Analysis of Respiratory Sounds: State of the Art

Objective This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds. Methods and material Review of the current medical and technological literature using Pubmed and personal experience. Results The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms… Conclusion The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools

Measurement and analysis of breath sounds

Existing breath sound measurement systems and possible new methods have been critically investigated. The frequency response of each part of the measurement system has been studied. Emphasis has been placed on frequency response of acoustic sensors; especially, a method to study a diaphragm type air-coupler in contact use has been proposed. Two new methods of breath sounds measurement have been studied: laser Doppler vibrometer and mobile phones. It has been shown that these two methods can find applications in breath sounds measurement, however there are some restrictions. A reliable automatic wheeze detection algorithm based on auditory modelling has been developed. That is the human’s auditory system is modelled as a bank of band pass filters, in which the bandwidths are frequency dependent. Wheezes are treated as signals additive to normal breath sounds (masker). Thus wheeze is detectable when it is above the masking threshold. This new algorithm has been validated using simulated and real data. It is superior to previous algorithms, being more reliable to detect wheezes and less prone to mistakes. Simulation of cardiorespiratory sounds and wheeze audibility tests have been developed. Simulated breath sounds can be used as a training tool, as well as an evaluation method. These simulations have shown that, under certain circumstance, there are wheezes but they are inaudible. It is postulated that this could also happen in real measurements. It has been shown that simulated sounds with predefined characteristics can be used as an objective method to evaluate automatic algorithms. Finally, the efficiency and necessity of heart sounds reduction procedures has been investigated. Based on wavelet decomposition and selective synthesis, heart sounds can be reduced with a cost of unnatural breath sounds. Heart sound reduction is shown not to be necessary if a time-frequency representation is used, as heart sounds have a fixed pattern in the time-frequency plane

Mapa de sonidos respiratorios adventicios discontinuos

La metodología propuesta para la detección de las crepitancias incluyó 3 esquemas de procesamiento con base en conceptos de técnicas no lineales como el cálculo de la Dimensión Fractal (DF), conceptos de técnicas lineales como la parametrización de la información acústica multicanal con un modelo AR invariante en el tiempo y su clasificación por redes neuronales artificiales (AR-RNA) y el concepto de modelo AR variante en el tiempo (ARVT). Se han realizado varios esfuerzos para detectar estertores crepitantes, sin embargo, los resultados basan sus medidas de desempeño en información obtenida por un experto médico mediante el procedimiento TEWA. En esta tesis, en una primera fase, se prescindió de la detección por un experto y se generó una base de datos con crepitancias simuladas donde se tiene el control sobre el número, tipo, SNR y distribución espacial de las mismas al ser insertadas en información acústica multicanal proveniente de sujetos sanos. Los resultados demuestran que en casos simulados y con datos adquiridos de sujetos enfermos, la metodología basada en la técnica de ARVT proporciona los mejores resultados en términos del número de crepitancias y su distribución espacial. En consecuencia, las imágenes de sonidos adventicios generadas por el esquema ARVT fueron las más cercanas a las imágenes patrón en diferentes condiciones de simulación. Utilizando casos simulados se logró demostrar el desempeño y las limitaciones que tiene un experto médico en la detección de crepitancias comparado con e l método de ARVT. Además, el resultado generado con el esquema ARVT, con información acústica proveniente de un sujeto con Neumonía Intersticial Difusa, correlaciona con el número de estertores que el médico especialista, aplicando TEW A y su experiencia clínica, es capaz de detectar. Los resultados de la tesis se presentaron en el congreso internacional de la IEEE-EMBS que se llevó a cabo en la ciudad de Vancouver en el 2008 mediante dos participaciones, una presentación oral y otra en sesión de posters. Se anexan copias de ambos trabajos al final de la presente tesis.El análisis de los sonidos respiratorios (SR) normales y anormales representa una alternativa en el apoyo al diagnóstico de diversas enfermedades pulmonares. Esta tesis de maestría se enfoca en la identificación automatizada en registros puntuales de los sonidos adventicios discontinuos (estertores crepitantes), con el objetivo de generar una imagen que refleje la localización y distribución espacial de los sonidos crepitantes. La propuesta de la lmagenología de Sonidos Discontinuos es novedosa en el campo de los SR y la información espacio-temporal presente en la imagen generada tiene la ventaja que se encuentra asociada a la función pulmonar. El procedimiento de análisis de sonidos discontinuos conocido como Time Expanded Waveform Analysis (TEW A) es ampliamente utilizado para detectar visualmente las crepitancias y extraer parámetros en el dominio del tiempo que definen su morfología. Sin embargo, la técnica requiere de criterios que a la fecha son poco precisos para identificar a las crepitancias por ejemplo, que la amplitud de las crepitancias sea del doble de la amplitud del sonido respiratorio de base. La técnica presenta limitaciones, ya que en situaciones reales las crepitancias pueden estar traslapadas temporalmente, tener una SNR baja y poseer una forma de onda alterada debido al sonido respiratorio pulmonar de base. Por lo tanto TEW A, desde nuestra experiencia, no es adecuada para producir la imagen de sonidos adventicios, esto es entonces motivo de la presente investigación

