Document Detail

Using voice-recognition technology to eliminate cardiac cycle segmentation in automated heart sound diagnosis.
MedLine Citation:
PMID:  17432672     Owner:  NLM     Status:  MEDLINE    
Advanced digital signal processing has the potential to revolutionize the stethoscope through the use of mathematical algorithms to interpret heart sound acoustic information. In this study, a novel classification algorithm that does not require cardiac cycle segmentation was used for identifying differences between normal and diseased heart sounds. The heart sound signals were not separated into systole and diastole. A recordable electronic stethoscope was used to record the heart sounds of 163 echocardiogram patients. Mel-cepstrum and Principal Components Analysis were applied to the 60 recorded heart sounds and decision spaces were developed. The algorithm was tested using 100 novel patients. The specificity of the algorithm is 72.4% and the sensitivity is 63.4%.
Marie Guion Johnson; Ahmed Tewfik; K P Madhu; Arthur G Erdman
Publication Detail:
Type:  Evaluation Studies; Journal Article    
Journal Detail:
Title:  Biomedical instrumentation & technology / Association for the Advancement of Medical Instrumentation     Volume:  41     ISSN:  0899-8205     ISO Abbreviation:  Biomed Instrum Technol     Publication Date:    2007 Mar-Apr
Date Detail:
Created Date:  2007-04-16     Completed Date:  2007-05-16     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8905560     Medline TA:  Biomed Instrum Technol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  157-66     Citation Subset:  IM    
Department of Mechanical Engineering, University of Minnesota, Minneapolis 55455, USA.
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MeSH Terms
Aged, 80 and over
Artificial Intelligence*
Biotechnology / methods
Diagnosis, Computer-Assisted / methods*
Heart Auscultation / instrumentation,  methods*
Heart Murmurs / diagnosis*
Heart Valve Diseases / diagnosis*
Middle Aged
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Speech Recognition Software

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

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