NDLI: Wheeze detection using fractional Hilbert transform in the time domain

Content Provider	IEEE Xplore Digital Library
Author	Li Zhenzhen Wu Xiaoming
Copyright Year	2012
Description	Author affiliation: Sch. of Biomed. Sci. & Eng., South China Univ. of Technol., Guangzhou, China (Li Zhenzhen; Wu Xiaoming)
Abstract	The aim of this paper is to provide an alternative method to detect wheezes, using fractional Hilbert Transform in the time domain instead of traditional spectra domain. Typical waveforms of wheezes show sinusoidal morphological features. To extract the features, fractional Hilbert transforms with a series of different fractional orders have been applied to wheezes, and then a kind of texture images would be generated with dark and lightening strips interlaced with each other. The interlaced dark and lightening strips are corresponding to the sinusoidal patterns of wheezes. Then, by two following processing steps of linear projection of radon transform and local extreme value collection, features of the texture image could be expressed as a feature point in a plane. Judgments have been made according to the positions of the feature points, and the results have been compared to the judgment made by four experienced researchers via expanded-time waveform analysis. The detection accuracy was 90.5%, which validated that the method can be an efficient alternative way to detect wheezes.
Sponsorship	Circuits & Syst. Soc.
Starting Page	316
Ending Page	319
File Size	569384
Page Count	4
File Format	PDF
ISBN	9781467322911
e-ISBN	9781467322935
e-ISBN	9781467322928
DOI	10.1109/BioCAS.2012.6418433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-28
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Strips Fourier transforms Morphology Time domain analysis Feature extraction
Content Type	Text
Resource Type	Article

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