Hyperspherical Manifold for EEG Signals of Epileptic Seizures

The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst m...

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Main Authors: Tahir Ahmad, Vinod Ramachandran
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2012/926358
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author Tahir Ahmad
Vinod Ramachandran
author_facet Tahir Ahmad
Vinod Ramachandran
author_sort Tahir Ahmad
collection DOAJ
description The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.
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spelling doaj-art-1502184cd2944b22bea676c1072611a32025-02-03T01:13:06ZengWileyJournal of Applied Mathematics1110-757X1687-00422012-01-01201210.1155/2012/926358926358Hyperspherical Manifold for EEG Signals of Epileptic SeizuresTahir Ahmad0Vinod Ramachandran1Ibnu Sina Institute for Fundamental Science Studies, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MalaysiaIbnu Sina Institute for Fundamental Science Studies, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MalaysiaThe mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.http://dx.doi.org/10.1155/2012/926358
spellingShingle Tahir Ahmad
Vinod Ramachandran
Hyperspherical Manifold for EEG Signals of Epileptic Seizures
Journal of Applied Mathematics
title Hyperspherical Manifold for EEG Signals of Epileptic Seizures
title_full Hyperspherical Manifold for EEG Signals of Epileptic Seizures
title_fullStr Hyperspherical Manifold for EEG Signals of Epileptic Seizures
title_full_unstemmed Hyperspherical Manifold for EEG Signals of Epileptic Seizures
title_short Hyperspherical Manifold for EEG Signals of Epileptic Seizures
title_sort hyperspherical manifold for eeg signals of epileptic seizures
url http://dx.doi.org/10.1155/2012/926358
work_keys_str_mv AT tahirahmad hypersphericalmanifoldforeegsignalsofepilepticseizures
AT vinodramachandran hypersphericalmanifoldforeegsignalsofepilepticseizures