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Spatial Feature Reduction in Long-term EEG for Patient-specific Epileptic Seizure Event Detection

Authors:

Nipun Dilesh Perera,

Chamara Madarasingha,

Anjula C. De SilvaAuthors Info & Claims

ICSPS 2017: Proceedings of the 9th International Conference on Signal Processing Systems

Pages 230 - 234

https://doi.org/10.1145/3163080.3163124

Published: 27 November 2017 Publication History

Abstract

Seizure is a common phenomenon among patients with epilepsy. Detection of seizure at the onset enables immediate actions to be taken. However, real-time seizure event and onset detection in an ambulatory epilepsy patient using continuous scalp EEG monitoring proves to be difficult with the hardware associated with signal acquisition. Furthermore, developing a universal seizure detection algorithm is not applicable due to the patient dependence. The prominent challenge in working with EEG its non-stationarity. Hence, time-frequency methods such as wavelet analysis have been used in recent research, to develop algorithms. In this study, we develop patient specific seizure detection system in which, Discrete Wavelet Packet Transform (DWPT) is used to extract wavelet packet energy and use a single EEG channel to train an offline SVM classifier. This study is conducted to prove that accurate seizure prediction can be performed using at most two channels for most patients and develop a system for real-time monitoring and seizure event detection. The objective is to maintain a high level of accuracy despite the drastic reduction in spatial features and achieve a low latency of detection so that this algorithm could be used in seizure alert devices with less hardware complexity. CHB-MIT Scalp EEG database has been used in this study and an average sensitivity of detection of 90.3% an average specificity of 99% with an average latency of 3.41s have been achieved for 60% of the epileptic seizure subjects considered.

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Cited By

Patiño-Claros MHolguin-García SDaza-Chica ATabares-Soto RBravo-Ortiz M(2023)Epileptic Seizure Prediction Methods Using Machine Learning and Deep Learning ModelsTrends in Sustainable Smart Cities and Territories10.1007/978-3-031-36957-5_21(244-253)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-3-031-36957-5_21
SHABARINATH BCHALLAGULLA KVISODHAN M(2019)A Comparative Study of Epileptic Seizure Detection Framework using SVM and ELM2019 International Conference on Intelligent Computing and Control Systems (ICCS)10.1109/ICCS45141.2019.9065458(302-306)Online publication date: May-2019
https://doi.org/10.1109/ICCS45141.2019.9065458

Index Terms

Spatial Feature Reduction in Long-term EEG for Patient-specific Epileptic Seizure Event Detection
1. Applied computing
  1. Life and medical sciences

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Patient non-specific algorithm for seizures detection in scalp EEG

Epilepsy is a brain disorder that affects about 1% of the population in the world. Seizure detection is an important component in both the diagnosis of epilepsy and seizure control. In this work a patient non-specific strategy for seizure detection ...
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cover image ACM Other conferences

ICSPS 2017: Proceedings of the 9th International Conference on Signal Processing Systems

November 2017

237 pages

ISBN:9781450353847

DOI:10.1145/3163080

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 27 November 2017

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ICSPS 2017

ICSPS 2017: The 9th International Conference on Signal Processing Systems

November 27 - 30, 2017

Auckland, New Zealand

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Cited By

Patiño-Claros MHolguin-García SDaza-Chica ATabares-Soto RBravo-Ortiz M(2023)Epileptic Seizure Prediction Methods Using Machine Learning and Deep Learning ModelsTrends in Sustainable Smart Cities and Territories10.1007/978-3-031-36957-5_21(244-253)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-3-031-36957-5_21
SHABARINATH BCHALLAGULLA KVISODHAN M(2019)A Comparative Study of Epileptic Seizure Detection Framework using SVM and ELM2019 International Conference on Intelligent Computing and Control Systems (ICCS)10.1109/ICCS45141.2019.9065458(302-306)Online publication date: May-2019
https://doi.org/10.1109/ICCS45141.2019.9065458

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