Document Detail


Automatic detection system for cough sounds as a symptom of abnormal health condition.
MedLine Citation:
PMID:  19273017     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The problem of attending to the health of the aged who live alone has became an important issue in developed countries. One way of solving the problem is to check their health condition by a remote-monitoring technique and support them with well-timed treatment. The purpose of this study is to develop an automatic system that can monitor a health condition in real time using acoustical information and detect an abnormal symptom. In this study, cough sound was chosen as a representative acoustical symptom of abnormal health conditions. For the development of the system distinguishing a cough sound from other environmental sounds, a hybrid model was proposed that consists of an artificial neural network (ANN) model and a hidden Markov model (HMM). The ANN model used energy cepstral coefficients obtained by filter banks based on human auditory characteristics as input parameters representing a spectral feature of a sound signal. Subsequently, an output of this ANN model and a filtered envelope of the signal were used for making an input sequence for the HMM that deals with the temporal variation of the sound signal. Compared with the conventional HMM using Mel-frequency cepstral coefficients, the proposed hybrid model improved recognition rates on low SNR from 5 dB down to -10 dB. Finally, a preliminary prototype of the automatic detection system was simply illustrated.
Authors:
Sung-Hwan Shin; Takeo Hashimoto; Shigeko Hatano
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-04-22
Journal Detail:
Title:  IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society     Volume:  13     ISSN:  1558-0032     ISO Abbreviation:  IEEE Trans Inf Technol Biomed     Publication Date:  2009 Jul 
Date Detail:
Created Date:  2009-07-09     Completed Date:  2009-10-05     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9712259     Medline TA:  IEEE Trans Inf Technol Biomed     Country:  United States    
Other Details:
Languages:  eng     Pagination:  486-93     Citation Subset:  IM    
Affiliation:
Department of Electrical and Mechanical Engineering, Seikei University, Tokyo 180-8633, Japan. soulshin@gmail.com
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MeSH Terms
Descriptor/Qualifier:
Aged
Cough / physiopathology*
Humans
Markov Chains
Models, Biological*
Monitoring, Physiologic / methods*
Neural Networks (Computer)*
Pattern Recognition, Automated / methods*
Respiratory Sounds / physiopathology*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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