Cough sound detection bases on EMD analysis and HMM recognition / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 277-281, 2009.
Article
Dans Chinois
| WPRIM
| ID: wpr-280216
ABSTRACT
Cough is one of the most common symptoms of many respiratory diseases; the characteristics of intensity and frequency of cough sound offer important clinical messages. When using these messages, we have need to differentiate the cough sound from the other sounds such as speech voice, throat clearing sound and nose clearing sound. In this paper, based on Empirical Mode Decomposition (EMD) and Hidden Markov Model (HMM), we proposed a novel method to analyze and detect cough sound. Employing the property of adaptive dyadic filter banks of EMD, we gained the mean energy distribution in the frequency domain of the signals in order to analyze the statistical characteristics of cough sound and of other sounds not accompanied by cough, and then we found the optimal characteristics for the recognition using HMM. The experiments on clinical date showed that this optimal characteristic method effectively improved the detective rate of cough sound.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Son (physique)
/
Spectrographie sonore
/
Reconnaissance automatique des formes
/
Chaines de Markov
/
Diagnostic assisté par ordinateur
/
Toux
/
Diagnostic
/
Méthodes
/
Monitorage physiologique
Type d'étude:
Etude diagnostique
/
Évaluation en économique de la santé
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2009
Type:
Article
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