Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi

Sinem Uysal; Husamettin Uysal; Bulent Bolat; Tulay Yildirim

doi:10.1109/SIU.2014.6830685

Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi

Translated title of the contribution: Classification of normal and abnormal lung sounds using wavelet coefficients

Sinem Uysal
, Husamettin Uysal
, Bulent Bolat
, Tulay Yildirim

Yildiz Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

Auscultation and analysing of lung sound is widely used in clinical area for diagnosis of lung diseases. Due to the non-stationary nature of lung sounds conventional frequency analysis technique is not a successful method for respiratory sound analysis. In this paper, classification of normal and abnormal lung sound using wavelet coefficient intended. Respiratory sounds are decomposed into the frequency subbands using wavelet transform and a set of statistical features are inspected from the sub-bands. Then, lung sounds classified as normal and abnormal using these statistical features. Artificial neural network and support vector machine are used for classification process.

Translated title of the contribution	Classification of normal and abnormal lung sounds using wavelet coefficients
Original language	Turkish
Title of host publication	2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
Publisher	IEEE Computer Society
Pages	2138-2141
Number of pages	4
ISBN (Print)	9781479948741
DOIs	https://doi.org/10.1109/SIU.2014.6830685
Publication status	Published - 2014
Externally published	Yes
Event	2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Trabzon, Turkey Duration: 23 Apr 2014 → 25 Apr 2014

Publication series

Name	2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings

Conference

Conference	2014 22nd Signal Processing and Communications Applications Conference, SIU 2014
Country/Territory	Turkey
City	Trabzon
Period	23/04/14 → 25/04/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/SIU.2014.6830685

Cite this

Uysal, S., Uysal, H., Bolat, B., & Yildirim, T. (2014). Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi. In 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings (pp. 2138-2141). Article 6830685 (2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings). IEEE Computer Society. https://doi.org/10.1109/SIU.2014.6830685

Uysal, Sinem ; Uysal, Husamettin ; Bolat, Bulent et al. / Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi. 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings. IEEE Computer Society, 2014. pp. 2138-2141 (2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings).

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title = "Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi",

abstract = "Auscultation and analysing of lung sound is widely used in clinical area for diagnosis of lung diseases. Due to the non-stationary nature of lung sounds conventional frequency analysis technique is not a successful method for respiratory sound analysis. In this paper, classification of normal and abnormal lung sound using wavelet coefficient intended. Respiratory sounds are decomposed into the frequency subbands using wavelet transform and a set of statistical features are inspected from the sub-bands. Then, lung sounds classified as normal and abnormal using these statistical features. Artificial neural network and support vector machine are used for classification process.",

keywords = "artificial neural network, respiratory sounds, support vector machine, wavelet coefficient",

author = "Sinem Uysal and Husamettin Uysal and Bulent Bolat and Tulay Yildirim",

year = "2014",

doi = "10.1109/SIU.2014.6830685",

language = "T{\"u}rk{\c c}e",

isbn = "9781479948741",

series = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",

publisher = "IEEE Computer Society",

pages = "2138--2141",

booktitle = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",

address = "United States",

note = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 ; Conference date: 23-04-2014 Through 25-04-2014",

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Uysal, S, Uysal, H, Bolat, B & Yildirim, T 2014, Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi. in 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings., 6830685, 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings, IEEE Computer Society, pp. 2138-2141, 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014, Trabzon, Turkey, 23/04/14. https://doi.org/10.1109/SIU.2014.6830685

Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi. / Uysal, Sinem; Uysal, Husamettin; Bolat, Bulent et al.
2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings. IEEE Computer Society, 2014. p. 2138-2141 6830685 (2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi

AU - Uysal, Sinem

AU - Uysal, Husamettin

AU - Bolat, Bulent

AU - Yildirim, Tulay

PY - 2014

Y1 - 2014

N2 - Auscultation and analysing of lung sound is widely used in clinical area for diagnosis of lung diseases. Due to the non-stationary nature of lung sounds conventional frequency analysis technique is not a successful method for respiratory sound analysis. In this paper, classification of normal and abnormal lung sound using wavelet coefficient intended. Respiratory sounds are decomposed into the frequency subbands using wavelet transform and a set of statistical features are inspected from the sub-bands. Then, lung sounds classified as normal and abnormal using these statistical features. Artificial neural network and support vector machine are used for classification process.

AB - Auscultation and analysing of lung sound is widely used in clinical area for diagnosis of lung diseases. Due to the non-stationary nature of lung sounds conventional frequency analysis technique is not a successful method for respiratory sound analysis. In this paper, classification of normal and abnormal lung sound using wavelet coefficient intended. Respiratory sounds are decomposed into the frequency subbands using wavelet transform and a set of statistical features are inspected from the sub-bands. Then, lung sounds classified as normal and abnormal using these statistical features. Artificial neural network and support vector machine are used for classification process.

KW - artificial neural network

KW - respiratory sounds

KW - support vector machine

KW - wavelet coefficient

UR - https://www.scopus.com/pages/publications/84903778392

U2 - 10.1109/SIU.2014.6830685

DO - 10.1109/SIU.2014.6830685

M3 - Konferans katkısı

AN - SCOPUS:84903778392

SN - 9781479948741

T3 - 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings

SP - 2138

EP - 2141

BT - 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings

PB - IEEE Computer Society

T2 - 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014

Y2 - 23 April 2014 through 25 April 2014

ER -

Uysal S, Uysal H, Bolat B, Yildirim T. Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi. In 2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings. IEEE Computer Society. 2014. p. 2138-2141. 6830685. (2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings). doi: 10.1109/SIU.2014.6830685

Saǧlikli ve saǧliksiz akciǧer seslerinin dalgacik katsayilari kullanilarak siniflandirilmasi

Abstract

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UN SDGs

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