Unsupervised classification of respiratory sound signal into snore/no-snore classes

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Volumn , Issue , 2010, Pages 3666-3669

  Azarbarzin, Ali ^a   Moussavi, Zahra ^a,b  

^a UNIVERSITY OF MANITOBA   (Canada)

^b TRLABS   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DETECTION ALGORITHM; FEATURE SPACE; K-MEANS CLUSTERING ALGORITHM; MANUAL ANNOTATION; POLYSOMNOGRAPHY; RESPIRATORY SOUNDS; SOUND SIGNAL; SUB-BAND ENERGY FEATURE; UNSUPERVISED CLASSIFICATION;

MICROPHONES; PRINCIPAL COMPONENT ANALYSIS;

CLUSTERING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUSCULTATION; AUTOMATED PATTERN RECOGNITION; CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; EVALUATION STUDY; FEMALE; HUMAN; MALE; METHODOLOGY; MIDDLE AGED; POLYSOMNOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SNORING; SOUND DETECTION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; AUSCULTATION; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; POLYSOMNOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SNORING; SOUND SPECTROGRAPHY;

EID: 78650822043     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2010.5627650     Document Type: Conference Paper

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