Identificação de estertores em sons respiratórios utilizando transformada wavelet e análise de discriminante linear

Crackles are adventitious and discontinuous breath sounds that occur in lung diseases. Time domain parameters classify the crackles as fine, medium, and coarse, and may have positive or negative polarity. This work investigates methods and tools to characterize and classify crackles. Samples of breath sounds containing crackles were normalized and resampled at 8 kHz. Several experiments using the discrete wavelet transform (DWT), linear discriminant analysis (LDA), and k-NN have been performed, and evaluated with ROC analysis. A pattern recognition system was implemented with DWT, LDA and k-NN to classify fine and coarse crackles, and normal breath sounds. The experiment with different signal border extension methods during DWT decomposition showed the influence on the results of the characterization. The results indicate that the methods ZPD, SP0, SYMH, SYMW, ASYMH, PPD and PER are recommended, while SP1 and ASYMW methods are not recommended for the decomposition and characterization of crackles because they generate different characteristics in the higher subbands. Another experiment showed that the characterization of crackles using DWT can be made using certain decomposition subbands (D3, D4, and D5 with signal sampled at 8 kHz), thus reducing the computational effort. Another classification system implemented using LDA and DWT showed that crackles can be classified by their polarity indicating a high degree of accuracy (AUC rate up to 0.9943 for Symlet 19). Two experiments were conducted for mother-wavelet selection that best characterizes crackles. The first one quantitatively evaluated the similarity between the crackle and several mother-wavelets using Pearson's correlation coefficient. The mother-wavelet that resulted a strong correlation with the crackles, being most indicated for use were: Reverse Biorthogonal 3.7, 5.5 Biorthogonal Reverse, Reverse Biorthogonal 3.5, Daubechies 5, Symlet 5, Daubechies 6, 7, and Symlet Daubechies 7. The second experiment selected mother-wavelets by the power concentration in subbands. Previous trials already shown that the energy of the crackles decomposed by DWT are concentrated in a few subbands, so mothers-wavelet that concentrate larger percentage of the energy in a specific subband were selected, which were Daubechies 7, Symlet 7, Coiflet 3 and Symlet 12. The final experiment performed was a combination of mother-wavelets to improve the separability of crackles and normal breath sounds. The experiment showed that a classification system using DWT, LDA, and a linear classifier may totally separate the two classes (AUC ratio = 1) when the combination of mother-wavelets to generate the feature vector of the signals is used.CAPESEstertores são sons respiratórios adventícios e descontínuos que ocorrem em patologias pulmonares. Parâmetros no domínio do tempo classificam os estertores como finos, médios e grossos, e podem ter polaridade positiva ou negativa. Este trabalho investiga métodos e ferramentas para caracterizar e classificar estertores. Amostras de sons respiratórios contendo estertores foram normalizadas e reamostradas em 8 kHz. Foram realizados diversos ensaios utilizando a transformada wavelet discreta (DWT) e a análise de discriminante linear (LDA), e avaliados com análise ROC. Um sistema de reconhecimento de padrões foi implementado com DWT, LDA e k-NN para classificar estertores finos, grossos e sons respiratórios normais. O ensaio com diferentes métodos de extensão de borda do sinal durante a decomposição DWT mostrou a influência nos resultados da caracterização. Os resultados indicam que os métodos ZPD, SP0, SYMH, SYMW, ASYMH, PPD e PER são recomendados, enquanto que os métodos SP1 e ASYMW não são recomendados para a decomposição e caracterização de estertores, pois geram características diferentes nas sub-bandas mais altas. Outro ensaio mostrou que a caracterização dos estertores utilizando DWT pode ser feita utilizando-se algumas sub-bandas de decomposição (D3, D4 e D5, no caso de sinais amostrados em 8 kHz), reduzindo-se desta forma o esforço computacional. Outro sistema de classificação implementado utilizando DWT e LDA mostrou que os estertores podem ser classificados indicando a polaridade com elevado grau de acerto (AUC de até 0,9943 para Symlet 19). Dois ensaios foram realizados para seleção da wavelet-mãe que melhor caracterize estertores. O primeiro ensaio avaliou quantitativamente a semelhança entre o estertor e diversas wavelets-mães através do índice de correlação de Pearson. As wavelets-mães que resultaram uma forte correlação com o estertores, se mostrando mais indicadas para serem utilizadas, foram: Reverse Biorthogonal 3.7, Reverse Biorthogonal 5.5, Reverse Biorthogonal 3.5, Daubechies 5, Symlet 5, Daubechies 6, Symlet 7 e Daubechies 7. O segundo ensaio selecionou a wavelet-mãe pela concentração de energia nas sub-bandas. Ensaios anteriores já mostravam que a energia dos estertores decompostos pela DWT se concentra em poucas sub-bandas, então foram selecionadas wavelets-mães que concentrassem maior porcentagem da energia em uma sub-banda específica, que foram: Daubechies 7, Symlet 7, Coiflet 3 e Symlet 12. O último ensaio realizado foi uma combinação de wavelets-mães para melhorar a separabilidade de estertores e sons respiratórios normais. O ensaio mostrou que um sistema de classificação utilizando DWT, LDA e um classificador linear pode separar totalmente as duas classes (índice AUC = 1) quando é utilizada a combinação de wavelets-mães para gerar o vetor de características dos sinais

