Document Detail

Automated algorithm for Wet/Dry cough sounds classification.
MedLine Citation:
PMID:  23366593     Owner:  NLM     Status:  MEDLINE    
Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully automated technology to classify cough into 'Wet' and 'Dry' categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.
V Swarnkar; U R Abeyratne; Yusuf A Amrulloh; Anne Chang
Related Documents :
24360193 - Enhanced response to pulmonary streptococcus pneumoniae infection is associated with pr...
24271263 - Respiratory rate estimation from the built-in cameras of smartphones and tablets.
16672243 - A categorization approach to automated ontological function annotation.
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  2012     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-31     Completed Date:  2013-08-26     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  3147-50     Citation Subset:  IM    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Cough / diagnosis*
Logistic Models

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

Previous Document:  Multi-lead QRS detection using window pairs.
Next Document:  Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity a...