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

Doctor of Philosophy

dissertationPatients sometimes suffer apnea during sedation procedures or after general anesthesia. Apnea presents itself in two forms: respiratory depression (RD) and respiratory obstruction (RO). During RD the patients' airway is open but they lose the drive to breathe. During RO the patients' airway is occluded while they try to breathe. Patients' respiration is rarely monitored directly, but in a few cases is monitored with a capnometer. This dissertation explores the feasibility of monitoring respiration indirectly using an acoustic sensor. In addition to detecting apnea in general, this technique has the possibility of differentiating between RD and RO. Data were recorded on 24 subjects as they underwent sedation. During the sedation, subjects experienced RD or RO. The first part of this dissertation involved detecting periods of apnea from the recorded acoustic data. A method using a parameter estimation algorithm to determine the variance of the noise of the audio signal was developed, and the envelope of the audio data was used to determine when the subject had stopped breathing. Periods of apnea detected by the acoustic method were compared to the periods of apnea detected by the direct flow measurement. This succeeded with 91.8% sensitivity and 92.8% specificity in the training set and 100% sensitivity and 98% specificity in the testing set. The second part of this dissertation used the periods during which apnea was detected to determine if the subject was experiencing RD or RO. The classifications determined from the acoustic signal were compared to the classifications based on the flow measurement in conjunction with the chest and abdomen movements. This did not succeed with a 86.9% sensitivity and 52.6% specificity in the training set, and 100% sensitivity and 0% specificity in the testing set. The third part of this project developed a method to reduce the background sounds that were commonly recorded on the microphone. Additive noise was created to simulate noise generated in typical settings and the noise was removed via an adaptive filter. This succeeded in improving or maintaining apnea detection given the different types of sounds added to the breathing data

SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses and four million people die annually due to air pollution. Regular lung health monitoring can lead to prognoses about deteriorating lung health conditions. This paper presents our system SpiroMask that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for continuous lung health monitoring. We evaluate our approach on 48 participants (including 14 with lung health issues) and find that we can estimate parameters such as lung volume and respiration rate within the approved error range by the American Thoracic Society (ATS). Further, we show that our approach is robust to sensor placement inside the mask.Comment: Accepted in the ACM Transactions on Computing for Healthcare (HEALTH

Acute lung injury in paediatric intensive care: course and outcome

Introduction: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) carry a high morbidity and mortality (10-90%). ALI is characterised by non-cardiogenic pulmonary oedema and refractory hypoxaemia of multifactorial aetiology [1]. There is limited data about outcome particularly in children. Methods This retrospective cohort study of 85 randomly selected patients with respiratory failure recruited from a prospectively collected database represents 7.1% of 1187 admissions. They include those treated with High Frequency Oscillation Ventilation (HFOV). The patients were admitted between 1 November 1998 and 31 October 2000. Results: Of the 85, 49 developed acute lung injury and 47 had ARDS. There were 26 males and 23 females with a median age and weight of 7.7 months (range 1 day-12.8 years) and 8 kg (range 0.8-40 kg). There were 7 deaths giving a crude mortality of 14.3%, all of which fulfilled the Consensus I [1] criteria for ARDS. Pulmonary occlusion pressures were not routinely measured. The A-a gradient and PaO2/FiO2 ratio (median + [95% CI]) were 37.46 [31.82-43.1] kPa and 19.12 [15.26-22.98] kPa respectively. The non-survivors had a significantly lower PaO2/FiO2 ratio (13 [6.07-19.93] kPa) compared to survivors (23.85 [19.57-28.13] kPa) (P = 0.03) and had a higher A-a gradient (51.05 [35.68-66.42] kPa) compared to survivors (36.07 [30.2-41.94]) kPa though not significant (P = 0.06). Twenty-nine patients (59.2%) were oscillated (Sensormedics 3100A) including all 7 non-survivors. There was no difference in ventilation requirements for CMV prior to oscillation. Seventeen of the 49 (34.7%) were treated with Nitric Oxide including 5 out of 7 non-survivors (71.4%). The median (95% CI) number of failed organs was 3 (1.96-4.04) for non-survivors compared to 1 (0.62-1.62) for survivors (P = 0.03). There were 27 patients with isolated respiratory failure all of whom survived. Six (85.7%) of the non-survivors also required cardiovascular support.Conclusion: A crude mortality of 14.3% compares favourably to published data. The A-a gradient and PaO2/FiO2 ratio may be of help in morbidity scoring in paediatric ARDS. Use of Nitric Oxide and HFOV is associated with increased mortality, which probably relates to the severity of disease. Multiple organ failure particularly respiratory and cardiac disease is associated with increased mortality. ARDS with isolated respiratory failure carries a good prognosis in children

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

Deep sleep: deep learning methods for the acoustic analysis of sleep-disordered breathing

