Nonlinear dynamic analysis of pathological voices

Chunying Fang; Haifeng Li; Lin Ma; Xiaopeng Zhang

doi:10.1007/978-3-642-39482-9_46

Nonlinear dynamic analysis of pathological voices

Chunying Fang
, Haifeng Li
, Lin Ma
, Xiaopeng Zhang

School of Computer Science and Technology, Harbin Institute of Technology
Heilongjiang University of Science and Technology

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

3 Scopus citations

Abstract

Research on the human health evaluation through sound analysis is now attracting more and more researchers in the world. Acoustic analysis could be a useful tool to diagnose the disease. Therefore, pathological voices can be used to evaluate the health status as a complementary technique, such as bronchitis. In this article, we proposed a nonlinear dynamic method to analysis pathological voices. Firstly, pathological voices were preprocessed and numerous features were extracted. Secondly, a binary coded chromosome genetic algorithm (GA) was applied as feature selection method to optimize feature descriptor set. The experimental results show that GA, PCA along with support vector machine (SVM) has the best performance in the pathology voices diagnosis.

Original language	English
Title of host publication	Intelligent Computing Theories and Technology - 9th International Conference, ICIC 2013, Proceedings
Publisher	Springer Verlag
Pages	401-409
Number of pages	9
ISBN (Print)	9783642394812
DOIs	https://doi.org/10.1007/978-3-642-39482-9_46
State	Published - 2013
Externally published	Yes
Event	9th International Conference on Intelligent Computing, ICIC 2013 - Nanning, China Duration: 28 Jul 2013 → 31 Jul 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7996 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Intelligent Computing, ICIC 2013
Country/Territory	China
City	Nanning
Period	28/07/13 → 31/07/13

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

GA
PCA
Pathology voice
SVM

Access to Document

10.1007/978-3-642-39482-9_46

Cite this

Fang, C., Li, H., Ma, L., & Zhang, X. (2013). Nonlinear dynamic analysis of pathological voices. In Intelligent Computing Theories and Technology - 9th International Conference, ICIC 2013, Proceedings (pp. 401-409). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7996 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-642-39482-9_46

Fang, Chunying ; Li, Haifeng ; Ma, Lin et al. / Nonlinear dynamic analysis of pathological voices. Intelligent Computing Theories and Technology - 9th International Conference, ICIC 2013, Proceedings. Springer Verlag, 2013. pp. 401-409 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Nonlinear dynamic analysis of pathological voices",

abstract = "Research on the human health evaluation through sound analysis is now attracting more and more researchers in the world. Acoustic analysis could be a useful tool to diagnose the disease. Therefore, pathological voices can be used to evaluate the health status as a complementary technique, such as bronchitis. In this article, we proposed a nonlinear dynamic method to analysis pathological voices. Firstly, pathological voices were preprocessed and numerous features were extracted. Secondly, a binary coded chromosome genetic algorithm (GA) was applied as feature selection method to optimize feature descriptor set. The experimental results show that GA, PCA along with support vector machine (SVM) has the best performance in the pathology voices diagnosis.",

keywords = "GA, PCA, Pathology voice, SVM",

author = "Chunying Fang and Haifeng Li and Lin Ma and Xiaopeng Zhang",

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booktitle = "Intelligent Computing Theories and Technology - 9th International Conference, ICIC 2013, Proceedings",

address = "德国",

note = "9th International Conference on Intelligent Computing, ICIC 2013 ; Conference date: 28-07-2013 Through 31-07-2013",

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Fang, C, Li, H , Ma, L & Zhang, X 2013, Nonlinear dynamic analysis of pathological voices. in Intelligent Computing Theories and Technology - 9th International Conference, ICIC 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7996 LNAI, Springer Verlag, pp. 401-409, 9th International Conference on Intelligent Computing, ICIC 2013, Nanning, China, 28/07/13. https://doi.org/10.1007/978-3-642-39482-9_46

Nonlinear dynamic analysis of pathological voices. / Fang, Chunying; Li, Haifeng ; Ma, Lin et al.
Intelligent Computing Theories and Technology - 9th International Conference, ICIC 2013, Proceedings. Springer Verlag, 2013. p. 401-409 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7996 LNAI).

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

TY - GEN

T1 - Nonlinear dynamic analysis of pathological voices

AU - Fang, Chunying

AU - Li, Haifeng

AU - Ma, Lin

AU - Zhang, Xiaopeng

PY - 2013

Y1 - 2013

N2 - Research on the human health evaluation through sound analysis is now attracting more and more researchers in the world. Acoustic analysis could be a useful tool to diagnose the disease. Therefore, pathological voices can be used to evaluate the health status as a complementary technique, such as bronchitis. In this article, we proposed a nonlinear dynamic method to analysis pathological voices. Firstly, pathological voices were preprocessed and numerous features were extracted. Secondly, a binary coded chromosome genetic algorithm (GA) was applied as feature selection method to optimize feature descriptor set. The experimental results show that GA, PCA along with support vector machine (SVM) has the best performance in the pathology voices diagnosis.

AB - Research on the human health evaluation through sound analysis is now attracting more and more researchers in the world. Acoustic analysis could be a useful tool to diagnose the disease. Therefore, pathological voices can be used to evaluate the health status as a complementary technique, such as bronchitis. In this article, we proposed a nonlinear dynamic method to analysis pathological voices. Firstly, pathological voices were preprocessed and numerous features were extracted. Secondly, a binary coded chromosome genetic algorithm (GA) was applied as feature selection method to optimize feature descriptor set. The experimental results show that GA, PCA along with support vector machine (SVM) has the best performance in the pathology voices diagnosis.

KW - GA

KW - PCA

KW - Pathology voice

KW - SVM

UR - https://www.scopus.com/pages/publications/84883204900

U2 - 10.1007/978-3-642-39482-9_46

DO - 10.1007/978-3-642-39482-9_46

M3 - 会议稿件

AN - SCOPUS:84883204900

SN - 9783642394812

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 401

EP - 409

BT - Intelligent Computing Theories and Technology - 9th International Conference, ICIC 2013, Proceedings

PB - Springer Verlag

T2 - 9th International Conference on Intelligent Computing, ICIC 2013

Y2 - 28 July 2013 through 31 July 2013

ER -

Fang C, Li H , Ma L, Zhang X. Nonlinear dynamic analysis of pathological voices. In Intelligent Computing Theories and Technology - 9th International Conference, ICIC 2013, Proceedings. Springer Verlag. 2013. p. 401-409. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-39482-9_46

Nonlinear dynamic analysis of pathological voices

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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