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

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

Design of wireless physiological measurement systems for patients affected by COPD

Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disorder that affects many adults in Canada. Patients affected by such a condition tend to become less active and are at risk of losing functional capacity. Therefore, it is important for them to exercise regularly throughout the year. However, there are some restricting situations, such as cold weather, limited training space and long distance from training centers that may prevent the regular practice of exercise. In those cases, a rehabilitation exercise program at patients’ homes may be a good option. To help these patients remain active, researchers are developing a hardware/software infrastructure for training remote patients via the internet. Since COPD is a disease that can be deadly, 3 key physiological parameters need to be monitored to ensure the safety of the patients: respiratory rate, oxygen saturation and heart rate. The objective of this master’s project was to develop two wireless instruments to measure physiological parameters: a respiratory sensor belt and a pulse oximeter. A series of experiments were carried out to evaluate the performance of the designed instruments. The test results showed these instruments meet the design requirements and are able be used to monitor the COPD patients. This will help patients with COPD stay active all year and ultimately, help maintain their functional capacity and their quality of life. La maladie pulmonaire obstructive chronique (MPOC) est un trouble respiratoire chronique qui affecte un grand nombre d’adultes au Canada. Les patients affectés par cette maladie tendent à être moins actifs et risquent de perdre leur capacité fonctionnelle. Par conséquent, il serait important pour eux de faire de l'activité physique régulièrement. En raison de certaines contraintes, tels que le froid, l’espace d’entraînement limité et l'isolement géographique, la pratique d'activité physique n'est pas toujours possible. Dans ce cas, un programme d’exercices de réhabilitation effectué à domicile pourrait être une bonne solution. Afin d’aider ces patients à demeurer actifs, des chercheurs mettent au point une infrastructure matérielle et logicielle afin d’entraîner ces patients à distance par un lien internet. Puisque la MPOC est une maladie qui peut être mortelle, trois paramètres physiologiques principaux (fréquence respiratoire, la saturation en oxygène et le rythme cardiaque) doivent être surveillés en temps réel pendant l'entraînement pour assurer la sécurité des patients. L’objectif de ce projet de maîtrise est de développer deux instruments sans fil pour mesurer ces paramètres physiologiques : une ceinture de détection du rythme respiratoire et une oxymètre de pouls. Des essais ont été effectués pour évaluer la performance de ces instruments. Les résultats des essais ont montré que ces instruments fonctionnent tel que prévu. À l’aide de ces instruments, les patients MPOC pourraient être actifs pendant toute l’année et ainsi maintenir leurs capacités fonctionnelles

19F Magnetic Resonance Imaging of Lung Ventilation Dynamics and Cell Tracking

The Fluorine isotope 19F has great potential in the use of magnetic resonance imaging (MRI) for clinical applications. 19F is inert, naturally abundant, has a close resonance frequency to proton (1H) (allowing most modern MRI scanners to work with the addition of a tuned coil), has negligible presence in the mammalian body (allowing background signal free acquisitions), and the high gyromagnetic ratio provides sufficient magnetic resonance signal to be visible without hyperpolarization. Uses for 19F MRI includes functional lung imaging, diffusion imaging, cell tracking, and oxygenation sensing among others. Although not widely used in the clinical setting at the time of writing this dissertation. The potential improvements 19F MRI could bring to healthcare are vast. 19F lung imaging has been studied in animal and human models, and has shown to be capable of producing sensitive markers for lung diseases such as cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) by providing spatially localized functional information. In cell tracking, 19F has shown potential in drug delivery monitoring, inflammation imaging, immune cell tracking, and oxygenation measurement with the potential of spatial localization and cell quantification. This dissertation presents my work on human in-vivo multi-breath wash-in/out 19F lung imaging, and the processing of biomarkers more sensitive to CF disease progression over the current gold standard (spirometry). 19F lung MRI was compared to hyperpolarized (HP) Xenon (129Xe) ventilation defect percentage (VDP) analysis. The feasibility of free-breathing 19F lung imaging was explored using a combination of spiral acquisition and denoising. The last two chapters present preliminary work on sequence programming for diffusion imaging and cell tracking at high magnetic fields (9.4T). Preliminary work on oxygen sensing at 9.4T is also explored.Doctor of Philosoph

