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Doctoral Thesis
DOI
10.11606/T.98.2017.tde-10102016-065309
Document
Author
Full name
Nelson Augusto Oliveira de Aguiar
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2016
Supervisor
Committee
Nicolosi, Denys Emilio Campion (President)
Zanini, Angelo Sebastião
Meneghelo, Romeu Sergio
Ricchetti, Pier Marco
Title in Portuguese
Segmentação automática para classificação digital de sinais de fonocardiograma
Keywords in Portuguese
Bulhas
Fonocardiograma
S1
S2
Separação
Abstract in Portuguese
Com o avanço tecnológico surgem novas ferramentas que auxiliam os médicos no diagnóstico de diversas doenças. Na área cardiovascular, após permanecer por um longo período em segundo plano, a ausculta cardíaca voltou a ser muito utilizada devido ao surgimento, no mercado, de estetoscópios digitais. Tais aparelhos contam com novos recursos tecnológicos que permitem a captação e a análise de dados de forma automática, oferecendo mais informações ao profissional da área. Levando em conta essa nova ascensão da área de Fonocardiografia,o presente trabalho se dedicou à separação das bulhas S1 e S2 por meio de ferramentas computacionais, com o propósito de auxiliar médicos não especialistas em Cardiologia a verificar a existência de possíveis anormalidades no som cardíaco. Acreditando na possibilidade de este procedimento vir a ser utilizado posteriormente para auxiliar no reconhecimento de padrões dos sons cardíacos, este trabalho se propôs a criar um algoritmo para detecção automática de anormalidades que afetam as bulhas S1 e S2. Assim, aplicou-se a técnica de Wavelet sobre uma base de dados de sons cardíacos constituída de 1209 bulhas, auditada pelo Real Hospital Português e também pelo Instituto Dante Pazzanese de Cardiologia. Os melhores resultados obtidos na separação das bulhas foram, nos sons normais, de 96,96% de acurácia para a S1 e de 97,92% para a S2. Já nos sons cardíacos com sopro, obteve-se a acurácia de 87,46% para a separação da S1 e de 89,26% para a S2. Juntos, os resultados dos sons normais e dos sons com sopro totalizaram uma acurácia de 94,02% para a separação da S1 e de 94,54% para a S2.
Title in English
Automatic segmentation for signal classification of digital phonocardiogram
Keywords in English
Heart Sound
Phonocardiogram
S1
S2
Separation
Abstract in English
New technological tools are often created in the medical field to assist doctors in the clinical diagnosis of many diseases. After being forgotten for many years in the cardiovascular area, cardiac auscultation is now back in the spotlight, as soon as digital stethoscope became available in the market. New digital stethoscope records patient's heart sounds, which can be automatically analyzed or also sent to another device for further more detailed investigation. This feature helps physicians in the study of auscultation results. Taking into account the new rise of cardiac auscultation, the present paper attempted to provide the separation of S1 and S2 heart sounds by computer tools, in order to support non-specialist physicians in finding heart sound abnormalities. Heart sound separation can thus be employed for the creation of pattern recognition algorithms, which are able to identify abnormalities automatically. This paper proposed the development of a S1 and S2 heart sound separation algorithm by using Wavelet technique, who was applied upon a database containing 1209 individual heart sounds. The referred database was audited by Royal Portuguese Hospital and Dante Pazzanese Institute of Cardiology medical staff. The best obtained results for S1 and S2 separation in regular heart sounds were a 96.96% accuracy rate for S1 and a 97.92% accuracy rate for S2. In murmur heart sounds were obtained an 87.46% accuracy rate for S1 and an 89.26% accuracy rate for S2. Overall results achieved a 94.02% accuracy rate for S1 and a 94.54% accuracy rate for S2.
 
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Publishing Date
2017-01-04
 
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