Reproducibility of dynamically represented acoustic lung images from healthy individuals

Background and aim: Acoustic lung imaging offers a unique method for visualising the lung. This study was designed to demonstrate reproducibility of acoustic lung images recorded from healthy individuals at different time points and to assess intra- and inter-rater agreement in the assessment of dynamically represented acoustic lung images. Methods: Recordings from 29 healthy volunteers were made on three separate occasions using vibration response imaging. Reproducibility was measured using quantitative, computerised assessment of vibration energy. Dynamically represented acoustic lung images were scored by six blinded raters. Results: Quantitative measurement of acoustic recordings was highly reproducible with an intraclass correlation score of 0.86 (very good agreement). Intraclass correlations for inter-rater agreement and reproducibility were 0.61 (good agreement) and 0.86 (very good agreement), respectively. There was no significant difference found between the six raters at any time point. Raters ranged from 88% to 95% in their ability to identically evaluate the different features of the same image presented to them blinded on two separate occasions. Conclusion: Acoustic lung imaging is reproducible in healthy individuals. Graphic representation of lung images can be interpreted with a high degree of accuracy by the same and by different reviewers

New Models for Expert System Design

This thesis presents new work on the analysis of human lung sound. Experimental studies investigated the relationship between the condition of the lungs and the power spectrum of lung sound detected at the chest wall. The conclusion drawn from two clinical studies was that the median frequency of the lung sound power spectrum increases with a decrease in airway calibre. The technique for the analysis of lung sound presented in this thesis is a non-invasive method which may be capable of assessing differences in airway calibre between different lobes of the lung. An expert system for the analysis of lung sound data and pulmonary function data was designed. The expert knowledge was expressed in a belief logic, a system of logic which is more expressive than first order logic. New automated theorem proving methods were developed for the belief logic. The new methods were implemented to form the 'inference engine' of the expert system. The new expert system compared favourably with systems which perform a similar task. The use of belief logic allows introspective reasoning to be carried out. Plausible reasoning, a type of introspective reasoning which allows conclusions to be drawn when the database is incomplete, was proposed and tested. The author concludes that the use of a belief logic in expert system design has significant advantages over conventional approaches. The experimental results of the lung sound research were incorporated into the expert system rule base: the medical and expert system research were complementary

Novel Measurements of Cough and Breathing Abnormalities during Sleep in Cystic Fibrosis

This Doctor of Philosophy thesis describes cystic fibrosis (CF), sleep parameters and novel measurement techniques to determine the effect of lung disease on sleep using non-invasive techniques. Cystic Fibrosis (CF) is characterised by lungs that are normal at birth, but as lung disease progresses with age, adults with CF can develop sleep abnormalities including alteration in sleep architecture and sleep disordered breathing. This thesis seeks to investigate simple non-invasive measures which can detect abnormalities of sleep and breathing in CF adults. The identification of respiratory sounds (normal lung sounds, coughs, crackles, wheezes and snores) will be examined using the non-invasive sleep and breathing measurement device, the Sonomat. The characterisation of these respiratory sounds will be based on spectrographic and audio analysis of the Sonomat. Cross-sectional and longitudinal analysis of adults with CF using polysomnography and the Sonomat will further assess objective sleep and breathing abnormalities. Additional to the examination of objective measurements of sleep, subjective evaluation using CF-specific and sleep-specific questionnaires will assess subjective sleep quality and QoL in adults with CF

Applications of aerospace technology in the public sector

Current activities of the program to accelerate specific applications of space related technology in major public sector problem areas are summarized for the period 1 June 1971 through 30 November 1971. An overview of NASA technology, technology applications, and supporting activities are presented. Specific technology applications in biomedicine are reported including cancer detection, treatment and research; cardiovascular diseases, diagnosis, and treatment; medical instrumentation; kidney function disorders, treatment, and research; and rehabilitation medicine

