Principles of time-frequency feature extraction for change detection in non-stationary signals: applications to newborn EEG abnormality detection

Boashash, Boualem, Azemi, Ghasem and Khan, Nabeel Ali (2015) Principles of time-frequency feature extraction for change detection in non-stationary signals: applications to newborn EEG abnormality detection. Pattern Recognition, 48 3: 616-627. doi:10.1016/j.patcog.2014.08.016

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Author Boashash, Boualem
Azemi, Ghasem
Khan, Nabeel Ali
Title Principles of time-frequency feature extraction for change detection in non-stationary signals: applications to newborn EEG abnormality detection
Journal name Pattern Recognition   Check publisher's open access policy
ISSN 0031-3203
1873-5142
Publication date 2015-03
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.patcog.2014.08.016
Open Access Status
Volume 48
Issue 3
Start page 616
End page 627
Total pages 12
Place of publication Amsterdam Netherlands
Publisher Elsevier
Collection year 2015
Language eng
Formatted abstract
This paper considers the general problem of detecting change in non-stationary signals using features observed in the time–frequency (t,f) domain, obtained using a class of quadratic time–frequency distributions (QTFDs). The focus of this study is to propose a methodology to define new (t,f) features by extending time-only and frequency-only features to the joint (t,f) domain for detecting changes in non-stationary signals. The (t,f) features are used as a representative subset characterizing the status of the observed non-stationary signal. Change in the signal is then reflected as a change in the (t,f) features. This (t,f) approach is applied to the problem of detecting abnormal brain activity in newborns (e.g. seizure) using measurements of the EEG for diagnosis and prognosis. In addition, a pre-processing stage for detecting artifacts in EEG signals for signal enhancement is studied and implemented separately. Overall results indicate that, in general, the (t,f) approach results in an improved performance in detecting artifacts and seizures in newborn EEG signals as compared to time-only or frequency-only features.
Keyword Time-frequency feature extraction
Abnormality detection
Seizure
Newborn EEG artifacts
ROC analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 27 Aug 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2015 Collection
 
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Citation counts: TR Web of Science Citation Count  Cited 9 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 17 times in Scopus Article | Citations
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