Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features

This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. Method: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods. Results and conclusion: All statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informativ

Identification Of Asthma Severity Levels Through Wheeze Sound Characterization And Classification Using Integrated Power Features

This study aimed to investigate and classify wheeze sound characteristics according to asthma severity levels (mild, moderate and severe) using integrated power (IP) features. Method: Validated and segmented wheeze sounds were obtained from the lower lung base (LLB) and trachea recordings of 55 asthmatic patients with different severity levels during tidal breathing manoeuvres. From the segments, nine datasets were obtained based on the auscultation location, breath phases and their combination. In this study, IP features were extracted for assessing asthma severity. Subsequently, univariate and multivariate (MANOVA) statistical analyses were separately implemented to analyse behaviour of wheeze sounds according to severity levels. Furthermore, the ensemble (ENS), knearest- neighbour (KNN) and support vector machine (SVM) classifiers were applied to classify the asthma severity levels. Results and conclusion: The univariate results of this study indicated that the majority of features significantly discriminated (p < 0.05) the severity levels in all the datasets. The MANOVA results yielded significantly (p < 0.05) large effect size in all datasets (including LLB-related) and almost all post hoc results were significant(p < 0.05). A comparison ofthe performance of classifiers revealed that eight ofthe nine datasets showed improved performance with the ENS classifier. The Trachea inspiratory (T-Inspir) dataset produced the highest performance. The overall best positive predictive rate (PPR) for the mild, moderate and severe severity levels were 100% (KNN), 92% (SVM) and 94% (ENS) respectively. Analysis related to auscultation locations revealed that tracheal wheeze sounds are more specific and sensitive predictors of asthma severity. Additionally, phase related investigations indicated that expiratory and inspiratory wheeze sounds are equally informative for the classification of asthma severit

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

Computerized respiratory sounds in paediatrics: a systematic review

Background Diagnosing and monitoring of children with respiratory disorders is often challenging. Respiratory sounds (RS) are simple, non-invasive and universally available measures that are directly related to movement of air, within the tracheobronchial tree. Thus, RS may be valuable indicators of respiratory health, their characteristics in the paediatric population are scattered in the literature and not systematized. Aim Systematically review the different acoustic RS properties in healthy children and in children with different respiratory disorders. Methods: MEDLINE, EMBASE, AMED and CINHAL databases were searched on Sept 2020. One author extracted data and two independently assessed the quality of the articles using the National Heart Lung and Blood Institute quality assessment tool. Results Twenty-eight studies were included with a total 2032 participants (44% with a respiratory condition, such as asthma, bronchiolitis, cystic fibrosis, presence of wheezing and non-specified low respiratory tract infections). A high heterogeneity in the procedures, outcomes and outcome measures used was found. Healthy participants showed lower values of F50 (from 194 ± 26 to 521 ± 18Hz) than those with asthma (from 140 ± 8 to 769 ± 85Hz) or bronchiolitis (from 100 to 80Hz). F50 tend to increase with provocation tests (136 ± 9 to 909 ± 81Hz) and decrease with treatments (128 ± 6 to 781 ± 57Hz). Wheeze rates ranged from 0 to 24.7 ± 25% on asthmatic participants. Crackles findings ranged from 6% on people with recurrent wheezing to 30.8% in middle lobe atelectasis. Conclusion RS show different acoustic properties in healthy children vs with different respiratory disorders and thus may be useful in the diagnostic and monitoring on paediatrics.publishe

Does Facemask Impact Diagnostic During Pulmonary Auscultation?

peer reviewedFacemasks have been widely used in hospitals, especially since the emergence of the coronavirus 2019 (COVID-19) pandemic, often severely affecting respiratory functions. Masks protect patients from contagious airborne transmission, and are thus more specifically important for chronic respiratory disease (CRD) patients. However, masks also increase air resistance and thus work of breathing, which may impact pulmonary auscultation and diagnostic acuity, the primary respiratory examination. This study is the first to assess the impact of facemasks on clinical auscultation diagnostic. Lung sounds from 29 patients were digitally recorded using an electronic stethoscope. For each patient, one recording was taken wearing a surgical mask and one without. Recorded signals were segmented in breath cycles using an autocorrelation algorithm. In total, 87 breath cycles were identified from sounds with mask, and 82 without mask. Time-frequency analysis of the signals was used to extract comparison features such as peak frequency, median frequency, band power, or spectral integration. All the features extracted in frequency content, its evolution, or power did not significantly differ between respiratory cycles with or without mask. This early stage study thus suggests minor impact on clinical diagnostic outcomes in pulmonary auscultation. However, further analysis is necessary such as on adventitious sounds characteristics differences with or without mask, to determine if facemask could lead to no discernible diagnostic outcome in clinical practice

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

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

A software toolkit for acoustic respiratory analysis

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 143-147).Millions of Americans suffer from pulmonary diseases. According to recent statistics, approximately 17 million people suffer from asthma, 16.4 million from chronic obstructive pulmonary disease, 12 million from sleep apnea, and 1.3 million from pneumonia - not to mention the prevalence of many other diseases associated with the lungs. Annually, the mortality attributed to pulmonary diseases exceeds 150,000. Clinical signs of most pulmonary diseases include irregular breathing patterns, the presence of abnormal breath sounds such as wheezes and crackles, and the absence of breathing entirely. Throughout the history of medicine, physicians have always listened for such sounds at the chest wall (or over the trachea) during patient examinations to diagnose pulmonary diseases - a procedure also known as auscultation. Recent advancements in computer technology have made it possible to record, store, and digitally process breath sounds for further analysis. Although automated techniques for lung sound analysis have not been widely employed in the medical field, there has been a growing interest among researchers to use technology to understand the subtler characteristics of lung sounds and their potential correlations with physiological conditions. Based on such correlations, algorithms and tools can be developed to serve as diagnostic aids in both the clinical and non-clinical settings.(cont.) We developed a software toolkit, using MATLAB, to objectively characterize lung sounds. The toolkit includes a respiration detector, respiratory rate detector, respiratory phase onset detector, respiratory phase classifier, crackle and wheeze detectors and characterizers, and a time-scale signal expander. This document provides background on lung sounds, describes and evaluates our analysis techniques, and compares our work to approaches in other diagnostic tools.by Gina Ann Yi.M.Eng

