Towards a Wearable Cough Detector Based on Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • External authors:
  • Prad Kadambi
  • Abinash Mohanty
  • Hao Ren
  • Kevin McGuinnes
  • Armin Furtwaengler
  • Roberto Slepetys
  • Zheng Yang
  • Jae Sun Seo
  • Junseok Chae
  • Yu Cao
  • Visar Berisha

Abstract

Persistent cough is a symptom common to a number of respiratory disorders; however, reliable monitoring of cough frequency and cough severity over an extended period of time can be a challenge. Traditional methods involve subjective evaluation by care providers or patient self-reports. As an alternative, we propose an objective method for monitoring cough using a wearable microphone. We collected 24-hour audio recordings from 9 patients suffering from chronic obstructive pulmonary disease, asthma, and lung cancer using the VitaloJAK wearable microphone. Trained professionals carefully listened to each audio stream and manually labeled each cough event. Using this data, we propose a new neural-network-based cough detection scheme. A pre-processing algorithm is used to estimate the start and end of each cough and the deep neural network is trained using each cough instance. Experiments demonstrate an average leave-one-participant-out cross-validation specificity and sensitivity of 93.7% and 97.6% respectively.

Bibliographical metadata

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherIEEE
Pages2161-2165
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Event duration: 15 Apr 201820 Apr 2018

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period15/04/1820/04/18