• DocumentCode
    3706224
  • Title

    DeepCough: A deep convolutional neural network in a wearable cough detection system

  • Author

    Justice Amoh;Kofi Odame

  • Author_Institution
    Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    In this paper, we present a system that employs a wearable acoustic sensor and a deep convolutional neural network for detecting coughs. We evaluate the performance of our system on 14 healthy volunteers and compare it to that of other cough detection systems that have been reported in the literature. Experimental results show that our system achieves a classification sensitivity of 95.1% and a specificity of 99.5%.
  • Keywords
    "Hidden Markov models","Feature extraction","Mel frequency cepstral coefficient","Speech","Training","Microphones","Neural networks"
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Circuits and Systems Conference (BioCAS), 2015 IEEE
  • Type

    conf

  • DOI
    10.1109/BioCAS.2015.7348395
  • Filename
    7348395