Sleep-disordered breathing (SDB) is a serious and prevalent condition that results from the collapse of the upper airway during sleep, which leads to oxygen desaturations, unphysiological variations in intrathoracic pressure, and sleep fragmentation. Its most common form is obstructive sleep apnoea (OSA). This has a big impact on quality of life, and is associated with cardiovascular morbidity. Polysomnography, the gold standard for diagnosing SDB, is obtrusive, time-consuming and expensive. Alternative diagnostic approaches have been proposed to overcome its limitations. In particular, acoustic analysis of sleep breathing sounds offers an unobtrusive and inexpensive means to screen for SDB, since it displays symptoms with unique acoustic characteristics. These include snoring, loud gasps, chokes, and absence of breathing. This thesis investigates deep learning methods, which have revolutionised speech and audio technology, to robustly screen for SDB in typical sleep conditions using acoustics. To begin with, the desirable characteristics for an acoustic corpus of SDB, and the acoustic definition of snoring are considered to create corpora for this study. Then three approaches are developed to tackle increasingly complex scenarios. Firstly, with the aim of leveraging a large amount of unlabelled SDB data, unsupervised learning is applied to learn novel feature representations with deep neural networks for the classification of SDB events such as snoring. The incorporation of contextual information to assist the classifier in producing realistic event durations is investigated. Secondly, the temporal pattern of sleep breathing sounds is exploited using convolutional neural networks to screen participants sleeping by themselves for OSA. The integration of acoustic features with physiological data for screening is examined. Thirdly, for the purpose of achieving robustness to bed partner breathing sounds, recurrent neural networks are used to screen a subject and their bed partner for SDB in the same session. Experiments conducted on the constructed corpora show that the developed systems accurately classify SDB events, screen for OSA with high sensitivity and specificity, and screen a subject and their bed partner for SDB with encouraging performance. In conclusion, this thesis makes promising progress in improving access to SDB diagnosis through low-cost and non-invasive methods

Case Report: Generalized Mutual Information (GMI) Analysis of Sensory Motor Rhythm in a Subject Affected by Facioscapulohumeral Muscular Dystrophy after Ken Ware Treatment

In this case report we study the dynamics of the SMR band in a subject affected from Facioscapulohumeral Muscular Dystrophy and subjected to Ken Ware Neuro Physics treatment. We use the Generalized Mutual Information (GMI) to analyze in detail the SMR band at rest during the treatment. Brain dynamics responds to a chaotic-deterministic regime with a complex behaviour that constantly self-rearranges and self-organizes such dynamics in function of the outside requirements. We demonstrate that the SMR chaotic dynamics responds directly to such regime and that also decreasing in EEG during muscular activity really increases its ability of self-arrangement and self-organization in brain. The proposed novel method of the GMI is arranged by us so that it may be used in several cases of clinical interest. In the case of muscular dystrophy here examined, GMI enables us to quantify with accuracy the improvement that the subject realizes during such treatment

Tracheal Breath Sounds - instrumentation and origin

Tässä työssä tehtiin kirjallisuuskatsaus trakeasta kuultavien hengitysäänien instrumentointiin, normaaleihin ja epätavallisiin trakeasta kuultaviin ääniin. Katsauksessa havaittiin, että hengitysäänten rekisteröintiin käytettävien sensoreiden suunnittelulla voidaan vaikuttaa rekisteröityihin ääniin. Erityisesti ilmakytkettyjen kontaktimikrofonien ilmakammiolla voi olla suuri vaikutus mitattuihin ääniin. Normaalien trakeahengitysäänien alkuperä on aeroakustinen. Ilmavirtauksen nopeuden kasvaessa laminaarinen virtaus irtautuu henkitorven seinämästä ja virtaukseen syntyy pyörteitä. Virtaus on muuttunut turbulenttiseksi. Kun nämä pyörteet kulkeutuvat kapeikon läpi, mikä voi olla pelkkä muutos henkitorven poikkipinta-alassa, osa pyörteen liike-energiasta muuttuu akustiseksi häiriöksi. Tämän lisäksi kirjallisuuskatsauksessa tutkittiin epätavallisten trakeasta kuultavien hengitysäänten syntyä. Avoimen, osittain ahtautuneen ja kokonaan ahtautuneen hengitystien tapaukset tarkasteltiin erikseen Sterling-vastusmallin avulla. Kuorsausta ja kudosvärinää havaittiin tilanteessa, jolloin hengitystie oli osittain ahtautunut. Pirkanmaan sairaanhoitopiirin unilaboratoriossa käytetylle trakeaäänien rekisteröintilaitteistolle tehtiin laatumittauksia. Mittauksissa havaittiin, että mikrofonissa oli hieman harmonista vääristymää ja käytetyn äänikortin taajuusvaste ei ollut suora, kun näytteenottotaajuutena oli 11kHz. Yhteenvetona voidaan sanoa, että eri tutkimusryhmien tulisi käyttää laadultaan korkeatasoisia mittalaitteita, kun trakeasta kuuluvia hengitysääniä rekisteröidään. Jos näin toimittaisiin, korkealaatuinen trakea-hengitysääni voisi tuoda uutta tietoa unen hengitysfysiologiaan liittyen. Nykyisessä tilanteessa eri tutkimusryhmien mittaukset eivät ole täysin vertailukelpoisia keskenään ja tämän vuoksi tieteellistä luotettavuutta on vaikea saavuttaa

