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Design and implementation of a multi-sensor newborn EEG seizure and background model with inter-channel field characterization

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Design and implementation of a multi-sensor newborn EEG seizure and background model with inter-channel field characterization. / Al-Sa'd, Mohammad F.; Boashash, Boualem.

julkaisussa: Digital Signal Processing: A Review Journal, Vuosikerta 90, 01.07.2019, s. 71-99.

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Al-Sa'd, Mohammad F. ; Boashash, Boualem. / Design and implementation of a multi-sensor newborn EEG seizure and background model with inter-channel field characterization. Julkaisussa: Digital Signal Processing: A Review Journal. 2019 ; Vuosikerta 90. Sivut 71-99.

Bibtex - Lataa

@article{c0302644593040d3b2c370290379b369,
title = "Design and implementation of a multi-sensor newborn EEG seizure and background model with inter-channel field characterization",
abstract = "This paper presents a novel multi-sensor non-stationary EEG model; it is obtained by combining state of the art mono-sensor newborn EEG simulators, a multilayer newborn head model comprised of four homogeneous concentric spheres, a multi-sensor propagation scheme based on array processing and optical dispersion to calculate inter-channel attenuation and delay, and lastly, a multi-variable optimization paradigm using particle swarm optimization and Monte-Carlo simulations to validate the model for optimal conditions. Multi-sensor EEG of 7 newborns, comprised of seizure and background epochs, are analyzed using time-space, time-frequency, power maps and multi-sensor causality techniques. The outcomes of these methods are validated by medical insights and serve as a backbone for any assumptions and as performance benchmarks for the model to be evaluated against. The results obtained with the developed model show 85.7{\%} averaged time-frequency correlation (which is the selected measure for similarity with real EEG)with 5.9{\%} standard deviation, and the averaged error obtained is 34.6{\%} with 8{\%} standard deviation. The resulting performances indicate that the proposed model provides a suitable matching fit with real EEG in terms of their probability density function, inter-sensor attenuation and translation, and multi-sensor causality. They also demonstrate the model flexibility to generate new unseen samples by utilizing user-defined parameters, making it suitable for other relevant applications.",
keywords = "EEG analysis, Multi-channel EEG, Multi-sensor propagation, Time-frequency processing, Time-space analysis",
author = "Al-Sa'd, {Mohammad F.} and Boualem Boashash",
year = "2019",
month = "7",
day = "1",
doi = "10.1016/j.dsp.2019.02.003",
language = "English",
volume = "90",
pages = "71--99",
journal = "Digital Signal Processing",
issn = "1051-2004",
publisher = "Elsevier",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Design and implementation of a multi-sensor newborn EEG seizure and background model with inter-channel field characterization

AU - Al-Sa'd, Mohammad F.

AU - Boashash, Boualem

PY - 2019/7/1

Y1 - 2019/7/1

N2 - This paper presents a novel multi-sensor non-stationary EEG model; it is obtained by combining state of the art mono-sensor newborn EEG simulators, a multilayer newborn head model comprised of four homogeneous concentric spheres, a multi-sensor propagation scheme based on array processing and optical dispersion to calculate inter-channel attenuation and delay, and lastly, a multi-variable optimization paradigm using particle swarm optimization and Monte-Carlo simulations to validate the model for optimal conditions. Multi-sensor EEG of 7 newborns, comprised of seizure and background epochs, are analyzed using time-space, time-frequency, power maps and multi-sensor causality techniques. The outcomes of these methods are validated by medical insights and serve as a backbone for any assumptions and as performance benchmarks for the model to be evaluated against. The results obtained with the developed model show 85.7% averaged time-frequency correlation (which is the selected measure for similarity with real EEG)with 5.9% standard deviation, and the averaged error obtained is 34.6% with 8% standard deviation. The resulting performances indicate that the proposed model provides a suitable matching fit with real EEG in terms of their probability density function, inter-sensor attenuation and translation, and multi-sensor causality. They also demonstrate the model flexibility to generate new unseen samples by utilizing user-defined parameters, making it suitable for other relevant applications.

AB - This paper presents a novel multi-sensor non-stationary EEG model; it is obtained by combining state of the art mono-sensor newborn EEG simulators, a multilayer newborn head model comprised of four homogeneous concentric spheres, a multi-sensor propagation scheme based on array processing and optical dispersion to calculate inter-channel attenuation and delay, and lastly, a multi-variable optimization paradigm using particle swarm optimization and Monte-Carlo simulations to validate the model for optimal conditions. Multi-sensor EEG of 7 newborns, comprised of seizure and background epochs, are analyzed using time-space, time-frequency, power maps and multi-sensor causality techniques. The outcomes of these methods are validated by medical insights and serve as a backbone for any assumptions and as performance benchmarks for the model to be evaluated against. The results obtained with the developed model show 85.7% averaged time-frequency correlation (which is the selected measure for similarity with real EEG)with 5.9% standard deviation, and the averaged error obtained is 34.6% with 8% standard deviation. The resulting performances indicate that the proposed model provides a suitable matching fit with real EEG in terms of their probability density function, inter-sensor attenuation and translation, and multi-sensor causality. They also demonstrate the model flexibility to generate new unseen samples by utilizing user-defined parameters, making it suitable for other relevant applications.

KW - EEG analysis

KW - Multi-channel EEG

KW - Multi-sensor propagation

KW - Time-frequency processing

KW - Time-space analysis

U2 - 10.1016/j.dsp.2019.02.003

DO - 10.1016/j.dsp.2019.02.003

M3 - Article

VL - 90

SP - 71

EP - 99

JO - Digital Signal Processing

JF - Digital Signal Processing

SN - 1051-2004

ER -