Detecção automática de crepitações em sons respiratórios

Mestrado em Engenharia Electrónica e TelecomunicaçõesEsta dissertação explora métodos automáticos de detecção de crepitações. É descrita e discutida a implementação em MatLab de quatro algoritmos propostos na literatura. O primeiro é o de Vannuccini et al. [1], que envolve a aplicação de filtros Savitzky Golay – SG. O segundo é o de Hadjileontiadis e Rekanos [2], baseado no cálculo da Fractal Dimension – FD. O terceiro é uma versão alternativa do anterior substituindo o algoritmo de Katz pelo de Sevcik [3] para o cálculo da FD. O último é o proposto por Bahoura e Lu, baseado em Wavelet packet-based stationary and non stationary filters - WPST_NST [4] (com algumas adaptações). A avaliação de desempenho foi realizada sobre um repositório composto por 10 sons respiratórios (5 de pacientes com fibrose quística e 5 de pacientes com pneumonia) devidamente anotados por 3 profissionais de saúde. Por acordo entre as suas anotações, geraram-se referências para o cálculo dos valores de sensibilidade (SE), de precisão (PPV) e da média harmónica destes para os vários algoritmos. Para efeitos de teste, definiram-se empiricamente gamas úteis de detecção para cada algoritmo e grelhas de 10 limiares uniformemente espaçados nessas gamas úteis. Nos limiares de máximo desempenho (considerando resultados médios no repositório) obtiveram-se SE=87,5% e PPV=71,6% (F=77,4%) para o primeiro algoritmo, SE=91,4% e PPV=74,5% (F=81%) para o segundo, SE=91,5% e PPV=72,14% (F=79,4%) para o terceiro e SE=89,6% e PPV=71,9% (F=78,7%) para o último. Por combinação de filtros SG e do algoritmo FD de Sevcik estabeleceu-se um novo algoritmo híbrido (SG-FD) cujo máximo desempenho (considerando resultados médios no repositório) foi SE=84,75% e PPV=62,57% (F=65%). Finalmente, explorou-se a detecção por acordo entre algoritmos. Obtiveram-se anotações de acordo para cada um dos 10 ficheiros do repositório considerando todas as 10000 combinações de limiares possíveis. No ponto de desempenho óptimo (considerando resultados médios no repositório) obtiveram-se SE=91,4% e PPV=83,7%. A média harmónica (F) atinge 86,7%, superando em 7% o máximo obtido com algoritmos individuais (F=81% para o algoritmo de Hadjileontiadis e Rekanos).This dissertation explores automatic methods for crackle detection. It describes and discusses the MatLab implementation of four algorithms proposed in the literature. The first, by Vannuccini et al. [1], involves the application of Savitzky Golay (SG) filters. The second, by Hadjileontiadis and Rekanos [2], is based on the Fractal Dimension (FD) function. The third is an alternative version of the previous one, replacing the Katz algorithm by the Sevcik [3] algorithm for calculating the waveformʼs FD. The last one proposed, by Bahoura and Lu, is based on Wavelet packet-based stationary and non stationary filters – WPSTNST [4] (implemented with a few adaptations). Performance evaluation was based on a repository of 10 respiratory sounds (5 from patients with cystic fibrosis and 5 from patients with pneumonia) duly annotated by three health professionals. By agreement among their annotations, references were generated to calculate the values of sensitivity (SE), accuracy (PPV) and their harmonic mean (F) of the various algorithms. The useful threshold ranges of the four algorithms were empirically established and a set of 10 evenly spaced thresholds were defined for each. The top performance thresholds (considering average results in the repository) yielded SE=87.5% and PPV=71.6% (F=77.4%) for the first algorithm, SE=91.4% and PPV=74.5% (F=81%) for the second, SE=91.5% and PPV=72.14% (F=79.4%) for the third and SE=89.6% and PPV=71.9% (F=78.7%) for the fourth. By combining SG filters and Sevcik´s FD algorithm, a new hybrid algorithm (SG-FD) was established, whose maximum performance (considering average results in the repository) was SE=84,75% and PPV=62,57% (F=65%). Finally, detection by agreement between algorithms was explored. Agreement annotations were obtained for each of the 10 files in the repository considering every one of the 10,000 possible combination thresholds. At the point of optimal performance (average results in the repository) SE=91,4% and PPV=83,7% were obtained. The harmonic mean (F) reached 86.7%, surpassing by 7% the top performance achieved with individual algorithms (F=81% for the Hadjileontiadis and Rekanos algorithm)