Snoring and arousals in full-night polysomnographic studies from sleep apnea-hypopnea syndrome patients

SAHS (Sleep Apnea-Hypopnea Syndrome) is recognized to be a serious disorder with high prevalence in the population. The main clinical triad for SAHS is made up of 3 symptoms: apneas and hypopneas, chronic snoring and excessive daytime sleepiness (EDS). The gold standard for diagnosing SAHS is an overnight polysomnographic study performed at the hospital, a laborious, expensive and time-consuming procedure in which multiple biosignals are recorded. In this thesis we offer improvements to the current approaches to diagnosis and assessment of patients with SAHS. We demonstrate that snoring and arousals, while recognized key markers of SAHS, should be fully appreciated as essential tools for SAHS diagnosis. With respect to snoring analysis (applied to a 34 subjects¿ database with a total of 74439 snores), as an alternative to acoustic analysis, we have used less complex approaches mostly based on time domain parameters. We concluded that key information on SAHS severity can be extracted from the analysis of the time interval between successive snores. For that, we built a new methodology which consists on applying an adaptive threshold to the whole night sequence of time intervals between successive snores. This threshold enables to identify regular and non-regular snores. Finally, we were able to correlate the variability of time interval between successive snores in short 15 minute segments and throughout the whole night with the subject¿s SAHS severity. Severe SAHS subjects show a shorter time interval between regular snores (p=0.0036, AHI cp(cut-point): 30h-1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p=0.006, AHI cp: 30h-1) is seen for less severe SAHS subjects. Also, we have shown successful in classifying the subjects according to their SAHS severity using the features derived from the time interval between regular snores. Classification accuracy values of 88.2% (with 90% sensitivity, 75% specificity) and 94.1% (with 94.4% sensitivity, 93.8% specificity) for AHI cut-points of severity of 5 and 30h-1, respectively. In what concerns the arousal study, our work is focused on respiratory and spontaneous arousals (45 subjects with a total of 2018 respiratory and 2001 spontaneous arousals). Current beliefs suggest that the former are the main cause for sleep fragmentation. Accordingly, sleep clinicians assign an important role to respiratory arousals when providing a final diagnosis on SAHS. Provided that the two types of arousals are triggered by different mechanisms we hypothesized that there might exist differences between their EEG content. After characterizing our arousal database through spectral analysis, results showed that the content of respiratory arousals on a mild SAHS subject is similar to that of a severe one (p>>0.05). Similar results were obtained for spontaneous arousals. Our findings also revealed that no differences are observed between the features of these two kinds of arousals on a same subject (r=0.8, p<0.01 and concordance with Bland-Altman analysis). As a result, we verified that each subject has almost like a fingerprint or signature for his arousals¿ content and is similar for both types of arousals. In addition, this signature has no correlation with SAHS severity and this is confirmed for the three EEG tracings (C3A2, C4A1 and O1A2). Although the trigger mechanisms of the two arousals are known to be different, our results showed that the brain response is fairly the same for both of them. The impact that respiratory arousals have in the sleep of SAHS patients is unquestionable but our findings suggest that the impact of spontaneous arousals should not be underestimated

IMAGE-BASED RESPIRATORY MOTION EXTRACTION AND RESPIRATION-CORRELATED CONE BEAM CT (4D-CBCT) RECONSTRUCTION

Accounting for respiration motion during imaging helps improve targeting precision in radiation therapy. Respiratory motion can be a major source of error in determining the position of thoracic and upper abdominal tumor targets during radiotherapy. Thus, extracting respiratory motion is a key task in radiation therapy planning. Respiration-correlated or four-dimensional CT (4DCT) imaging techniques have been recently integrated into imaging systems for verifying tumor position during treatment and managing respiration-induced tissue motion. The quality of the 4D reconstructed volumes is highly affected by the respiratory signal extracted and the phase sorting method used. This thesis is divided into two parts. In the first part, two image-based respiratory signal extraction methods are proposed and evaluated. Those methods are able to extract the respiratory signals from CBCT images without using external sources, implanted markers or even dependence on any structure in the images such as the diaphragm. The first method, called Local Intensity Feature Tracking (LIFT), extracts the respiratory signal depending on feature points extracted and tracked through the sequence of projections. The second method, called Intensity Flow Dimensionality Reduction (IFDR), detects the respiration signal by computing the optical flow motion of every pixel in each pair of adjacent projections. Then, the motion variance in the optical flow dataset is extracted using linear and non-linear dimensionality reduction techniques to represent a respiratory signal. Experiments conducted on clinical datasets showed that the respiratory signal was successfully extracted using both proposed methods and it correlates well with standard respiratory signals such as diaphragm position and the internal markers’ signal. In the second part of this thesis, 4D-CBCT reconstruction based on different phase sorting techniques is studied. The quality of the 4D reconstructed images is evaluated and compared for different phase sorting methods such as internal markers, external markers and image-based methods (LIFT and IFDR). Also, a method for generating additional projections to be used in 4D-CBCT reconstruction is proposed to reduce the artifacts that result when reconstructing from an insufficient number of projections. Experimental results showed that the feasibility of the proposed method in recovering the edges and reducing the streak artifacts