A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity
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Accepted Version
Date
2012-05
Authors
Stevenson, Nathan J.
O'Toole, John M.
Rankine, Luke J.
Boylan, Geraldine B.
Boashash, Boualem
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Published Version
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 hrs 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).
Description
Keywords
Neonatal EEG , Fourier transform , Time-frequency distributions , Nonstationary , Matched filter , Neonate , Seizure detection , Time-frequency signal processing
Citation
Stevenson NJ, O'Toole JM, Rankine LJ, Boylan GB, Boashash B. (2012) 'A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity'. Medical Engineering & Physics, 34 (4):437-446. doi: 10.1016/j.medengphy.2011.08.001
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Copyright
© 2011 IPEM. NOTICE: this is the author’s version of a work that was accepted for publication in Medical Engineering and Physics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Medical Engineering and Physics, Vol 34, Issue 4, (2012) http://dx.doi.org/10.1016/j.medengphy.2011.08.001