Multichannel analysis of normal and continuous adventitious respiratory sounds for the assessment of pulmonary function in respiratory diseases

Premi extraordinari doctorat UPC curs 2015-2016, àmbit d’Enginyeria IndustrialRespiratory sounds (RS) are produced by turbulent airflows through the airways and are inhomogeneously transmitted through different media to the chest surface, where they can be recorded in a non-invasive way. Due to their mechanical nature and airflow dependence, RS are affected by respiratory diseases that alter the mechanical properties of the respiratory system. Therefore, RS provide useful clinical information about the respiratory system structure and functioning. Recent advances in sensors and signal processing techniques have made RS analysis a more objective and sensitive tool for measuring pulmonary function. However, RS analysis is still rarely used in clinical practice. Lack of a standard methodology for recording and processing RS has led to several different approaches to RS analysis, with some methodological issues that could limit the potential of RS analysis in clinical practice (i.e., measurements with a low number of sensors, no controlled airflows, constant airflows, or forced expiratory manoeuvres, the lack of a co-analysis of different types of RS, or the use of inaccurate techniques for processing RS signals). In this thesis, we propose a novel integrated approach to RS analysis that includes a multichannel recording of RS using a maximum of five microphones placed over the trachea and the chest surface, which allows RS to be analysed at the most commonly reported lung regions, without requiring a large number of sensors. Our approach also includes a progressive respiratory manoeuvres with variable airflow, which allows RS to be analysed depending on airflow. Dual RS analyses of both normal RS and continuous adventitious sounds (CAS) are also proposed. Normal RS are analysed through the RS intensity–airflow curves, whereas CAS are analysed through a customised Hilbert spectrum (HS), adapted to RS signal characteristics. The proposed HS represents a step forward in the analysis of CAS. Using HS allows CAS to be fully characterised with regard to duration, mean frequency, and intensity. Further, the high temporal and frequency resolutions, and the high concentrations of energy of this improved version of HS, allow CAS to be more accurately characterised with our HS than by using spectrogram, which has been the most widely used technique for CAS analysis. Our approach to RS analysis was put into clinical practice by launching two studies in the Pulmonary Function Testing Laboratory of the Germans Trias i Pujol University Hospital for assessing pulmonary function in patients with unilateral phrenic paralysis (UPP), and bronchodilator response (BDR) in patients with asthma. RS and airflow signals were recorded in 10 patients with UPP, 50 patients with asthma, and 20 healthy participants. The analysis of RS intensity–airflow curves proved to be a successful method to detect UPP, since we found significant differences between these curves at the posterior base of the lungs in all patients whereas no differences were found in the healthy participants. To the best of our knowledge, this is the first study that uses a quantitative analysis of RS for assessing UPP. Regarding asthma, we found appreciable changes in the RS intensity–airflow curves and CAS features after bronchodilation in patients with negative BDR in spirometry. Therefore, we suggest that the combined analysis of RS intensity–airflow curves and CAS features—including number, duration, mean frequency, and intensity—seems to be a promising technique for assessing BDR and improving the stratification of BDR levels, particularly among patients with negative BDR in spirometry. The novel approach to RS analysis developed in this thesis provides a sensitive tool to obtain objective and complementary information about pulmonary function in a simple and non-invasive way. Together with spirometry, this approach to RS analysis could have a direct clinical