Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure scalp topographies
Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure scalp topographies
Conventional methods for monitoring clinical (epileptiform) multichannel electroencephalogram (EEG) signals often involve morphological, spectral or time–frequency analysis on individual channels to determine waveform features for detecting and classifying ictal events (seizures) and inter-ictal spikes. Blind source separation (BSS) methods, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing and EEG analysis for extracting a set of underlying source waveforms and sensor projections from multivariate time-series data, some of which reflect clinically relevant neurophysiological (epileptiform) activity. The work presents an alternative spatial approach to source tracking and detection in multichannel EEG that exploits prior knowledge of the spatial topographies of the sensor projections associated with the target sources. The target source sensor projections are obtained by ICA decomposition of data segments containing representative examples of target source activity, e.g. a seizure or ocular artifact. Source tracking and detection are then based on the subspace correlation between individual target sensor projections and the signal subspace over a moving window. Different window lengths and subspace correlation threshold criteria reflect transient or sustained target source activity. To study the behaviour and potential application of this spatial source tracking and detection approach, the method was used to detect (transient) ocular artifacts and (sustained) seizure activity in two segments of 25-channel EEG data recorded from one epilepsy patient on two separate occasions, with promising and intuitive results.
blind source separation, independent component analysis, spatial topography, source tracking and detection, EEG analysis, epilepsy
909-916
Hesse, C.W.
53fee7f7-a12e-4783-a426-0d59afbf475d
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52
October 2007
Hesse, C.W.
53fee7f7-a12e-4783-a426-0d59afbf475d
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52
Hesse, C.W. and James, C.J.
(2007)
Tracking and detection of epileptiform activity in multichannel ictal EEG using signal subspace correlation of seizure scalp topographies.
Medical and Biological Engineering and Computing, 45 (10), .
(doi:10.1007/s11517-006-0103-8).
Abstract
Conventional methods for monitoring clinical (epileptiform) multichannel electroencephalogram (EEG) signals often involve morphological, spectral or time–frequency analysis on individual channels to determine waveform features for detecting and classifying ictal events (seizures) and inter-ictal spikes. Blind source separation (BSS) methods, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing and EEG analysis for extracting a set of underlying source waveforms and sensor projections from multivariate time-series data, some of which reflect clinically relevant neurophysiological (epileptiform) activity. The work presents an alternative spatial approach to source tracking and detection in multichannel EEG that exploits prior knowledge of the spatial topographies of the sensor projections associated with the target sources. The target source sensor projections are obtained by ICA decomposition of data segments containing representative examples of target source activity, e.g. a seizure or ocular artifact. Source tracking and detection are then based on the subspace correlation between individual target sensor projections and the signal subspace over a moving window. Different window lengths and subspace correlation threshold criteria reflect transient or sustained target source activity. To study the behaviour and potential application of this spatial source tracking and detection approach, the method was used to detect (transient) ocular artifacts and (sustained) seizure activity in two segments of 25-channel EEG data recorded from one epilepsy patient on two separate occasions, with promising and intuitive results.
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Published date: October 2007
Keywords:
blind source separation, independent component analysis, spatial topography, source tracking and detection, EEG analysis, epilepsy
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Local EPrints ID: 49610
URI: http://eprints.soton.ac.uk/id/eprint/49610
ISSN: 0140-0118
PURE UUID: a47d3e38-5183-4398-9928-c829cb55cf2f
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Date deposited: 22 Nov 2007
Last modified: 15 Mar 2024 09:57
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Author:
C.W. Hesse
Author:
C.J. James
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