Document Detail


A framework for the analysis of acoustical cardiac signals.
MedLine Citation:
PMID:  17405372     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Skilled cardiologists perform cardiac auscultation, acquiring and interpreting heart sounds, by implicitly carrying out a sequence of steps. These include discarding clinically irrelevant beats, selectively tuning in to particular frequencies and aggregating information across time to make a diagnosis. In this paper, we formalize a series of analytical stages for processing heart sounds, propose algorithms to enable computers to approximate these steps, and investigate the effectiveness of each step in extracting relevant information from actual patient data. Through such reasoning, we provide insight into the relative difficulty of the various tasks involved in the accurate interpretation of heart sounds. We also evaluate the contribution of each analytical stage in the overall assessment of patients. We expect our framework and associated software to be useful to educators wanting to teach cardiac auscultation, and to primary care physicians, who can benefit from presentation tools for computer-assisted diagnosis of cardiac disorders. Researchers may also employ the comprehensive processing provided by our framework to develop more powerful, fully automated auscultation applications.
Authors:
Zeeshan Syed; Daniel Leeds; Dorothy Curtis; Francesca Nesta; Robert A Levine; John Guttag
Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  54     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2007 Apr 
Date Detail:
Created Date:  2007-04-04     Completed Date:  2007-04-24     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  651-62     Citation Subset:  IM    
Affiliation:
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. zhs@csail.mit.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Artificial Intelligence
Cluster Analysis
Diagnosis, Computer-Assisted / methods*
Heart Auscultation / methods*
Heart Murmurs / diagnosis*,  physiopathology*
Humans
Mitral Valve Insufficiency / diagnosis*,  physiopathology*
Pattern Recognition, Automated / methods
Reproducibility of Results
Sensitivity and Specificity
Sound Spectrography / methods*

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


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