No description available.

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. / 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.

There has been in the past and still is controversy over the genesis of the third heart sound (S3). Recent studies, strongly suggest that S3 is a manifestation of a sudden intrinsic limitation in the expansion of the left ventricle. The thesis has aimed to explore that hypothesis further using combined echocardiographic and spectral analysis techniques. Spectral analysis was carried out via conventional fast fourier transform methods and the maximum entropy method. The efficacy of these techniques, in relation to clinical and scientific application, was explored further. Briefly discussed was the application of autoregressive-moving average (ARMA) modelling for spectral analysis of S3, in relation to further work. Following is a brief synopsis of the thesis: CHAPTER 1 This gives an historical and general introduction to heart sound analysis. Discussed briefly is the physiology of the heart and heart sounds and the diagnostic implications of S3 analysis. CHAPTER 2 Here is discussed the instrumentation system used and phonocardiographic and echocardiographic data aquisition. Data preprocessing and storage is also covered. CHAPTER 3 In this chapter the application of a FFT method and correlation of resultant spectral parameters with echocardiographic parameters is reported. CHAPTER 4 The theoretical development of the maximum entropy technique (based on published papers and expanded) is discussed here. Numerical experiments with the method and associated problems are also discussed. CHAPTER 5 The MEM is applied to the spectral analysis of S3 and compared with the FFT method. Correlation analysis of MEM derived spectral parameters with echocardiograhic data is performed. CHAPTER 6 Here ARMA modelling and application to further work is discussed. An ARMA model from the maxixum entropy coefficients is derived. The application of this model to the deconvolution of the chest wall transfer function is discussed as an approach for further work. / Thesis (M.Sc.)--School of Mathematical Sciences, 1989.

<p>Phonocardiographic signals contain bioacoustic information reflecting the operation of the heart. Normally there are two heart sounds, and additional sounds indicate disease. If a third heart sound is present it could be a sign of heart failure whereas a murmur indicates defective valves or an orifice in the septal wall. The primary aim of this thesis is to use signal processing tools to improve the diagnostic value of this information. More specifically, three different methods have been developed:</p><p>• A nonlinear change detection method has been applied to automatically detect heart sounds. The first and the second heart sounds can be found using recurrence times of the first kind while the third heart sound can be found using recurrence times of the second kind. Most third heart sound occurrences were detected (98 %), but the amount of false extra detections was rather high (7 % of the heart cycles).</p><p>• Heart sounds obscure the interpretation of lung sounds. A new method based on nonlinear prediction has been developed to remove this undesired disturbance. High similarity was obtained when comparing actual lung sounds with lung sounds after removal of heart sounds.</p><p>• Analysis methods such as Shannon energy, wavelets and recurrence quantification analysis were used to extract information from the phonocardiographic signal. The most prominent features, determined by a feature selection method, were used to create a new feature set for heart murmur classification. The classification result was 86 % when separating patients with aortic stenosis, mitral insufficiency and physiological murmurs.</p><p>The derived methods give reasonable results, and they all provide a step forward in the quest for an intelligent stethoscope, a universal phonocardiography tool able to enhance auscultation by improving sound quality, emphasizing abnormal events in the heart cycle and distinguishing different heart murmurs.</p>

There has been in the past and still is controversy over the genesis of the third heart sound (S3). Recent studies, strongly suggest that S3 is a manifestation of a sudden intrinsic limitation in the expansion of the left ventricle. The thesis has aimed to explore that hypothesis further using combined echocardiographic and spectral analysis techniques. Spectral analysis was carried out via conventional fast fourier transform methods and the maximum entropy method. The efficacy of these techniques, in relation to clinical and scientific application, was explored further. Briefly discussed was the application of autoregressive-moving average (ARMA) modelling for spectral analysis of S3, in relation to further work. Following is a brief synopsis of the thesis: CHAPTER 1 This gives an historical and general introduction to heart sound analysis. Discussed briefly is the physiology of the heart and heart sounds and the diagnostic implications of S3 analysis. CHAPTER 2 Here is discussed the instrumentation system used and phonocardiographic and echocardiographic data aquisition. Data preprocessing and storage is also covered. CHAPTER 3 In this chapter the application of a FFT method and correlation of resultant spectral parameters with echocardiographic parameters is reported. CHAPTER 4 The theoretical development of the maximum entropy technique (based on published papers and expanded) is discussed here. Numerical experiments with the method and associated problems are also discussed. CHAPTER 5 The MEM is applied to the spectral analysis of S3 and compared with the FFT method. Correlation analysis of MEM derived spectral parameters with echocardiograhic data is performed. CHAPTER 6 Here ARMA modelling and application to further work is discussed. An ARMA model from the maxixum entropy coefficients is derived. The application of this model to the deconvolution of the chest wall transfer function is discussed as an approach for further work. / Thesis (M.Sc.)--School of Mathematical Sciences, 1989.

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. / 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.

Acute congestive heart failure is a leading cause of morbidity and mortality. Patients often undergo repeated hospitalizations with an annual cost in excess of $32B dollars. Early detection of impending acute congestion allows for pharmaceutical interdiction that prevents hospitalization, improves outcomes, and reduces healthcare spending. A subcutaneous implantable monitoring device that detects impending acute congestive heart failure by using heart sounds may provide a valuable tool that can be used to titrate heart failure medications to prevent acute heart failure requiring hospitalization. The device may be used to measure changes in the intervals between the R-wave and S1 and S2 heart sounds, and to detect evolving S3 and S4 heart sounds consistent with volume overload. The amplitudes of S1 and S3 heart sounds change as heart failure patients' symptoms worsen. Designing a non-invasive, external device, capable of recording heart sounds and EKGs in patients undergoing pharmaceutical regression of acute congestive heart failure in a hospital setting may give important insight into the nature of heart sound and EKG changes that occur in patients during progression of acute heart failure while they lead their day-to-day lives. This thesis details the design of a portable, non-invasive device, worn externally on the left-pectoral muscle, capable of continuously recording human EKG signals and heart sounds (through custom MEMS accelerometer technology) over a period of two days. Hardware testing for the scope of this thesis exclusively involved healthy volunteers.

No description available.

Phonocardiographic signals contain bioacoustic information reflecting the operation of the heart. Normally there are two heart sounds, and additional sounds indicate disease. If a third heart sound is present it could be a sign of heart failure whereas a murmur indicates defective valves or an orifice in the septal wall. The primary aim of this thesis is to use signal processing tools to improve the diagnostic value of this information. More specifically, three different methods have been developed: • A nonlinear change detection method has been applied to automatically detect heart sounds. The first and the second heart sounds can be found using recurrence times of the first kind while the third heart sound can be found using recurrence times of the second kind. Most third heart sound occurrences were detected (98 %), but the amount of false extra detections was rather high (7 % of the heart cycles). • Heart sounds obscure the interpretation of lung sounds. A new method based on nonlinear prediction has been developed to remove this undesired disturbance. High similarity was obtained when comparing actual lung sounds with lung sounds after removal of heart sounds. • Analysis methods such as Shannon energy, wavelets and recurrence quantification analysis were used to extract information from the phonocardiographic signal. The most prominent features, determined by a feature selection method, were used to create a new feature set for heart murmur classification. The classification result was 86 % when separating patients with aortic stenosis, mitral insufficiency and physiological murmurs. The derived methods give reasonable results, and they all provide a step forward in the quest for an intelligent stethoscope, a universal phonocardiography tool able to enhance auscultation by improving sound quality, emphasizing abnormal events in the heart cycle and distinguishing different heart murmurs.

No description available.