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Author Stevenson, N.J.en_US
Author O'Toole, J.M.en_US
Author Rankine, L.J.en_US
Author Boylan, G.B.en_US
Author Boashash, B.en_US
Available date 2012-01-15T06:52:07Zen_US
Publication Date 2011-09en_US
Publication Name Medical Engineering & Physics
Citation N.J. Stevenson, J.M. O’Toole, L.J. Rankine, G.B. Boylan, B. Boashash, A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity, Medical Engineering & Physics, Volume 34, Issue 4, May 2012, Pages 437-446en_US
Description The NFM is a new method of signal representation that can be used to detecting pseudo-periodicity in the neonatal EEG, using data-driven TF path integration to compress a TFD into a representation of nonstationary signal component power and mean frequency. (Additional details can be found in the comprehensive book on Time-Frequency Signal Analysis and Processing (see In addition, the most recent upgrade of the original software package that calculates Time-Frequency Distributions and Instantaneous Frequency estimators can be downloaded from the web site: This was the first software developed in the field, and it was first released publicly in 1987 at the 1st ISSPA conference held in Brisbane, Australia, and then continuously updated).en_US
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).en_US
Language enen_US
Publisher Elsevier Ltden_US
Subject neonatal EEG signalsen_US
Subject Fourier transformen_US
Subject time-frequency distributionsen_US
Subject non-stationaryen_US
Subject neonateen_US
Subject seizure detectionen_US
Subject time-frequency signal processingen_US
Subject instantaneous frequency estimationen_US
Subject quadratic time-frequency distributionsen_US
Subject time-frequency analysisen_US
Subject newborn EEGen_US
Title A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicityen_US
Type Articleen_US

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