Principles of time–frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection. Issue 3 (March 2015)

• Record Type: Journal Article
• Authors: Boashash, Boualem; Azemi, Ghasem; Ali Khan, Nabeel
• Is Part Of: Pattern recognition. Volume 48:Issue 3(2015:Mar.)
• Page Start: 616
• Page End: 627
• Publication Date: 2015-03
• Subjects: Time–frequency feature extraction -- Abnormality detection -- Seizure -- Newborn EEG artifacts -- ROC analysis; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2014.08.016
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract : Highlights: We propose ( t, f ) based features for detecting change in nonstationary signals. We use the features to detect seizures and artifacts in newborn EEGs. The features result in an improved performance in detecting seizures and artifacts. Performance of ( t, f ) features depends on the type of time–frequency distribution.