Detecting paroxysmal coughing from pertussis cases using voice recognition technology
Genre
Journal ArticleDate
2013-12-31Author
Parker, DPicone, J
Harati, A
Lu, S
Jenkyns, MH
Polgreen, PM
Subject
AcousticsAlgorithms
Artificial Intelligence
Child
Cough
Diagnosis, Computer-Assisted
Diagnosis, Differential
Humans
Neural Networks, Computer
Speech Recognition Software
Whooping Cough
Permanent link to this record
http://hdl.handle.net/20.500.12613/5345
Metadata
Show full item recordDOI
10.1371/journal.pone.0082971Abstract
Background: 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. Methods: 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 Melfrequency 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. Results: 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. Conclusion: 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. © 2013 Parker et al.Citation to related work
Public Library of Science (PLoS)Has part
PLoS ONEADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.eduae974a485f413a2113503eed53cd6c53
http://dx.doi.org/10.34944/dspace/5327