On applying continuous wavelet transform in wheeze analysis

Styliani A. Taplidou; Leontios J. Hadjileontiadis; Ilias K. Kitsas; Konstantinos I. Panoulas; Thomas Penzel; Volker Gross; Stavros M. Panas

On applying continuous wavelet transform in wheeze analysis

Styliani A. Taplidou, Leontios J. Hadjileontiadis, Ilias K. Kitsas, Konstantinos I. Panoulas, Thomas Penzel, Volker Gross, Stavros M. Panas

Biomedical Engineering & Biotechnology

Research output: Contribution to journal › Conference article › peer-review

32 Scopus citations

Abstract

The identification of continuous abnormal lung sounds, like wheezes, in the total breathing cycle is of great importance in the diagnosis of obstructive airways pathologies. To this vein, the current work introduces an efficient method for the detection of wheezes, based on the time-scale representation of breath sound recordings. The employed Continuous Wavelet Transform is proven to be a valuable tool at this direction, when combined with scale-dependent thresholding. Analysis of lung sound recordings from 'wheezing' patients shows promising performance in the detection and extraction of wheezes from the background noise and reveals its potentiality for data-volume reduction in long-term wheezing screening, such as in sleep-laboratories.

Original language	British English
Pages (from-to)	3832-3835
Number of pages	4
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	26 V
State	Published - 2004
Event	Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States Duration: 1 Sep 2004 → 5 Sep 2004

Keywords

Breath sounds
Continuous wavelet transform
Detection/denoising
Scalogram
Wheezes

Cite this

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title = "On applying continuous wavelet transform in wheeze analysis",

abstract = "The identification of continuous abnormal lung sounds, like wheezes, in the total breathing cycle is of great importance in the diagnosis of obstructive airways pathologies. To this vein, the current work introduces an efficient method for the detection of wheezes, based on the time-scale representation of breath sound recordings. The employed Continuous Wavelet Transform is proven to be a valuable tool at this direction, when combined with scale-dependent thresholding. Analysis of lung sound recordings from 'wheezing' patients shows promising performance in the detection and extraction of wheezes from the background noise and reveals its potentiality for data-volume reduction in long-term wheezing screening, such as in sleep-laboratories.",

keywords = "Breath sounds, Continuous wavelet transform, Detection/denoising, Scalogram, Wheezes",

author = "Taplidou, {Styliani A.} and Hadjileontiadis, {Leontios J.} and Kitsas, {Ilias K.} and Panoulas, {Konstantinos I.} and Thomas Penzel and Volker Gross and Panas, {Stavros M.}",

year = "2004",

language = "British English",

volume = "26 V",

pages = "3832--3835",

journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

issn = "0589-1019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 ; Conference date: 01-09-2004 Through 05-09-2004",

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TY - JOUR

T1 - On applying continuous wavelet transform in wheeze analysis

AU - Taplidou, Styliani A.

AU - Hadjileontiadis, Leontios J.

AU - Kitsas, Ilias K.

AU - Panoulas, Konstantinos I.

AU - Penzel, Thomas

AU - Gross, Volker

AU - Panas, Stavros M.

PY - 2004

Y1 - 2004

N2 - The identification of continuous abnormal lung sounds, like wheezes, in the total breathing cycle is of great importance in the diagnosis of obstructive airways pathologies. To this vein, the current work introduces an efficient method for the detection of wheezes, based on the time-scale representation of breath sound recordings. The employed Continuous Wavelet Transform is proven to be a valuable tool at this direction, when combined with scale-dependent thresholding. Analysis of lung sound recordings from 'wheezing' patients shows promising performance in the detection and extraction of wheezes from the background noise and reveals its potentiality for data-volume reduction in long-term wheezing screening, such as in sleep-laboratories.

AB - The identification of continuous abnormal lung sounds, like wheezes, in the total breathing cycle is of great importance in the diagnosis of obstructive airways pathologies. To this vein, the current work introduces an efficient method for the detection of wheezes, based on the time-scale representation of breath sound recordings. The employed Continuous Wavelet Transform is proven to be a valuable tool at this direction, when combined with scale-dependent thresholding. Analysis of lung sound recordings from 'wheezing' patients shows promising performance in the detection and extraction of wheezes from the background noise and reveals its potentiality for data-volume reduction in long-term wheezing screening, such as in sleep-laboratories.

KW - Breath sounds

KW - Continuous wavelet transform

KW - Detection/denoising

KW - Scalogram

KW - Wheezes

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M3 - Conference article

AN - SCOPUS:11144317991

SN - 0589-1019

VL - 26 V

SP - 3832

EP - 3835

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004

Y2 - 1 September 2004 through 5 September 2004

ER -

On applying continuous wavelet transform in wheeze analysis

Abstract

Keywords

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