Title :
Detection of cough signals in continuous audio recordings using hidden Markov models
Author :
Matos, Sergio ; Birring, Surinder S. ; Pavord, Ian D. ; Evans, David H.
Author_Institution :
Dept. of Med. Phys., Univ. Hospitals of Leicester, UK
fDate :
6/1/2006 12:00:00 AM
Abstract :
Cough is a common symptom of many respiratory diseases. The evaluation of its intensity and frequency of occurrence could provide valuable clinical information in the assessment of patients with chronic cough. In this paper we propose the use of hidden Markov models (HMMs) to automatically detect cough sounds from continuous ambulatory recordings. The recording system consists of a digital sound recorder and a microphone attached to the patient´s chest. The recognition algorithm follows a keyword-spotting approach, with cough sounds representing the keywords. It was trained on 821 min selected from 10 ambulatory recordings, including 2473 manually labeled cough events, and tested on a database of nine recordings from separate patients with a total recording time of 3060 min and comprising 2155 cough events. The average detection rate was 82% at a false alarm rate of seven events/h, when considering only events above an energy threshold relative to each recording´s average energy. These results suggest that HMMs can be applied to the detection of cough sounds from ambulatory patients. A postprocessing stage to perform a more detailed analysis on the detected events is under development, and could allow the rejection of some of the incorrectly detected events.
Keywords :
bioacoustics; diseases; hidden Markov models; medical signal detection; medical signal processing; 821 min; chronic cough; continuous ambulatory recordings; continuous audio recordings; cough signal detection; cough sounds; digital sound recorder; hidden Markov models; key word-spotting approach; microphone; recognition algorithm; respiratory diseases; Audio recording; Clinical diagnosis; Digital recording; Diseases; Event detection; Frequency; Hidden Markov models; Microphones; Signal detection; Testing; Automatic detection; cough counts; cough monitor; hidden Markov models; Algorithms; Artificial Intelligence; Auscultation; Cough; Diagnosis, Computer-Assisted; Humans; Markov Chains; Monitoring, Ambulatory; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Sound Spectrography;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2006.873548