application for improving the assessment of pulmonary function in patients with respiratory diseases.Los sonidos respiratorios (SR) se generan con el paso del flujo de aire a través de las vías respiratorias y se transmiten de forma no homogénea hasta la superficie torácica. Dada su naturaleza mecánica, los SR se ven afectados en gran medida por enfermedades que alteran las propiedades mecánicas del sistema respiratorio. Por lo tanto, los SR proporcionan información clínica relevante sobre la estructura y el funcionamiento del sistema respiratorio. La falta de una metodología estándar para el registro y procesado de los SR ha dado lugar a la aparición de diferentes estrategias de análisis de SR con ciertas limitaciones metodológicas que podrían haber restringido el potencial y el uso de esta técnica en la práctica clínica (medidas con pocos sensores, flujos no controlados o constantes y/o maniobras forzadas, análisis no combinado de distintos tipos de SR o uso de técnicas poco precisas para el procesado de los SR). En esta tesis proponemos un método innovador e integrado de análisis de SR que incluye el registro multicanal de SR mediante un máximo de cinco micrófonos colocados sobre la tráquea yla superficie torácica, los cuales permiten analizar los SR en las principales regiones pulmonares sin utilizar un número elevado de sensores . Nuestro método también incluye una maniobra respiratoria progresiva con flujo variable que permite analizar los SR en función del flujo respiratorio. También proponemos el análisis combinado de los SR normales y los sonidos adventicios continuos (SAC), mediante las curvas intensidad-flujo y un espectro de Hilbert (EH) adaptado a las características de los SR, respectivamente. El EH propuesto representa un avance importante en el análisis de los SAC, pues permite su completa caracterización en términos de duración, frecuencia media e intensidad. Además, la alta resolución temporal y frecuencial y la alta concentración de energía de esta versión mejorada del EH permiten caracterizar los SAC de forma más precisa que utilizando el espectrograma, el cual ha sido la técnica más utilizada para el análisis de SAC en estudios previos. Nuestro método de análisis de SR se trasladó a la práctica clínica a través de dos estudios que se iniciaron en el laboratorio de pruebas funcionales del hospital Germans Trias i Pujol, para la evaluación de la función pulmonar en pacientes con parálisis frénica unilateral (PFU) y la respuesta broncodilatadora (RBD) en pacientes con asma. Las señales de SR y flujo respiratorio se registraron en 10 pacientes con PFU, 50 pacientes con asma y 20 controles sanos. El análisis de las curvas intensidad-flujo resultó ser un método apropiado para detectar la PFU , pues encontramos diferencias significativas entre las curvas intensidad-flujo de las bases posteriores de los pulmones en todos los pacientes , mientras que en los controles sanos no encontramos diferencias significativas. Hasta donde sabemos, este es el primer estudio que utiliza el análisis cuantitativo de los SR para evaluar la PFU. En cuanto al asma, encontramos cambios relevantes en las curvas intensidad-flujo yen las características de los SAC tras la broncodilatación en pacientes con RBD negativa en la espirometría. Por lo tanto, sugerimos que el análisis combinado de las curvas intensidad-flujo y las características de los SAC, incluyendo número, duración, frecuencia media e intensidad, es una técnica prometedora para la evaluación de la RBD y la mejora en la estratificación de los distintos niveles de RBD, especialmente en pacientes con RBD negativa en la espirometría. El método innovador de análisis de SR que se propone en esta tesis proporciona una nueva herramienta con una alta sensibilidad para obtener información objetiva y complementaria sobre la función pulmonar de una forma sencilla y no invasiva. Junto con la espirometría, este método puede tener una aplicación clínica directa en la mejora de la evaluación de la función pulmonar en pacientes con enfermedades respiratoriasAward-winningPostprint (published version