Computerized respiratory sounds can differentiate smokers and non-smokers

Cigarette smoking is often associated with the development of several respiratory diseases however, if diagnosed early, the changes in the lung tissue caused by smoking may be reversible. Computerised respiratory sounds have shown to be sensitive to detect changes within the lung tissue before any other measure, however it is unknown if it is able to detect changes in the lungs of healthy smokers. This study investigated the differences between computerised respiratory sounds of healthy smokers and non-smokers. Healthy smokers and non-smokers were recruited from a university campus. Respiratory sounds were recorded simultaneously at 6 chest locations (right and left anterior, lateral and posterior) using air-coupled electret microphones. Airflow (1.0–1.5 l/s) was recorded with a pneumotachograph. Breathing phases were detected using airflow signals and respiratory sounds with validated algorithms. Forty-four participants were enrolled: 18 smokers (mean age 26.2, SD = 7 years; mean FEV1 % predicted 104.7, SD = 9) and 26 non-smokers (mean age 25.9, SD = 3.7 years; mean FEV1 % predicted 96.8, SD = 20.2). Smokers presented significantly higher frequency at maximum sound intensity during inspiration [(M = 117, SD = 16.2 Hz vs. M = 106.4, SD = 21.6 Hz; t(43) = −2.62, p = 0.0081, d z = 0.55)], lower expiratory sound intensities (maximum intensity: [(M = 48.2, SD = 3.8 dB vs. M = 50.9, SD = 3.2 dB; t(43) = 2.68, p = 0.001, d z = −0.78)]; mean intensity: [(M = 31.2, SD = 3.6 dB vs. M = 33.7,SD = 3 dB; t(43) = 2.42, p = 0.001, d z = 0.75)] and higher number of inspiratory crackles (median [interquartile range] 2.2 [1.7–3.7] vs. 1.5 [1.2–2.2], p = 0.081, U = 110, r = −0.41) than non-smokers. Significant differences between computerised respiratory sounds of smokers and non-smokers have been found. Changes in respiratory sounds are often the earliest sign of disease. Thus, computerised respiratory sounds might be a promising measure to early detect smoking related respiratory diseases

Advanced sensing technologies and systems for lung function assessment

Chest X-rays and computed tomography scans are highly accurate lung assessment tools, but their hazardous nature and high cost remain a barrier for many patients. Acoustic imaging is an alternative to lung function assessment that is non-hazardous, less costly, and has a patient-to-equipment approach. In this thesis, the suitability of acoustic imaging for lung health assessment is proven via systematic review and numerical airway modelling. An acoustic lung sound acquisition system, consisting of an optimal denoising filter translated into imaging for continual and reliable lung function assessment, is then developed. To the author’s best knowledge, locating obstructed airways via an acoustic lung model andthe resulting acoustic lung imaging have yet to be investigated in the open literature; hence,a novel acoustic lung spatial model was first developed in this research, which links acousticlung sounds and acoustic images with pathologic changes. About 89% structural similaritybetween an acoustic reference image based on actual lung sound and the developed modelacoustic image based on the computation of airway impedance was achieved. External interference is inevitable in lung sound recordings; thus, an indirect unifying of wavelet-based total variation (WATV) and empirical Wiener denoising filter is proposed to enhance recorded lung sound signals. To the author’s best knowledge, the integration of WATV and Wiener filters has not been investigated for lung sound signals. Selection and analysis of optimal parameters for the denoising filter were performed through a case study. The optimal parameters achieved through simulation studies led to an average 12.69 ± 5.05 dB improvement in signal-to-noise ratio (SNR), and the average SNR was improved by 16.92 ± 8.51 dB in the experimental studies. The hybrid denoising filter significantly enhances the signal quality of the captured lung sounds while preserving the characteristics of a lung sound signal and is less sensitive to the variation of SNR values of the input signal. A robust system was developed based on the established lung spatial model and denoising filter through hardware redesign and signal processing, which outperformed commercial digital stethoscopes regarding SNR and root mean square error by about 8 dB and 0.15, respectively. Regarding sensing sensitivity power spectrum mapping, the developed system sensors’ position is neutral, as opposed to digital stethoscopes, when representing lung signals, with a signal power loss ratio of around 5 dB compared to 10 dB from digital stethoscopes. The developed system obtains better detection by about 10% in the obstructed airway region compared to digital stethoscopes in the experimental studies

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis