Detecting Paroxysmal Coughing from Pertussis Cases Using Voice Recognition Technology

Abstract

BackgroundPertussis 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.MethodsWe 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.ResultsAfter 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.ConclusionOur 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.

Measurement of Cough Aerodynamics in Healthy Adults

“…Distinguishing a normal, healthy cough from a pathologic cough may not be obvious to clinicians based solely on their acoustic perception. 6 Furthermore, studies linking the acoustic or aerodynamic features of cough with underlying pathophysiology are limited to a few disease processes. 6,7…”

A Cough-Based Algorithm for Automatic Diagnosis of Pertussis

“…This uses neural networks to classify coughs with sensitivity and specificity of 93% and 92% respectively for 47 cough events only. In comparison, the cough classifier part of the diagnosis algorithm proposed in this paper uses significantly more cough events as part of the test data and achieves performance comparable to that in [ 17 ]. Its performance can be improved further by incorporating other types of coughs such as those in croup, bronchiolitis, asthma, and cold cough.…”

“…Algorithms for cough classification have also been published previously including those for pneumonia [ 16 , 31 , 32 ], wet and dry cough classification [ 14 , 15 , 33 ] and asthma [ 34 ]. However, the only other study for pertussis cough classification is published by Parker et al [ 17 ]. This uses neural networks to classify coughs with sensitivity and specificity of 93% and 92% respectively for 47 cough events only.…”

“…Their method achieved sensitivity and specificity of 81% and 50% respectively for a total of 375 cough samples from 25 subjects. Specific to pertussis coughs, Parker et al [ 17 ] studied the performance of three different classifiers for their classification. They used audio files of pertussis cough sounds available on the internet to create a dataset consisting of 16 non-pertussis cough signals and 31 pertussis cough signals.…”

A COVID-19 Multipurpose Platform

“…Lung ultrasonography images can also reveal signs of COVID-19 [3]. Sound processing (respiratory [4] or cough [5][6][7][8][9][10]) can also support the valid diagnosis of this virus. Speech modeling can track COVID-19 in asymptomatic/ symptomatic stages [11].…”