NDLI: Exploiting the ambiguity domain for non-stationary biomedical signal classification

Content Provider	IEEE Xplore Digital Library
Author	Sugavaneswaran, L. Umapathy, K. Krishnan, S.
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering in Ryerson University, Toronto, ON M5B 2K3, Canada (Sugavaneswaran, L.; Umapathy, K.; Krishnan, S.)
Abstract	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.
Starting Page	1934
Ending Page	1937
File Size	548052
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5627723
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Time frequency analysis Pathology Speech Accuracy Support vector machine classification Databases classification Ambiguity domain non-stationary random processes time-frequency analysis pathological speech pattern recognition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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