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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Recentemente muitas pesquisas relativas à análise dos sons cardíacos estão sendo processadas com o desenvolvimento de processamento digital de sinais e componentes eletrônicos. Mas há poucas pesquisas sobre o reconhecimento dos sons cardíacos, especialmente os sons cardíacos com ciclo cardíaco completo. Neste artigo, foram propostos três novos métodos de reconhecimento de sons cardíacos com ciclo cardíaco completo. O primeiro método reconhece as características do som cardíaco integrando picos importantes e analisando variáveis estatísticas no domínio do tempo. O segundo método constrói um banco de dados por meio da análise de componentes principais sobre sons cardíacos de treinamento definidos no domínio do tempo. Este banco de dados é usado para reconhecer novas entradas de sons cardíacos. O terceiro método constrói o mesmo tipo de banco de dados, não no domínio do tempo, mas no domínio da frequência do tempo. Classificamos os sons cardíacos em sete classes, como classe normal (NO), classe de sopro pré-sistólico (PS), classe de sopro sistólico precoce (ES), classe de sopro sistólico tardio (LS), classe de sopro diastólico precoce (DE), classe de sopro diastólico precoce (ED), classe de sopro sistólico tardio (LS). classe de sopro diastólico (LD) e classe de sopro contínuo (CM). Como resultado, pudemos verificar que o terceiro método é mais eficiente para reconhecer as características dos sons cardíacos do que os demais e também do que qualquer pesquisa anterior. As taxas de reconhecimento do terceiro método são de 100% para NO, 80% para PS e ES, 67% para LS, 93 para ED, 80% para LD e 30% para 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 -