Propostas de técnicas para caracterização e classificação automática de sons pulmonares adventícios

In this thesis, the investigation of methods to characterize and classify adventitious lung sounds by spectral analysis is described. To accomplish this task, two novel techniques were developed, through Multiressolution Analysis, based on the Discrte Wavelet Transform. The first technique aims to detect abnormal sounds and classity them info four groups: normal, continuous and discontinuous adventitions lung sounds, also notifying their simultaneous occurence. During its processing, the respiratory cycle signal is decomposed up to its tenth level, and the energy present in the detail and approximation coefficients for each decomposition level is calculated, resulting on a curve of energy versus decomposition level. The resulting curves show different signatures for each kind of adventitious sound. These signatures are used as data source for a classifier system based on Radial Basis Function Artificial Neural Networks. This technique was tested for ten different wavelets, training a hundred neural networks for each wavelet, totalizing a thousand neural networks trained. The best performance rates for each wavelet reach values from 88% to 92.36% for the test group, in a set of 275 respiratory cycles. In the second technique, named Filtering by Selective Spectral Analysis, the lung sound is decomposed until its fourth level, the approximation coefficients spectra are calculatedand, based on the highest frequency component found on those coefficients, a multiband FIR filter is determined. This filter is used to eliminate all frequency components in the approximation coefficients except the highest one. After the filtering procedure, the signal is recomposed by wavelet reconstruction. In order to evaluate the proposed technique, ten wavelets were used in the decomposition and reconstruction stages. The wavelet which presented the best performance attenuated heart sounds 6 dB more than the adventitious sounds that occur in the same spectral band. For measuring this attenuation, the Power Spectral Density was used. This procedure showed satisfactory results, elimination the normal airflow noise and cardiac sounds, leaving only the adventitious sounds in the recorded lung sounds.Nesta tese, descrevem-se técnicas matemáticas visando a caracterização e classificação de sons pulmonares adventícios, por meio de sua análise espectral. Para alcançar este objetivo, desenvolveu-se duas novas metodologias, que utilizam Análise em Multiresolução, implementada a partir da Transformada Wavelet Discreta. A primeira metodologia desenvolvida é utilizada para classificar automaticamente os sons pulmonares em quatro grupos: sons normais e sons adventícios contínuos e descontínuos, notificando também o caso de ocorrência das duas anomalias no mesmo ciclo respiratório. Durante o processamento, o ciclo respiratório é decomposto até seu décimo nível, calculando a energia dos coeficientes detalhe em cada nível de decomposição, assim como a energia dos coeficientes de aproximação. Deste cálculo, obtém-se uma curva de variação da energia em relação ao nível de decomposição, sendo que as curvas obtidas se mostraram curvas caracterísitcas em relação ao tipo de som adventício. Tais curvas são aplicadas a uma simulação de Rede Neural Artificial de Função de Base Radial, que atua como classificador entre os quatro grupos. Esta técnica foi testada utilizando dez wavelets, sendo treinadas cem redes neurais para cada uma. Os melhores resultados apresentaram índice de acerto entre 88% e 92,36% para o conjunto de teste, em um total de 275 ciclos respiratórios. A segunda metodologia, denominada Filtragem por Análise Espectral Seletiva, decompõe o som pulmonar até seu quarto nível, calculando o espectro dos coeficientes aproximação e, baseado na componente de frequência prepoderante, calcula um filtro FIR multibanda. Este filtro é utilizado para eliminar todas as {sic} componentes espectrais dos coeficientes de aproximação, com exceção do mais proeminente. Após o procedimento de filtragem, o sinal é recomposto através de reconstrução wavelet. Para a avaliação de seus resultados, foram testadas dez wavelets no processo de decomposição e reconstrução. Para a wavelet que apresentou melhores resultados, obteve-se uma atenuação dos sons cardíacos da ordem de 6dB em relação aos sons adventícios que ocorrem na mesma faixa espectral, utilizando a Densidade Espectral de Potência dos sinais como referência. Esta metodologia mostrou resultados satisfatórios na tarefa de eliminar tanto os ruídos relativos ao fluxo aéreo normal nas vias aeríferas quanto os sons cardíacos, mantendo somente os sons adventícios nas gravações de sons pulmonares

