The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Récemment, de nombreuses recherches concernant l'analyse des bruits cardiaques ont été traitées avec le développement du traitement du signal numérique et des composants électroniques. Mais il existe peu de recherches sur la reconnaissance des bruits cardiaques, en particulier des bruits cardiaques à cycle complet. Dans cet article, trois nouvelles méthodes de reconnaissance des sons cardiaques à cycle complet ont été proposées. La première méthode reconnaît les caractéristiques du bruit cardiaque en intégrant des pics importants et en analysant les variables statistiques dans le domaine temporel. La deuxième méthode construit une base de données par analyse des composantes principales sur les bruits cardiaques d'entraînement dans le domaine temporel. Cette base de données est utilisée pour reconnaître une nouvelle entrée de bruit cardiaque. La troisième méthode construit le même type de base de données non pas dans le domaine temporel mais dans le domaine temps-fréquence. Nous classons les bruits cardiaques en sept classes telles que la classe normale (NO), la classe de souffle pré-systolique (PS), la classe de souffle systolique précoce (ES), la classe de souffle systolique tardif (LS), la classe de souffle diastolique précoce (ED), la classe tardive. classe de souffle diastolique (LD) et classe de souffle continu (CM). En conséquence, nous avons pu vérifier que la troisième méthode est plus efficace pour reconnaître les caractéristiques des bruits cardiaques que les autres et aussi que toute recherche précédente. Les taux de reconnaissance de la troisième méthode sont de 100 % pour NO, 80 % pour PS et ES, 67 % pour LS, 93 pour ED, 80 % pour LD et 30 % pour CM.
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Sang Min LEE, In Young KIM, Seung Hong HONG, "Heart Sound Recognition by New Methods Using the Full Cardiac Cycled Sound Data" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 4, pp. 521-529, April 2001, doi: .
Abstract: Recently many researches concerning heart sound analysis are being processed with development of digital signal processing and electronic components. But there are few researches about recognition of heart sound, especially full cardiac cycled heart sound. In this paper, three new recognition methods about full cardiac cycled heart sound were proposed. The first method recognizes the characteristics of heart sound by integrating important peaks and analyzing statistical variables in time domain. The second method builds a database by principal components analysis on training heart sound set in time domain. This database is used to recognize new input of heart sound. The third method builds the same sort of the database not in time domain but in time-frequency domain. We classify the heart sounds into seven classes such as normal (NO) class, pre-systolic murmur (PS) class, early systolic murmur (ES) class, late systolic murmur (LS) class, early diastolic murmur (ED) class, late diastolic murmur (LD) class and continuous murmur (CM) class. As a result, we could verify that the third method is better efficient to recognize the characteristics of heart sound than the others and also than any precedent research. The recognition rates of the third method are 100% for NO, 80% for PS and ES, 67% for LS, 93 for ED, 80% for LD and 30% for CM.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_4_521/_p
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@ARTICLE{e84-d_4_521,
author={Sang Min LEE, In Young KIM, Seung Hong HONG, },
journal={IEICE TRANSACTIONS on Information},
title={Heart Sound Recognition by New Methods Using the Full Cardiac Cycled Sound Data},
year={2001},
volume={E84-D},
number={4},
pages={521-529},
abstract={Recently many researches concerning heart sound analysis are being processed with development of digital signal processing and electronic components. But there are few researches about recognition of heart sound, especially full cardiac cycled heart sound. In this paper, three new recognition methods about full cardiac cycled heart sound were proposed. The first method recognizes the characteristics of heart sound by integrating important peaks and analyzing statistical variables in time domain. The second method builds a database by principal components analysis on training heart sound set in time domain. This database is used to recognize new input of heart sound. The third method builds the same sort of the database not in time domain but in time-frequency domain. We classify the heart sounds into seven classes such as normal (NO) class, pre-systolic murmur (PS) class, early systolic murmur (ES) class, late systolic murmur (LS) class, early diastolic murmur (ED) class, late diastolic murmur (LD) class and continuous murmur (CM) class. As a result, we could verify that the third method is better efficient to recognize the characteristics of heart sound than the others and also than any precedent research. The recognition rates of the third method are 100% for NO, 80% for PS and ES, 67% for LS, 93 for ED, 80% for LD and 30% for CM.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - Heart Sound Recognition by New Methods Using the Full Cardiac Cycled Sound Data
T2 - IEICE TRANSACTIONS on Information
SP - 521
EP - 529
AU - Sang Min LEE
AU - In Young KIM
AU - Seung Hong HONG
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2001
AB - Recently many researches concerning heart sound analysis are being processed with development of digital signal processing and electronic components. But there are few researches about recognition of heart sound, especially full cardiac cycled heart sound. In this paper, three new recognition methods about full cardiac cycled heart sound were proposed. The first method recognizes the characteristics of heart sound by integrating important peaks and analyzing statistical variables in time domain. The second method builds a database by principal components analysis on training heart sound set in time domain. This database is used to recognize new input of heart sound. The third method builds the same sort of the database not in time domain but in time-frequency domain. We classify the heart sounds into seven classes such as normal (NO) class, pre-systolic murmur (PS) class, early systolic murmur (ES) class, late systolic murmur (LS) class, early diastolic murmur (ED) class, late diastolic murmur (LD) class and continuous murmur (CM) class. As a result, we could verify that the third method is better efficient to recognize the characteristics of heart sound than the others and also than any precedent research. The recognition rates of the third method are 100% for NO, 80% for PS and ES, 67% for LS, 93 for ED, 80% for LD and 30% for CM.
ER -