title: Detecting paroxysmal coughing from pertussis cases using voice recognition technology.
abstract: 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 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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: GTD Unlimited, Oxford, Mississippi, United States of America.
authors.forename: Danny
authors.initials: D
authors.lastname: Parker
authors.affiliation: Department of Electrical and Computer Engineering, Temple University, Philadelphia, Pennsylvania, United States of America.
authors.forename: Joseph
authors.initials: J
authors.lastname: Picone
authors.affiliation: Department of Electrical and Computer Engineering, Temple University, Philadelphia, Pennsylvania, United States of America.
authors.forename: Amir
authors.initials: A
authors.lastname: Harati
authors.affiliation: Department of Electrical and Computer Engineering, Temple University, Philadelphia, Pennsylvania, United States of America.
authors.forename: Shuang
authors.initials: S
authors.lastname: Lu
authors.affiliation: Jenkyns Oxford High School, Oxford, United Kindgom.
authors.forename: Marion H
authors.initials: MH
authors.lastname: Jenkyns
authors.affiliation: Departments of Internal Medicine and Epidemiology, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Philip M
authors.initials: PM
authors.lastname: Polgreen
mesh_terms: D000162:Acoustics; D000465:Algorithms; D001185:Artificial Intelligence; D002648:Child; D003371:Cough / Q000175:diagnosis* / Q000503:physiopathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D006801:Humans; D016571:Neural Networks, Computer; D049250:Speech Recognition Software; D014917:Whooping Cough / Q000175:diagnosis* / Q000503:physiopathology