Locating nidi for high-frequency chest wall oscillation smart therapy via acoustic imaging of lung airways as a spatial network

High-frequency chest wall oscillation (HFCWO) therapy is one of the techniques to facilitate the draining of a patient’s lung secretion in pathological situations, and smart therapy with HFCWO devices equipped with multiple actuators can be achieved via locating nidi in the lung. In this paper, through developing a novel acoustic lung spatial model and utilizing acoustic imaging simulation, a new and effective method for assessing lung function with acoustic imaging is presented, which links acoustic lung images with pathologic changes. The structural similarity between the acoustic reference image based on actual lung sound and our model acoustic image based on the airway impedance was achieved by an index of 0.8987, with 1 as the exact score. Simulation studies based on the model are used to analyze the practicality and the extreme design of the acoustic imaging system on the resolution of the located nidus. For instance, a practical system design with sensor numbers between 4 and 35 may recognize a lower resolution nidus length of 73 mm to a better resolution nidus length of 22 mm. On the other hand, an extreme system design with more than 1000 sensors can recognize greater nidus resolution at under 10 mm. Additionally, this research may be utilized to offer recommendations for acoustic imaging system design and assess the number of sensors and sensing diameter in current acoustic imaging systems. Furthermore, the geographic detection of nidus length allows for analyzing of HFCWO therapy results

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Devices and Data Workflow in COPD Wearable Remote Patient Monitoring: A Systematic Review

Background: With global increase in Chronic Obstructive Pulmonary Disease (COPD) prevalence and mortality rates, and socioeconomical burden continuing to rise, current disease management strategies appear inadequate, paving the way for technological solutions, namely remote patient monitoring (RPM), adoption considering its acute disease events management benefit. One RPM’s category stands out, wearable devices, due to its availability and apparent ease of use. Objectives: To assess the current market and interventional solutions regarding wearable devices in the remote monitoring of COPD patients through a systematic review design from a device composition, data workflow, and collected parameters description standpoint. Methods: A systematic review was conducted to identify wearable device trends in this population through the development of a comprehensive search strategy, searching beyond the mainstream databases, and aggregating diverse information found regarding the same device. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed, and quality appraisal of identified studies was performed using the Critical Appraisal Skills Programme (CASP) quality appraisal checklists. Results: The review resulted on the identification of 1590 references, of which a final 79 were included. 56 wearable devices were analysed, with the slight majority belonging to the wellness devices class. Substantial device heterogeneity was identified regarding device composition type and wearing location, and data workflow regarding 4 considered components. Clinical monitoring devices are starting to gain relevance in the market and slightly over a third, aim to assist COPD patients and healthcare professionals in exacerbation prediction. Compliance with validated recommendations is still lacking, with no devices assessing the totality of recommended vital signs. Conclusions: The identified heterogeneity, despite expected considering the relative novelty of wearable devices, alerts for the need to regulate the development and research of these technologies, specially from a structural and data collection and transmission standpoints.Introdução: Com o aumento global das taxas de prevalência e mortalidade da Doença Pulmonar Obstrutiva Crónica (DPOC) e o seu impacto socioeconómico, as atuais estratégias de gestão da doença parecem inadequadas, abrindo caminho para soluções tecnológicas, nomeadamente para a adoção da monitorização remota, tendo em conta o seu benefício na gestão de exacerbações de doenças crónicas. Dentro destaca-se uma categoria, os dispositivos wearable, pela sua disponibilidade e aparente facilidade de uso. Objetivos: Avaliar as soluções existentes, tanto no mercado, como na área de investigação, relativas a dispositivos wearable utilizados na monitorização remota de pacientes com DPOC através de uma revisão sistemática, do ponto de vista da composição do dispositivo, fluxo de dados e descrição dos parâmetros coletados. Métodos: Uma revisão sistemática foi realizada para identificar tendências destes dispositivos, através do desenvolvimento de uma estratégia de pesquisa abrangente, procurando pesquisar para além das databases convencionais e agregar diversas informações encontradas sobre o mesmo dispositivo. Para tal, foram seguidas as diretrizes PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), e a avaliação da qualidade dos estudos identificados foi realizada utilizando a ferramenta CASP (Critical Appraisal Skills Programme). Resultados: A revisão resultou na identificação de 1590 referências, das quais 79 foram incluídas. Foram analisados 56 dispositivos wearable, com a ligeira maioria a pertencer à classe de dispositivos de wellness. Foi identificada heterogeneidade substancial nos dispositivos em relação à sua composição, local de uso e ao fluxo de dados em relação a 4 componentes considerados. Os dispositivos de monitorização clínica já evidenciam alguma relevância no mercado e, pouco mais de um terço, visam auxiliar pacientes com DPOC e profissionais de saúde na previsão de exacerbações. Ainda assim, é notória a falta do cumprimento das recomendações validadas, não estando disponíveis dispositivos que avaliem a totalidade dos sinais vitais recomendados. Conclusão: A heterogeneidade identificada, apesar de esperada face à relativa novidade dos dispositivos wearable, alerta para a necessidade de regulamentação do desenvolvimento e investigação destas tecnologias, especialmente do ponto de vista estrutural e de recolha e transmissão de dados

