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

• A streamlined methodology for designing high resolution quadratic TFDs using separable, directional and adaptive kernels.
• A formulation of new (t, f) features by translation from time-domain only features or frequency-domain only features.
• A review of (t, f) image processing techniques for resolution enhancement, de-noising and improved classification.
• A review of multi-component IF estimation techniques as a performance criterion to compare time–frequency distributions.
• Experiments that illustrate the above points in EEG seizure detection and classification using a large medical database.

This paper presents a tutorial review of recent advances in the field of time–frequency (t,f)(t,f) signal processing with focus on exploiting (t,f)(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)(t,f) images, (2) using image enhancement techniques to improve the resolution of TFDs, and (3) defining new (t,f)(t,f) features such as (t,f)(t,f) flatness and (t,f)(t,f) entropy by extending time-domain or frequency-domain features. Comparative results indicate that the new (t,f)(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)(t,f) features further improves performance. The findings are corroborated by new experimental results, theoretical derivations and conceptual insights.