Influence of event duration on automatic wheeze classification

Patients with respiratory conditions typically exhibit adventitious respiratory sounds, such as wheezes. Wheeze events have variable duration. In this work we studied the influence of event duration on wheeze classification, namely how the creation of the non-wheeze class affected the classifiers' performance. First, we evaluated several classifiers on an open access respiratory sound database, with the best one reaching sensitivity and specificity values of 98% and 95%, respectively. Then, by changing one parameter in the design of the non-wheeze class, i.e., event duration, the best classifier only reached sensitivity and specificity values of 55% and 76%, respectively. These results demonstrate the importance of experimental design on the assessment of wheeze classification algorithms' performance.publishe

Automatic classification of adventitious respiratory sounds: a (un)solved problem?

(1) Background: Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS), such as wheezes and crackles. ARS events have variable duration. In this work we studied the influence of event duration on automatic ARS classification, namely, how the creation of the Other class (negative class) affected the classifiers’ performance. (2) Methods: We conducted a set of experiments where we varied the durations of the other events on three tasks: crackle vs. wheeze vs. other (3 Class); crackle vs. other (2 Class Crackles); and wheeze vs. other (2 Class Wheezes). Four classifiers (linear discriminant analysis, support vector machines, boosted trees, and convolutional neural networks) were evaluated on those tasks using an open access respiratory sound database. (3) Results: While on the 3 Class task with fixed durations, the best classifier achieved an accuracy of 96.9%, the same classifier reached an accuracy of 81.8% on the more realistic 3 Class task with variable durations. (4) Conclusion: These results demonstrate the importance of experimental design on the assessment of the performance of automatic ARS classification algorithms. Furthermore, they also indicate, unlike what is stated in the literature, that the automatic classification of ARS is not a solved problem, as the algorithms’ performance decreases substantially under complex evaluation scenarios.publishe

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

Multi-Time-Scale Features for Accurate Respiratory Sound Classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation

Multi-time-scale features for accurate respiratory sound classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85% ± 3% and an precision of 80% ± 8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation

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

Distinguishing Between Asthma and Pneumonia Through Automated Lung Sound Analysis

This project attempts to distinguish between two pulmonary disorders, asthma and pneumonia, using automated analysis of lung sounds. Such an approach minimizes the subjectivity of diagnosis inherent to current practices by physicians. Breath sounds are recorded by a physiological microphone and hardware acquisition system, and then analyzed in software using a two stage algorithm. The first stage detects abnormal lung sounds and second stage makes a diagnosis. A clinical trial was conducted at a pediatric clinic to validate the system

Computerized respiratory sounds: a comparison between patients with stable and exacerbated COPD

INTRODUCTION: Diagnosis of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is often challenging as it relies on patients' clinical presentation. Computerized respiratory sounds (CRS), namely crackles and wheezes, may have the potential to contribute for the objective diagnosis/monitoring of an AECOPD. OBJECTIVES: This study explored if CRS differ during stable and exacerbation periods in patients with COPD. METHODS: 13 patients with stable COPD and 14 with AECOPD were enrolled. CRS were recorded simultaneously at trachea, anterior, lateral and posterior chest locations using seven stethoscopes. Airflow (0.4-0.6l/s) was recorded with a pneumotachograph. Breathing phases were detected using airflow signals; crackles and wheezes with validated algorithms. RESULTS: At trachea, anterior and lateral chest, no significant differences were found between the two groups in the number of inspiratory/expiratory crackles or inspiratory wheeze occupation rate. At posterior chest, the number of crackles (median 2.97-3.17 vs. 0.83-1.2, P < 0.001) and wheeze occupation rate (median 3.28%-3.8% vs. 1.12%-1.77%, P = 0.014-0.016) during both inspiration and expiration were significantly higher in patients with AECOPD than in stable patients. During expiration, wheeze occupation rate was also significantly higher in patients with AECOPD at trachea (median 3.12% vs. 0.79%, P < 0.001) and anterior chest (median 3.55% vs. 1.28%, P < 0.001). CONCLUSION: Crackles and wheezes are more frequent in patients with AECOPD than in stable patients, particularly at posterior chest. These findings suggest that these CRS can contribute to the objective diagnosis/monitoring of AECOPD, which is especially valuable considering that they can be obtained by integrating computerized techniques with pulmonary auscultation, a noninvasive method that is a component of patients' physical examination

IMPROVING THE QUALITY, ANALYSIS AND INTERPRETATION OF BODY SOUNDS ACQUIRED IN CHALLENGING CLINICAL SETTINGS

Despite advances in medicine and technology, Acute Lower Respiratory Diseases are a leading cause of sickness and mortality worldwide, highly affecting countries where access to appropriate medical technology and expertise is scarce. Chest auscultation provides a low-cost, non-invasive, widely available tool for the examination of pulmonary health. Despite universal adoption, its use is riddled by a number of issues including subjectivity in interpretation and vulnerability to ambient noise, limiting its diagnostic capability. Digital auscultation and computerized methods come as a natural aid towards overcoming such imposed limitations. Focused on the challenges, we address the demanding real-life scenario of pediatric lung auscultation in busy clinical settings. Two major objectives lead to our contributions: 1) Can we improve the quality of the delicate auscultated sounds and reduce unwanted noise contamination; 2) Can we augment the screening capabilities of current stethoscopes using computerized lung sound analysis to capture the presence of abnormal breaths, and can we standardize findings. To address the first objective, we developed an adaptive noise suppression scheme that tackles contamination coming from a variety of sources, including subject-centric and electronic artifacts, and environmental noise. The proposed method was validated using objective and subjective measures including an expert reviewer panel and objective signal quality metrics. Results revealed the ability and superiority of the proposed method to i) suppress unwanted noise when compared to state-of-the-art technology, and ii) faithfully maintain the signature of the delicate body sounds. The second objective was addressed by exploring appropriate feature representations that capture distinct characteristics of body sounds. A biomimetic approach was employed, and the acoustic signal was projected onto high-dimensional spaces spanning time, frequency, temporal dynamics and spectral modulations. Trained classifiers produced localized decisions on these breath content features, indicating lung diseases. Unlike existing literature, our proposed scheme is further able to combine and integrate the localized decisions into individual, patient-level evaluation. A large corpus of annotated patient data was used to validate our approach, demonstrating the superiority of the proposed features and patient evaluation scheme. Overall findings indicate that improved accessible auscultation care is possible, towards creating affordable health care solutions with worldwide impact