Long-term monitoring of respiratory metrics using wearable devices

Recently, there has been an increased interest in monitoring health using wearable sensors technologies however, few have focused on breathing. The utility of constant monitoring of breathing is currently not well understood, both for general health as well as respiratory conditions such as asthma and chronic obstructive pulmonary disease (COPD) that have significant prevalence in society. Having a wearable device that could measure respiratory metrics continuously and non-invasively with high adherence would allow us to investigate the significance of ambulatory breathing monitoring in health and disease management. The purpose of this thesis was to determine if it was feasible to continuously monitor respiratory metrics. To do this, we identified pulse oximetry to provide the best balance between use of mature signal processing methods, commercial availability, power efficiency, monitoring site and perceived wearability. Through a survey, it was found users would monitor their breathing, irrespective of their health status using a smart watch. Then it was found that reducing the duty cycle and power consumption adversely affected the reliability to capture accurate respiratory rate measurements through pulse oximetry. To account for the decreased accuracy of PPG derived respiratory rate at higher rates, a long short-term memory (LSTM) network and a U-Net were proposed, characterised and implemented. In addition to respiratory rate, inspiration time, expiration time, inter-breath intervals and the Inspiration:Expiration ratio were also predicted. Finally, the accuracy of these predictions was validated using pilot data from 11 healthy participants and 11 asthma participants. While percentage bias was low, the 95\% limits of agreement was high. While there is likely going to be enthusiastic uptake in wearable device use, it remains unseen whether clinical utility can be achieved, in particular the ability to forecast respiratory status. Further, the issues of sensor noise and algorithm performance during activity was not calculated. However, this body of work has investigated and developed the use of pulse oximetry, classical signal processing and machine learning methodologies to extract respiratory metrics to lay a foundation for both the hardware and software requirements in future clinical research

Deep learning in structural and functional lung image analysis.

The recent resurgence of deep learning (DL) has dramatically influenced the medical imaging field. Medical image analysis applications have been at the forefront of DL research efforts applied to multiple diseases and organs, including those of the lungs. The aims of this review are twofold: (i) to briefly overview DL theory as it relates to lung image analysis; (ii) to systematically review the DL research literature relating to the lung image analysis applications of segmentation, reconstruction, registration and synthesis. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. 479 studies were initially identified from the literature search with 82 studies meeting the eligibility criteria. Segmentation was the most common lung image analysis DL application (65.9% of papers reviewed). DL has shown impressive results when applied to segmentation of the whole lung and other pulmonary structures. DL has also shown great potential for applications in image registration, reconstruction and synthesis. However, the majority of published studies have been limited to structural lung imaging with only 12.9% of reviewed studies employing functional lung imaging modalities, thus highlighting significant opportunities for further research in this field. Although the field of DL in lung image analysis is rapidly expanding, concerns over inconsistent validation and evaluation strategies, intersite generalisability, transparency of methodological detail and interpretability need to be addressed before widespread adoption in clinical lung imaging workflow