Nonlinear Noise Reduction and Predictability of Epileptic Seizures

Rajeshkumar Venugopal; Awadhesh Prasad; Narayanan Krishnamurthi; Andreas Spanias; L. D. Iasemidis

Nonlinear Noise Reduction and Predictability of Epileptic Seizures

Rajeshkumar Venugopal, Awadhesh Prasad, Narayanan Krishnamurthi, Andreas Spanias, L. D. Iasemidis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this work we address the problem of nonlinear filtering of noise in EEG (electroencephalographic) signal using the centroid correction method, in an attempt to improve the predictability of epileptic seizures long prior to their occurrences. We analyzed multi-channel EEG recordings containing 15 successive seizures in one epileptic patient with focal temporal lobe epilepsy. The total EEG spanned 3 days. The results show an improvement in the predictability time in most of the seizures in addition to improvement in the number of predictable seizures (from 10 to 12) after filtering and dynamical analysis of the EEG.

Original language	English (US)
Title of host publication	Proceedings of the IASTED International Conference on Modelling and Simulation
Editors	M.H. Hamza, M.H. Hamza
Pages	240-245
Number of pages	6
State	Published - Dec 1 2003
Event	Proceedings of the IASTED International Conference on Modelling and Simulation - Palm Springs, CA, United States Duration: Feb 24 2003 → Feb 26 2003

Publication series

Name	Proceedings of the IASTED International Conference on Modelling and Simulation

Other

Other	Proceedings of the IASTED International Conference on Modelling and Simulation
Country/Territory	United States
City	Palm Springs, CA
Period	2/24/03 → 2/26/03

Keywords

Biomedical signals
EEG
Nonlinear noise reduction
Seizure predictability
Time series analysis

ASJC Scopus subject areas

Software
Modeling and Simulation
Computer Science Applications

Cite this

Nonlinear Noise Reduction and Predictability of Epileptic Seizures. / Venugopal, Rajeshkumar; Prasad, Awadhesh; Krishnamurthi, Narayanan et al.
Proceedings of the IASTED International Conference on Modelling and Simulation. ed. / M.H. Hamza; M.H. Hamza. 2003. p. 240-245 (Proceedings of the IASTED International Conference on Modelling and Simulation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Venugopal, R, Prasad, A, Krishnamurthi, N, Spanias, A & Iasemidis, LD 2003, Nonlinear Noise Reduction and Predictability of Epileptic Seizures. in MH Hamza & MH Hamza (eds), Proceedings of the IASTED International Conference on Modelling and Simulation. Proceedings of the IASTED International Conference on Modelling and Simulation, pp. 240-245, Proceedings of the IASTED International Conference on Modelling and Simulation, Palm Springs, CA, United States, 2/24/03.

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AU - Venugopal, Rajeshkumar

AU - Prasad, Awadhesh

AU - Krishnamurthi, Narayanan

AU - Spanias, Andreas

AU - Iasemidis, L. D.

PY - 2003/12/1

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N2 - In this work we address the problem of nonlinear filtering of noise in EEG (electroencephalographic) signal using the centroid correction method, in an attempt to improve the predictability of epileptic seizures long prior to their occurrences. We analyzed multi-channel EEG recordings containing 15 successive seizures in one epileptic patient with focal temporal lobe epilepsy. The total EEG spanned 3 days. The results show an improvement in the predictability time in most of the seizures in addition to improvement in the number of predictable seizures (from 10 to 12) after filtering and dynamical analysis of the EEG.

AB - In this work we address the problem of nonlinear filtering of noise in EEG (electroencephalographic) signal using the centroid correction method, in an attempt to improve the predictability of epileptic seizures long prior to their occurrences. We analyzed multi-channel EEG recordings containing 15 successive seizures in one epileptic patient with focal temporal lobe epilepsy. The total EEG spanned 3 days. The results show an improvement in the predictability time in most of the seizures in addition to improvement in the number of predictable seizures (from 10 to 12) after filtering and dynamical analysis of the EEG.

KW - Biomedical signals

KW - EEG

KW - Nonlinear noise reduction

KW - Seizure predictability

KW - Time series analysis

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BT - Proceedings of the IASTED International Conference on Modelling and Simulation

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Nonlinear Noise Reduction and Predictability of Epileptic Seizures

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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