Electroglottographic signal acquisition and neural network based classification for pathology

S. G. Nayak; Jagdish Nayak

doi:10.1007/978-3-540-68017-8_16

Electroglottographic signal acquisition and neural network based classification for pathology

S. G. Nayak^*
, Jagdish Nayak

^*Corresponding author for this work

Manipal Institute of Technology, Manipal

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

2 Citations (Scopus)

Abstract

The method is used to register the laryngeal behavior indirectly by measuring change in the electrical impedance across the throat during speak or voice. The RF carrier signal is amplitude modulated by the modulating speech/ voice signal and the dc component from the demodulated signal is extracted. The variations in the dc component corresponds to the vocal fold abduction/laryngeal movement. For normal and pathology conditions the results are recorded. These values form a feature vector which reveal information regarding pathology. Then a classical multilayer feed forward neural network with back propagation algorithm is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered.

Original language	English
Title of host publication	3rd Kuala Lumpur International Conference on Biomedical Engineering 2006
Editors	Fatimah Ibrahim, Noor Azuan Abu Osman, Juliana Usman, Nahrizul Adib Kadri
Publisher	Springer Verlag
Pages	59-62
Number of pages	4
Volume	15
ISBN (Electronic)	9783540680161
DOIs	https://doi.org/10.1007/978-3-540-68017-8_16
Publication status	Published - 2007
Event	3rd Kuala Lumpur International Conference on Biomedical Engineering, Biomed 2006 - Kuala Lumpur, Malaysia Duration: 11-12-2006 → 14-12-2006

Publication series

Name	IFMBE Proceedings
Volume	15
ISSN (Print)	1680-0737
ISSN (Electronic)	1433-9277

Conference

Conference	3rd Kuala Lumpur International Conference on Biomedical Engineering, Biomed 2006
Country/Territory	Malaysia
City	Kuala Lumpur
Period	11-12-06 → 14-12-06

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Bioengineering

Access to Document

10.1007/978-3-540-68017-8_16

Cite this

Nayak, S. G., & Nayak, J. (2007). Electroglottographic signal acquisition and neural network based classification for pathology. In F. Ibrahim, N. A. Abu Osman, J. Usman, & N. A. Kadri (Eds.), 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006 (Vol. 15, pp. 59-62). (IFMBE Proceedings; Vol. 15). Springer Verlag. https://doi.org/10.1007/978-3-540-68017-8_16

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title = "Electroglottographic signal acquisition and neural network based classification for pathology",

abstract = "The method is used to register the laryngeal behavior indirectly by measuring change in the electrical impedance across the throat during speak or voice. The RF carrier signal is amplitude modulated by the modulating speech/ voice signal and the dc component from the demodulated signal is extracted. The variations in the dc component corresponds to the vocal fold abduction/laryngeal movement. For normal and pathology conditions the results are recorded. These values form a feature vector which reveal information regarding pathology. Then a classical multilayer feed forward neural network with back propagation algorithm is employed to serve as a classifier of the feature vector, giving 100\% successful results for the specific data set considered.",

author = "Nayak, \{S. G.\} and Jagdish Nayak",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.; 3rd Kuala Lumpur International Conference on Biomedical Engineering, Biomed 2006 ; Conference date: 11-12-2006 Through 14-12-2006",

year = "2007",

doi = "10.1007/978-3-540-68017-8\_16",

language = "English",

volume = "15",

series = "IFMBE Proceedings",

publisher = "Springer Verlag",

pages = "59--62",

editor = "Fatimah Ibrahim and \{Abu Osman\}, \{Noor Azuan\} and Juliana Usman and Kadri, \{Nahrizul Adib\}",

booktitle = "3rd Kuala Lumpur International Conference on Biomedical Engineering 2006",

address = "Germany",

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Nayak, SG & Nayak, J 2007, Electroglottographic signal acquisition and neural network based classification for pathology. in F Ibrahim, NA Abu Osman, J Usman & NA Kadri (eds), 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. vol. 15, IFMBE Proceedings, vol. 15, Springer Verlag, pp. 59-62, 3rd Kuala Lumpur International Conference on Biomedical Engineering, Biomed 2006, Kuala Lumpur, Malaysia, 11-12-06. https://doi.org/10.1007/978-3-540-68017-8_16

Electroglottographic signal acquisition and neural network based classification for pathology. / Nayak, S. G.; Nayak, Jagdish.
3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. ed. / Fatimah Ibrahim; Noor Azuan Abu Osman; Juliana Usman; Nahrizul Adib Kadri. Vol. 15 Springer Verlag, 2007. p. 59-62 (IFMBE Proceedings; Vol. 15).

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

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Y1 - 2007

N2 - The method is used to register the laryngeal behavior indirectly by measuring change in the electrical impedance across the throat during speak or voice. The RF carrier signal is amplitude modulated by the modulating speech/ voice signal and the dc component from the demodulated signal is extracted. The variations in the dc component corresponds to the vocal fold abduction/laryngeal movement. For normal and pathology conditions the results are recorded. These values form a feature vector which reveal information regarding pathology. Then a classical multilayer feed forward neural network with back propagation algorithm is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered.

AB - The method is used to register the laryngeal behavior indirectly by measuring change in the electrical impedance across the throat during speak or voice. The RF carrier signal is amplitude modulated by the modulating speech/ voice signal and the dc component from the demodulated signal is extracted. The variations in the dc component corresponds to the vocal fold abduction/laryngeal movement. For normal and pathology conditions the results are recorded. These values form a feature vector which reveal information regarding pathology. Then a classical multilayer feed forward neural network with back propagation algorithm is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered.

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Electroglottographic signal acquisition and neural network based classification for pathology

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