Reconnaissance automatique des crépitants et des sibilants dans les sons acoustiques respiratoires

Les crépitants sont des sons respiratoires explosifs de courte durée, associés le plus souvent à un dysfonctionnement pulmonaire. Il s'agit de sons transitoires surajoutés aux bruits respiratoires normaux. Dans ce projet de recherche, nous avons développé un système d'analyse des crépitants dans le but de réaliser un outil d'aide au diagnostic. Ce système opère en deux étapes: dans la première, il sépare les crépitants des sons vésiculaires à l'aide d'un filtre adéquat; tandis que dans la seconde il détecte et classifie les crépitants en deux classes : les crépitants fins et les gros crépitants. La séparation des crépitants est une étape importante qui permet d 'extraire ceuxci, qui sont de nature non-stationnaire, des sons vésiculaires et cela afin d 'appliquer les méthodes de détection et de classification propres à ces types de signaux. Dans ce mémoire, nous proposons un nouveau filtre , basé sur les paquets d'ondelettes (WPST - NST), pour séparer les bruits non-stationnaires des bruits stationnaires. Le filtre proposé offre une meilleure performance comparativement aux méthodes existantes. Dans l'étape de la détection et de la classification des crépitants, nous avons expérimenté et comparé plusieurs paramètres afin de trouver le plus performant. Le paramètre dimension fractale (FD) est de premier choix pour la détection, tandis que la durée de la déflexion maximale (MDW), la fréquence du pic (PF) et la largeur de la bande gaussiènne (GBW) sont utilisées avec un modèle de mélange de gaussiènnes (GMM) pour la classification. Enfin, nous nous sommes intéressés à l'extraction des sibilants qui appartiennent à la classe des sons adventices continus. Nous avons testé quatre méthodes différentes afin d'évaluer et comparer leurs performances

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

Algoritmos de procesado de señal basados en Non-negative Matrix Factorization aplicados a la separación, detección y clasificación de sibilancias en señales de audio respiratorias monocanal

La auscultación es el primer examen clínico que un médico lleva a cabo para evaluar el estado del sistema respiratorio, debido a que es un método no invasivo, de bajo coste, fácil de realizar y seguro para el paciente. Sin embargo, el diagnóstico que se deriva de la auscultación sigue siendo un diagnóstico subjetivo que se encuentra condicionado a la habilidad, experiencia y entrenamiento de cada médico en la escucha e interpretación de las señales de audio respiratorias. En consecuencia, se producen un alto porcentaje de diagnósticos erróneos que ponen en riesgo la salud de los pacientes e incrementan el coste asociado a los centros de salud. Esta Tesis propone nuevos métodos basados en Non-negative Matrix Factorization aplicados a la separación, detección y clasificación de sonidos sibilantes para proporcionar una vía de información complementaria al médico que ayude a mejorar la fiabilidad del diagnóstico emitido por el especialista. Auscultation is the first clinical examination that a physician performs to evaluate the condition of the respiratory system, because it is a non-invasive, low-cost, easy-to-perform and safe method for the patient. However, the diagnosis derived from auscultation remains a subjective diagnosis that is conditioned by the ability, experience and training of each physician in the listening and interpretation of respiratory audio signals. As a result, a high percentage of misdiagnoses are produced that endanger the health of patients and increase the cost associated with health centres. This Thesis proposes new methods based on Non-negative Matrix Factorization applied to separation, detection and classification of wheezing sounds in order to provide a complementary information pathway to the physician that helps to improve the reliability of the diagnosis made by the doctor.Tesis Univ. Jaén. Departamento INGENIERÍA DE TELECOMUNICACIÓ