Conference proceeding
Audio-Based Cough Detection in Clinic Waiting Rooms
2022 IEEE 10TH INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI 2022), pp.182-191
IEEE International Conference on Healthcare Informatics
01/01/2022
DOI: 10.1109/ICHI54592.2022.00037
Abstract
Automated cough detection has significant applications for the surveillance of diseases and supports medical decisions, as cough sounds can be a useful biomarker. However, the implementation and evaluation of robust cough detection models can be challenging due to the lack of real-world data. This paper introduces and makes available a collection of 2,883 coughs and 3,074 non-cough sounds recorded in clinic waiting rooms that we hope will become a baseline for this task. Using this dataset, we evaluate different convolutional network architectures for classifying short audio segments as cough or non-cough. An ensemble model of convolutional neuronal networks provides the most robust performance and has a ROC AUC of 98.1%. Equally important, we construct a cough counter that incorporates the ensemble model to compute the number of coughs per day. Then, a simple linear model estimates the number of visits in which the patients report cough symptoms from the cough counts. This simple regression model can predict the number of cough visits in the clinic with an absolute mean error of 4.26 cough visits per day. Using additional information about when patients are in the clinic helps a similar regression model reach a mean absolute error of 3.65 cough visits per day. These results demonstrate the feasibility of using cough detection as a biomarker for the spread of respiratory viruses within the community.
Details
- Title: Subtitle
- Audio-Based Cough Detection in Clinic Waiting Rooms
- Creators
- Yumna Anwar - University of IowaSean M. Mullan - University of IowaOctav Chipara - University of IowaAlberto M. Segre - University of IowaPhilip Polgreen - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2022 IEEE 10TH INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI 2022), pp.182-191
- Series
- IEEE International Conference on Healthcare Informatics
- DOI
- 10.1109/ICHI54592.2022.00037
- ISSN
- 2575-2634
- eISSN
- 2575-2626
- Publisher
- IEEE
- Number of pages
- 10
- Grant note
- IIS-1838830; CNS1750155 / NSF; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2022
- Academic Unit
- Infectious Diseases; Epidemiology; Nursing; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Computer Science; Internal Medicine
- Record Identifier
- 9984359776602771
Metrics
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