Exploiting the ambiguity domain for non-stationary biomedical signal classification

Research in time-frequency distributions (TFDs) is limited in terms of their use of the available spatial domains and in their target applications. Most of the work up till now has been concentrated mainly on the t-ƒ domain space. This work presents a detailed study about the ambiguity domain (AD), their resemblance in the t-ƒ space and the significance of using such a representation. Further, a novel approach for the analysis and classification between normal and pathological speech signals is also provided. The quantitative measures obtained show comparable performance scores with the existing schemes. Evidently, gait from 51-normal and 161-abnormal subjects were studied and classified in this analysis. Results obtained from the quantitative analysis illustrate comparable performance characteristics with some of the recent schemes and a maximum classification accuracy of 97.5% is obtained.