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

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

BreathPrint: Breathing acoustics-based user authentication

Ministry of Education, Singapore under its Academic Research Funding Tier 2; National Research Foundation (NRF) Singapore under IDM Futures Funding Initiativ

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

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

Stethoscope acoustics

Audible-frequency sounds from the human body are an invaluable diagnostic tool. For over 200 years, stethoscopes have been used to listen to these sounds. Despite this, the physics of how stethoscopes work remain poorly understood. While the stethoscope itself is a simple device, its performance depends on how it is coupled to both the patient and the clinician. Existing models do not adequately address these interactions, forcing design choices to be made based on simple heuristics. The aims of this thesis are to provide a theoretical framework for understanding the acoustics of stethoscopes, propose a low order model to simulate the response, and develop an experimental methodology to validate the model. When a stethoscope is pressed against the chest, body sounds induce small perturbations around the equilibrium position of the nonlinear chest-stethoscope system. In this thesis, a lumped-element approach is used to model these perturbations. The resulting models are validated using experiments conducted on a phantom (a laboratory model representing the human chest). Impedance measurements on the phantom and on the human chest allow differences between these systems to be accounted for. The models presented in this thesis capture the trends associated with each of the key design parameters. Minimising the cavity volume maximises the response, while tubing significantly attenuates low frequencies and introduces distorting standing-wave resonances. Using a diaphragm attenuates the response and shifts the resonances to higher frequencies, but also allows smaller air cavities to be used. Holding a stethoscope against the chest sets the equilibrium position of the coupled system and provides a damping-dominated impedance load on the chestpiece. The strong dependence of a stethoscope’s performance on external factors, such as the properties of the chest and the way it is held, makes it difficult to compare sensors objectively. The work presented in this thesis dispels various misconceptions about how stethoscopes work and can be used to inform design choices, ultimately improving the diagnostic capabilities of future stethoscopes.Magdalene College, Cambridge Philosophical Societ

Gapped cantilever for the enhancement of strain sensitivity and energy efficiency

Cantilever structures have been widely used in a large variety of transducer applications. For cantilever based transducers, piezoresistive/piezoelectric mechanisms has always been a popular choice due to the advantages of being low cost, simple structure and portability. However, low sensitivity is recognized as a major disadvantage of these transducers compared with optical based measurement. In this research, a gapped cantilever structure is proposed to potentially increase the sensitivity by orders of magnitude. In order to guide the design, an advanced analytical model is developed, and the increased strain sensitivity is theoretically demonstrated. In addition, optimizations with this model interestingly reveal that the gapped cantilever is much more efficient than conventional cantilever from energy perspective as well. Applications of gapped cantilever structure including piezoresistive accelerometer, vibration energy harvester and resonant mass sensor are carefully investigated in this work. Multiple prototypes of these applications both in meso-scale and micro-scale are designed, manufactured and characterized. The testing results show good agreement with theoretical expectation, and demonstrate a good potential of gapped cantilever structure for the enhancement of strain sensitivity and energy efficiency

Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023