This 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. 
Time–frequency features for pattern recognition using high-resolution TFDs: A tutorial review