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Breath sounds have long been important indicators of respiratory health and disease. Acoustical monitoring of respiratory sounds has been used by researchers for various diagnostic purposes. A few decades ago, physicians relied on their hearing to detect any symptomatic signs in respiratory sounds of their patients. However, with the aid of computer technology and digital signal processing techniques in recent years, breath sound analysis has drawn much attention because of its diagnostic capabilities. Computerized respiratory sound analysis can now quantify changes in lung sounds; make permanent records of the measurements made and produce graphical representations that help with the diagnosis and treatment of patients suffering from lung diseases. Digital signal processing techniques have been widely used to derive characteristics features of the lung sounds for both diagnostic and assessment of treatment purposes. Although the analytical techniques of signal processing are largely independent of the application, interpretation of their results on biological data, i.e. respiratory sounds, requires substantial understanding of the involved physiological system. This lecture series begins with an overview of the anatomy and physiology related to human respiratory system, and proceeds to advanced research in respiratory sound analysis and modeling, and their application as diagnostic aids. Although some of the used signal processing techniques have been explained briefly, the intention of this book is not to describe the analytical methods of signal processing but the application of them and how the results can be interpreted. The book is written for engineers with university level knowledge of mathematics and digital signal processing.
Lung sounds auscultation is often the first noninvasive resource for detection and discrimination of respiratory pathologies available to the physician through the use of the stethoscope. Hearing interpretation, though, was the only means of appreciation of the lung sounds diagnostic information for many decades. Nevertheless, in recent years, computerized auscultation combined with signal processing techniques has boosted the diagnostic capabilities of lung sounds. The latter were traditionally analyzed and characterized by morphological changes in the time domain using statistical measures, by spectral properties in the frequency domain using simple spectral analysis, or by nonstationary properties in a joint time–frequency domain using short-time Fourier transform. Advanced signal processing techniques, however, have emerged in the last decade, broadening the perspective in lung sounds analysis. The scope of this book is to present up-to-date signal processing techniques that have been applied to the area of lung sound analysis. It starts with a description of the nature of lung sounds and continues with the introduction of new domains in their representation, new denoising techniques, and concludes with some reflective implications, both from engineers’ and physicians’ perspective. Issues of nonstationarity, nonlinearity, non-Gaussianity, modeling, and classification of lung sounds are addressed with new methodologies, revealing a more realistic approach to their pragmatic nature. Advanced denoising techniques that effectively circumvent the noise presence (e.g., heart sound interference, background noise) in lung sound recordings are described, providing the physician with high-quality auscultative data. The book offers useful information both to engineers and physicians interested in bioacoustics, clearly demonstrating the current trends in lung sound analysis. Table of Contents: The Nature of Lung Sound Signals / New Domains in LS Representation / Denoising Techniques / Reflective Implications
Breath sounds have long been important indicators of respiratory health and disease. Acoustical monitoring of respiratory sounds has been used by researchers for various diagnostic purposes. A few decades ago, physicians relied on their hearing to detect any symptomatic signs in respiratory sounds of their patients. However, with the aid of computer technology and digital signal processing techniques in recent years, breath sound analysis has drawn much attention because of its diagnostic capabilities. Computerized respiratory sound analysis can now quantify changes in lung sounds; make permanent records of the measurements made and produce graphical representations that help with the diagnosis and treatment of patients suffering from lung diseases. Digital signal processing techniques have been widely used to derive characteristics features of the lung sounds for both diagnostic and assessment of treatment purposes. Although the analytical techniques of signal processing are largely independent of the application, interpretation of their results on biological data, i.e. respiratory sounds, requires substantial understanding of the involved physiological system. This lecture series begins with an overview of the anatomy and physiology related to human respiratory system, and proceeds to advanced research in respiratory sound analysis and modeling, and their application as diagnostic aids. Although some of the used signal processing techniques have been explained briefly, the intention of this book is not to describe the analytical methods of signal processing but the application of them and how the results can be interpreted. The book is written for engineers with university level knowledge of mathematics and digital signal processing.
