Document Detail


Segmentation of heart sound recordings by a duration-dependent hidden Markov model.
MedLine Citation:
PMID:  20208091     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Digital stethoscopes offer new opportunities for computerized analysis of heart sounds. Segmentation of heart sound recordings into periods related to the first and second heart sound (S1 and S2) is fundamental in the analysis process. However, segmentation of heart sounds recorded with handheld stethoscopes in clinical environments is often complicated by background noise. A duration-dependent hidden Markov model (DHMM) is proposed for robust segmentation of heart sounds. The DHMM identifies the most likely sequence of physiological heart sounds, based on duration of the events, the amplitude of the signal envelope and a predefined model structure. The DHMM model was developed and tested with heart sounds recorded bedside with a commercially available handheld stethoscope from a population of patients referred for coronary arterioangiography. The DHMM identified 890 S1 and S2 sounds out of 901 which corresponds to 98.8% (CI: 97.8-99.3%) sensitivity in 73 test patients and 13 misplaced sounds out of 903 identified sounds which corresponds to 98.6% (CI: 97.6-99.1%) positive predictivity. These results indicate that the DHMM is an appropriate model of the heart cycle and suitable for segmentation of clinically recorded heart sounds.
Authors:
S E Schmidt; C Holst-Hansen; C Graff; E Toft; J J Struijk
Publication Detail:
Type:  Evaluation Studies; Journal Article     Date:  2010-03-05
Journal Detail:
Title:  Physiological measurement     Volume:  31     ISSN:  1361-6579     ISO Abbreviation:  Physiol Meas     Publication Date:  2010 Apr 
Date Detail:
Created Date:  2010-03-19     Completed Date:  2010-06-23     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9306921     Medline TA:  Physiol Meas     Country:  England    
Other Details:
Languages:  eng     Pagination:  513-29     Citation Subset:  IM    
Affiliation:
Department of Health Science and Technology, Aalborg University, Denmark. sschmidt@hst.aau.dk
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Artificial Intelligence*
Diagnosis, Computer-Assisted / methods*
Heart Auscultation / methods*
Humans
Markov Chains
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity

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


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