Sons respiratórios computorizados em crianças com infeção respiratória do trato inferior : um estudo comparativo

Mestrado em FisioterapiaBackground: Lower respiratory tract infections (LRTI) are the main cause of health burden in the first years of age. To enhance the diagnosis and monitoring of infants with LRTI, researchers have been trying to use the large advantages of conventional auscultation. Computerised respiratory sound analysis (CORSA) is a simple method to detect and characterise Normal Respiratory Sounds (NRS) and Adventitious Respiratory Sounds (ARS). However, if this measure is to be used in the paediatric population, reference values have to be established first. Aim: To compare and characterise NRS and ARS in healthy infants and infants with LRTI. Methods: A cross-sectional descriptive-comparative study was conducted in three institutions. Infants were diagnosed by the paediatrician as presenting or not presenting an LRTI, healthy volunteers were recruited from the institutions. Socio-demographic, anthropometric and cardio-respiratory parameters were collected. Respiratory sounds were recorded with a digital stethoscope. Frequency at maximum intensity (Fmax), maximum intensity (Imax) and mean intensity (Imean) over the whole frequency range were collected to characterise NRS. Location, mean number, type, duration and frequency were collected to characterise ARS. All analysis was performed per breathing phase (i.e., inspiration and expiration). Results: Forty nine infants enrolled in this study: 25 healthy infants (G1) and 24 infants with LRTI. Inspiratory Fmax (G1: M 116.1 Hz IQR [107.2-132.4] vs G2: M 118.9Hz IQR [113.2-128.7], p=0.244) and expiratory frequencies (G1: M 107.3Hz IQR [102.9-116.9] vs G2: M 112.6Hz IQR [106.6-122.6], p= 0.083) slightly higher than their healthy peers. Wheeze occupation rate was statistically significantly different between groups in inspiration (G1: M 0 IQR [0-0.1] vs G2: M 0.2 IQR [0-5.2] p= 0.032) and expiration (G1: M 0 IQR [0-1.9] vs G2: M 1.5 IQR [0.2-6.7] p= 0.015), being the infants with LRTI the ones presenting more wheezes. Conclusion: Computerised respiratory sounds in healthy infants and infants with LRTI presented differences. The main findings indicated that NRS have Fmax higher in infants with LRTI than in healthy infant and Wh% was the characteristic that differ the most between infant with LRTI and healthy infant.Enquadramento: As infeções respiratórias do trato inferior (IRTI) constituem o principal problema de saúde nos primeiros anos de vida das crianças. Desta forma, a investigação tem-se focado no desenvolvimento de medidas objetivas para o diagnóstico de IRTI, utilizando essencialmente as vantagens da auscultação convencional incorporadas numa análise computorizada e automática. Contudo, apesar da análise computorizada de sons respiratórios ser um método simples de deteção e caraterização dos sons respiratórios normais (SRN) e adventícios (SRA), desconhecem-se quais os valores de referência dos sons respiratórios em crianças, o que limita a sua aplicação na prática clínica Objetivos: Caraterizar e comparar os SRN e os SRA em crianças saudáveis e com IRTI. Métodos: Estudo descritivo, comparativo e transversal realizado em três instituições. Eram elegíveis crianças diagnosticadas pelo pediatra com IRTI e voluntários para crianças saudáveis. Foram recolhidos dados sócio demográficos, antropométricos e parâmetros cardiorrespiratórios. Os sons respiratórios foram registados com um estetoscópio digital. Foram analisados diversos parâmetros para os SRN: a frequência na intensidade máxima (Fmax), a intensidade máxima (Imax) e a média da intensidade ao longo de toda a faixa de frequência (Imean). Nos SRA foram analisados: a taxa de ocupação por wheezes (Wh%), a média wheezes (Wh), o número e o tipo Wh, a frequência e a localização Wh por região; o número crackles (Cr), o tipo e a frequência Cr, a duração da deflexão inicial, da maior deflexão e dos dois ciclos de deflexão dos Cr. Todos estes dados foram analisados por fase do ciclo respiratório (i.e., inspiração e expiração). Resultados: Quarenta e nove crianças foram incluídas neste estudo: 25 saudáveis (G1) e 24 com IRTI (G2). A Fmax inspiratória (G1: M 116,1 Hz IQR [107,2-132,4] vs G2: M 118.9Hz IQR [113,2-128,7], p = 0,244) e expiratória (G1: M 107.3Hz IQR [102,9-116,9] vs G2: M 112.6Hz IQR [106,6-122,6], p = 0,083) foi superior nas crianças com IRTI relativamente às crianças saudáveis. A Wh% foi significativamente superior nas crianças com IRTI, relativamente às crianças saudáveis na inspiração (G1: M 0 IQR [0-0,1] vs G2: M 0,2 IQR [0-5,2] p = 0,032) e na expiração (G1: M 0 IQR [0-1,9] vs G2: M 1,5 IQR [0,2-6,7] p = 0,015). Conclusão: Os sons respiratórios computorizados de crianças saudáveis e com IRTI apresentam diferenças. Os principais resultados indicam que os sons respiratórios normais apresentam uma Fmax maior em crianças com IRTI do que em saudáveis e que Wh% é a característica que mais difere entre os dois grupos

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