Published in 1995: Breath Sounds Methodology is a practical introduction to the measurement of the acoustic properties of the respiratory system. The author describes objective and quantitative methods for extracting the information embedded in the sounds produced in the airways and by the lung during breathing.
Respiratory diseases are a leading cause of death worldwide. Despite modern medicine, treatment of lung diseases is limited by the tools available to diagnose these disorders, especially in low resource settings. While tools such as chest x-ray and CT scans are highly accurate, their high cost provides a high barrier for many patient populations. The physical exam has been a long standing tried and true method that provides a low cost solution for for diagnosis of many common lung diseases including pneumonia. However, this method is subjective and its sensitivity is limited to the operator ability. Lung sound classification and using a digital stethoscope can be used to provide an immediate diagnostic for respiratory-related diseases. The International Conference on Biomedical and Health Informatics (ICBHI) created a sound data base in 2017 that is annotated with a classification of the lung sound by physicians. In this thesis, artificial intelligence libraries are used in a deeo learning architecture to identify and classify the lung sounds. The data set was split into training and test data and evaluated using standard performance metrics: precision, 92.3%, accuracy, 87.3%, sensitivity (recall), 87.1%, specificity, 87.5% and F1 Score, 0.89%. Because the data set is skewed right, the best evaluation metric is the F1 Score, which is a weighted average of precision and sensitivity. The F1 score was found to be better than other comparable known attempts on this same data set. The space for new, innovative, portable and affordable diagnostic devices that aid patients towards pulmonary health and wellness will likely push the development further of the acceptance of electronic auscultations. As telemedicine grows, this will also drive up the demands for such devices. Other holistic measures that are used in medicine will likely also be be developed as the landscape of healthtech changes what is possible.
Many resource-poor communities across the globe lack access to quality healthcare, due to shortages in medical expertise and poor availability of medical diagnostic devices. In recent years, mobile phones have become increasingly complex and ubiquitous. These devices present a tremendous opportunity to provide low-cost diagnostics to under-served populations and to connect non-experts with experts. This thesis explores the capture of cardiac and respiratory sounds on a mobile phone for analysis, with the long-term aim of developing intelligent algorithms for the detection of heart and respiratory-related problems. Using standard labeled databases, existing and novel algorithms are developed to analyze cardiac and respiratory audio data. In order to assess the algorithms' performance under field conditions, a low-cost stethoscope attachment is constructed and data is collected using a mobile phone. Finally, a telemedicine infrastructure and work-flow is described, in which these algorithms can be deployed and trained in a large-scale deployment.
Respiratory sound analysis offers critical and clear advantages for diagnosis and monitoring of respiratory disorders. Yet, the recording technology available today remains relatively undeveloped and non-specialized. Standard high-quality audio recording systems often do not capture the low-frequency spectrum of respiratory sounds; not to mention such equipment typically has high associated costs. Contributing to the issue is a lack of standardization of equipment used; hence, what follows is a large variability of the recordings and the inability to effectively compare results between different recording systems. The objective of the following presented work was to design and build an electronic audio recording device along with microphone and suitable air-chamber to be placed over the trachea or lung for capturing respiratory breathing sounds. Design objectives included maintaining a cost-effective, portable, and small form-factor for the device as well as compatibility with our team's patented obstructive sleep apnea (OSA) diagnostic algorithms to detect for the severity of OSA during either overnight sleep or wakefulness. All desired objectives for the design were able to be realized in a compact, cost-effective, and highly accurate device. When reviewing the tracheal breathing sound recordings conducted with the device, the results identify signal content, albeit low amplitude, past 5 kHz up to 9 kHz; that indicates the previous cut-off point of many precursory studies might not have been adequate for capturing the entire characteristic features of tracheal respiratory sounds.