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AuthorBoashash B.
AuthorKhan N.A.
AuthorBen-Jabeur T.
Available date2022-05-31T19:01:36Z
Publication Date2015
Publication NameDigital Signal Processing: A Review Journal
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.dsp.2014.12.015
URIhttp://hdl.handle.net/10576/31914
AbstractThis paper presents a tutorial review of recent advances in the field of time-frequency (t, f) signal processing with focus on exploiting (t, f) image feature information using pattern recognition techniques for detection and classification applications. This is achieved by (1) revisiting and streamlining the design of high-resolution quadratic time frequency distributions (TFDs) so as to produce adequate (t, f) images, (2) using image enhancement techniques to improve the resolution of TFDs, and (3) defining new (t, f) features such as (t, f) flatness and (t, f) entropy by extending time-domain or frequency-domain features. Comparative results indicate that the new (t, f) features give better performance as compared to time-only or frequency-only features for the detection of abnormalities in newborn EEG signals. Defining high-resolution TFDs for the extraction of new (t, f) features further improves performance. The findings are corroborated by new experimental results, theoretical derivations and conceptual insights.
Languageen
PublisherElsevier Inc.
SubjectBiomedical signal processing
Classification (of information)
Extraction
Frequency domain analysis
Image enhancement
Time domain analysis
Feature extraction
Image processing
Pattern recognition
Signal analysis
Signal detection
Signal processing
Kernel distribution
Newborn EEG seizure abnormality
Nonstationary signals
Quadratic time-frequency
Time frequency features
Time-frequency images
Feature extraction
Time domain analysis
TitleTime-frequency features for pattern recognition using high-resolution TFDs: A tutorial review
TypeArticle
Pagination1-30
Issue Number1
Volume Number40
dc.accessType Abstract Only


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