Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression

Dry cough has been recognized as a common symptom of coronavirus respiratory diseases, emphasizing the importance of accurately identifying and classifying cough types to mitigate the spread of the disease. The study employs various acoustic features and a Python-based data processing algorithm to e...

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Main Authors: Sean Andrei, P. Co, Madamba, Claudine Anne J., Guico, Ma. Leonora, Galicia, Jan Kevin A
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Published: Archīum Ateneo 2023
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Online Access:https://archium.ateneo.edu/ecce-faculty-pubs/147
https://doi.org/10.1109/ICCCE58854.2023.10246105
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.ecce-faculty-pubs-11412024-02-21T07:11:51Z Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression Sean Andrei, P. Co Madamba, Claudine Anne J. Guico, Ma. Leonora Galicia, Jan Kevin A Dry cough has been recognized as a common symptom of coronavirus respiratory diseases, emphasizing the importance of accurately identifying and classifying cough types to mitigate the spread of the disease. The study employs various acoustic features and a Python-based data processing algorithm to extract and analyze the Energy Envelope Peaks, Crest Factors, Zero-Crossings, and Formant Frequencies 1-4 from a dataset of 870 cough samples. The analysis of 347 wet cough sound samples and 523 dry cough samples reveals distinctive characteristics. Wet coughs exhibit a higher number of peaks and zero-crossings, while dry coughs display a slightly higher crest factor on average. Moreover, the F1 and F2 formant frequencies are higher in wet coughs, whereas the F3 and F4 formant frequencies are higher in dry coughs. To classify the cough types, both Support Vector Machine (SVM) and Logistic Regression Method (LRM) classifiers are trained using the identified features. The SVM classifier achieves an average accuracy of 71.26%, sensitivity of 72.73%, specificity of 70.87%, and F1-score of 67.94% during testing. Similarly, the LRM classifier achieves an accuracy of 71.26%, sensitivity of 70.59%, specificity of 71.55%, and F1-score of 68.45%. Such automated classification systems have the potential to aid in the early detection and monitoring of respiratory diseases in enclosed spaces. 2023-01-01T08:00:00Z text https://archium.ateneo.edu/ecce-faculty-pubs/147 https://doi.org/10.1109/ICCCE58854.2023.10246105 Electronics, Computer, and Communications Engineering Faculty Publications Archīum Ateneo cough classification logistic regression support vector machine wet and dry cough Biomedical Electrical and Computer Engineering Engineering
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic cough classification
logistic regression
support vector machine
wet and dry cough
Biomedical
Electrical and Computer Engineering
Engineering
spellingShingle cough classification
logistic regression
support vector machine
wet and dry cough
Biomedical
Electrical and Computer Engineering
Engineering
Sean Andrei, P. Co
Madamba, Claudine Anne J.
Guico, Ma. Leonora
Galicia, Jan Kevin A
Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression
description Dry cough has been recognized as a common symptom of coronavirus respiratory diseases, emphasizing the importance of accurately identifying and classifying cough types to mitigate the spread of the disease. The study employs various acoustic features and a Python-based data processing algorithm to extract and analyze the Energy Envelope Peaks, Crest Factors, Zero-Crossings, and Formant Frequencies 1-4 from a dataset of 870 cough samples. The analysis of 347 wet cough sound samples and 523 dry cough samples reveals distinctive characteristics. Wet coughs exhibit a higher number of peaks and zero-crossings, while dry coughs display a slightly higher crest factor on average. Moreover, the F1 and F2 formant frequencies are higher in wet coughs, whereas the F3 and F4 formant frequencies are higher in dry coughs. To classify the cough types, both Support Vector Machine (SVM) and Logistic Regression Method (LRM) classifiers are trained using the identified features. The SVM classifier achieves an average accuracy of 71.26%, sensitivity of 72.73%, specificity of 70.87%, and F1-score of 67.94% during testing. Similarly, the LRM classifier achieves an accuracy of 71.26%, sensitivity of 70.59%, specificity of 71.55%, and F1-score of 68.45%. Such automated classification systems have the potential to aid in the early detection and monitoring of respiratory diseases in enclosed spaces.
format text
author Sean Andrei, P. Co
Madamba, Claudine Anne J.
Guico, Ma. Leonora
Galicia, Jan Kevin A
author_facet Sean Andrei, P. Co
Madamba, Claudine Anne J.
Guico, Ma. Leonora
Galicia, Jan Kevin A
author_sort Sean Andrei, P. Co
title Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression
title_short Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression
title_full Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression
title_fullStr Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression
title_full_unstemmed Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression
title_sort wet and dry cough classification system using support vector machine and logistic regression
publisher Archīum Ateneo
publishDate 2023
url https://archium.ateneo.edu/ecce-faculty-pubs/147
https://doi.org/10.1109/ICCCE58854.2023.10246105
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