user while driving/holding a baby etc. 2014 Conference or Workshop Item NonPeerReviewed application/pdf http://eprints.utp.edu.my/11429/1/ISMS2014%20-%20Fuzzy%20Platform%20for%20Embedded%20Wearable%20EEG%20Seizure%20Detection%20in%20Ambulatory%20State.pdf Shakir, Mohamed and Malik, Aamir Saeed and Kamel , Nidal and Qidwai, Uvais (2014) Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State. In: 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation (ISMS 2014). http://eprints.utp.edu.my/11429/
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
topic	Q Science (General) T Technology (General)
spellingShingle	Q Science (General) T Technology (General) Shakir, Mohamed Malik, Aamir Saeed Kamel , Nidal Qidwai, Uvais Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State
description	This paper describes a classification method is presented using an Fuzzy System to detect the occurrences of Partial Seizures from Epilepsy data, which can be implemented in any embedded system as a wearable detection system. The system distinguishes between 'Normal' and 'Seizure' state using on-the-fly calculated features representing the statistical measures for specifically filtered signals from the raw data. It was noticed that for a large number of cases, the seizure waveforms manifest higher energy components during the seizure episodes as compared to the normal brain activity in specific bands of frequencies. Same is also true in the reverse fashion for a separate band of frequency that changes the energy levels from higher to lower when a patient goes from Normal to a Seizure state. This fact has been exploited in this paper and filter has been developed to isolate the seizure band. The Fuzzy system has been developed on the calculated measures for the filtered signal from this band-filter and classification is performed on the basis of certain empirical thresholds. Since the complexity of calculations have been deliberately kept quite low, the algorithm is highly suitable for implementation in a small micro-controller environment with near-real-time operation. This gives an enhanced advantage over the existing EEG based seizure detection systems due to their complex pattern classification methodologies. Based on the presented technique, a wearable ubiquitous system can be easily developed with applications in personal healthcare and clinical usage. In this case, the users are not necessarily restricted to the clinical environment in which many devices are connected to the patient externally. The wearable devices allow the user to continue daily activities while being monitored for seizure activities. Once seizure is detected, a number of possible usages can be employed such as alerting the user while driving/holding a baby etc.
format	Conference or Workshop Item
author	Shakir, Mohamed Malik, Aamir Saeed Kamel , Nidal Qidwai, Uvais
author_facet	Shakir, Mohamed Malik, Aamir Saeed Kamel , Nidal Qidwai, Uvais
author_sort	Shakir, Mohamed
title	Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State
title_short	Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State
title_full	Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State
title_fullStr	Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State
title_full_unstemmed	Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State
title_sort	fuzzy platform for embedded wearable eeg seizure detection in ambulatory state
publishDate	2014
url	http://eprints.utp.edu.my/11429/1/ISMS2014%20-%20Fuzzy%20Platform%20for%20Embedded%20Wearable%20EEG%20Seizure%20Detection%20in%20Ambulatory%20State.pdf http://eprints.utp.edu.my/11429/
_version_	1738655952107732992
score	13.211869

Fuzzy Platform for Embedded Wearable EEG Seizure Detection in Ambulatory State

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