A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity

Stevenson, N. J., O'Toole, J. M., Rankine, L. J., Boylan, G. B. and Boashash, B. (2012) A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity. Medical Engineering and Physics, 34 4: 437-446.


Author Stevenson, N. J.
O'Toole, J. M.
Rankine, L. J.
Boylan, G. B.
Boashash, B.
Total Author Count Override 6
Title A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity
Journal name Medical Engineering and Physics   Check publisher's open access policy
ISSN 1350-4533
1873-4030
Publication date 2012-05
Year available 2011
Sub-type Article (original research)
DOI 10.1016/j.medengphy.2011.08.001
Volume 34
Issue 4
Start page 437
End page 446
Total pages 10
Place of publication Oxford, United Kingdom
Publisher Elsevier
Collection year 2013
Language eng
Abstract Automated methods of neonatal EEG seizure detection attempt to highlight the evolving, stereotypical, pseudo-periodic, nature of EEG seizure while rejecting the nonstationary, modulated, coloured stochastic background in the presence of various EEG artefacts. An important aspect of neonatal seizure detection is, therefore, the accurate representation and detection of pseudo-periodicity in the neonatal EEG. This paper describes a method of detecting pseudo-periodic components associated with neonatal EEG seizure based on a novel signal representation; the nonstationary frequency marginal (NFM). The NFM can be considered as an alternative time-frequency distribution (TFD) frequency marginal. This method integrates the TFD along data-dependent, time-frequency paths that are automatically extracted from the TFD using an edge linking procedure and has the advantage of reducing the dimension of a TFD. The reduction in dimension simplifies the process of estimating a decision statistic designed for the detection of the pseudo-periodicity associated with neonatal EEG seizure. The use of the NFM resulted in a significant detection improvement compared to existing stationary and nonstationary methods. The decision statistic estimated using the NFM was then combined with a measurement of EEG amplitude and nominal pre- and post-processing stages to form a seizure detection algorithm. This algorithm was tested on a neonatal EEG database of 18 neonates, 826 h in length with 1389 seizures, and achieved comparable performance to existing second generation algorithms (a median receiver operating characteristic area of 0.902; IQR 0.835–0.943 across 18 neonates).
Keyword Neonatal EEG
Fourier transform
Time-frequency distributions
Nonstationary
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online 16 September 2011

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2013 Collection
 
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Created: Mon, 06 Feb 2012, 13:08:23 EST by Roheen Gill on behalf of UQ Centre for Clinical Research