Detecting paroxysmal coughing from pertussis cases using voice recognition technology.
Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinic...
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doaj-dbb5bf02aa434ac0b5ab3997547bf3292020-11-25T02:33:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01812e8297110.1371/journal.pone.0082971Detecting paroxysmal coughing from pertussis cases using voice recognition technology.Danny ParkerJoseph PiconeAmir HaratiShuang LuMarion H JenkynsPhilip M PolgreenPertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs.We collected a series of recordings representing pertussis, croup and miscellaneous coughing by children. We manually categorized coughs as either pertussis or non-pertussis, and extracted features for each category. We used Mel-frequency cepstral coefficients (MFCC), a sampling rate of 16 KHz, a frame Duration of 25 msec, and a frame rate of 10 msec. The coughs were filtered. Each cough was divided into 3 sections of proportion 3-4-3. The average of the 13 MFCCs for each section was computed and made into a 39-element feature vector used for the classification. We used the following machine learning algorithms: Neural Networks, K-Nearest Neighbor (KNN), and a 200 tree Random Forest (RF). Data were reserved for cross-validation of the KNN and RF. The Neural Network was trained 100 times, and the averaged results are presented.After categorization, we had 16 examples of non-pertussis coughs and 31 examples of pertussis coughs. Over 90% of all pertussis coughs were properly classified as pertussis. The error rates were: Type I errors of 7%, 12%, and 25% and Type II errors of 8%, 0%, and 0%, using the Neural Network, Random Forest, and KNN, respectively.Our results suggest that we can build a robust classifier to assist clinicians and the public to help identify pertussis cases in children presenting with typical symptoms.http://europepmc.org/articles/PMC3876998?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Danny Parker Joseph Picone Amir Harati Shuang Lu Marion H Jenkyns Philip M Polgreen |
spellingShingle |
Danny Parker Joseph Picone Amir Harati Shuang Lu Marion H Jenkyns Philip M Polgreen Detecting paroxysmal coughing from pertussis cases using voice recognition technology. PLoS ONE |
author_facet |
Danny Parker Joseph Picone Amir Harati Shuang Lu Marion H Jenkyns Philip M Polgreen |
author_sort |
Danny Parker |
title |
Detecting paroxysmal coughing from pertussis cases using voice recognition technology. |
title_short |
Detecting paroxysmal coughing from pertussis cases using voice recognition technology. |
title_full |
Detecting paroxysmal coughing from pertussis cases using voice recognition technology. |
title_fullStr |
Detecting paroxysmal coughing from pertussis cases using voice recognition technology. |
title_full_unstemmed |
Detecting paroxysmal coughing from pertussis cases using voice recognition technology. |
title_sort |
detecting paroxysmal coughing from pertussis cases using voice recognition technology. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
description |
Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs.We collected a series of recordings representing pertussis, croup and miscellaneous coughing by children. We manually categorized coughs as either pertussis or non-pertussis, and extracted features for each category. We used Mel-frequency cepstral coefficients (MFCC), a sampling rate of 16 KHz, a frame Duration of 25 msec, and a frame rate of 10 msec. The coughs were filtered. Each cough was divided into 3 sections of proportion 3-4-3. The average of the 13 MFCCs for each section was computed and made into a 39-element feature vector used for the classification. We used the following machine learning algorithms: Neural Networks, K-Nearest Neighbor (KNN), and a 200 tree Random Forest (RF). Data were reserved for cross-validation of the KNN and RF. The Neural Network was trained 100 times, and the averaged results are presented.After categorization, we had 16 examples of non-pertussis coughs and 31 examples of pertussis coughs. Over 90% of all pertussis coughs were properly classified as pertussis. The error rates were: Type I errors of 7%, 12%, and 25% and Type II errors of 8%, 0%, and 0%, using the Neural Network, Random Forest, and KNN, respectively.Our results suggest that we can build a robust classifier to assist clinicians and the public to help identify pertussis cases in children presenting with typical symptoms. |
url |
http://europepmc.org/articles/PMC3876998?pdf=render |
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