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

Sleep-Disordered Breathing Affects Auditory Processing in 5–7 Year-Old Children: Evidence From Brain Recordings

Poor sleep in children is associated with lower neurocognitive functioning and increased maladaptive behaviors. The current study examined the impact of snoring (the most common manifestation of sleep-disordered breathing) on cognitive and brain functioning in a sample of 35 asymptomatic children ages 5–7 years identified in the community as having habitual snoring (SDB). All participants completed polysomnographic, neurocognitive (NEPSY) and psychophysiological (ERPs to speech sounds) assessments. The results indicated that sub-clinical levels of SDB may not necessarily lead to reduced performance on standardized behavioral measures of attention and memory. However, brain indices of speech perception and discrimination (N1/P2) are sensitive to individual differences in the quality of sleep. We postulate that addition of ERPs to the standard clinical measures of sleep problems could lead to early identification of children who may be more cognitively vulnerable because of chronic sleep disturbances

Description of a novel method for detection of sleep-disordered breathing in brachycephalic dogs

Background: Sleep-disordered breathing (SDB), defined as any difficulty in breathing during sleep, occurs in brachycephalic dogs. Diagnostic methods for SDB in dogs require extensive equipment and laboratory assessment. Objectives: To evaluate the usability of a portable neckband system for detection of SDB in dogs. We hypothesized that the neckband is a feasible method for evaluation of SDB and that brachycephaly predisposes to SDB. Animals: Twenty-four prospectively recruited client-owned dogs: 12 brachycephalic dogs and 12 control dogs of mesocephalic or dolicocephalic breeds. Methods: Prospective observational cross-sectional study with convenience sampling. Recording was done over 1 night at each dog's home. The primary outcome measure was the obstructive Respiratory Event Index (OREI), which summarized the rate of obstructive SDB events per hour. Additionally, usability, duration of recording, and snore percentage were documented. Results: Brachycephalic dogs had a significantly higher OREI value (Hodges-Lehmann estimator for median difference = 3.5, 95% confidence interval [CI] 2.2-6.8; P <.001) and snore percentage (Hodges-Lehmann estimator = 34.2, 95% CI 13.6-60.8; P <.001) than controls. A strong positive correlation between OREI and snore percentage was detected in all dogs (rs =.79, P <.001). The neckband system was easy to use. Conclusions and Clinical Importance: Brachycephaly is associated with SDB. The neckband system is a feasible way of characterizing SDB in dogs.publishedVersionPeer reviewe

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

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

Submandibular mechanical stimulation of upper airway muscles to treat obstructive sleep apnea

The extrinsic tongue muscles are activated in coordination with pharyngeal muscles to keep a patent airway during respiration in wakefulness and sleep. The activity of genioglossus, the primary tongue-protruding muscle playing an important role in this coordination, is known to be modulated by several reflex pathways mediated through the mechanoreceptors of the upper airways. The main objective is to investigate the effectiveness of activating these reflex pathways with mechanical stimulations, for the long-term goal of improving the upper airway patency during disordered breathing in sleep. The genioglossus response is examined during mandibular and sub-mandibular mechanical stimulations in healthy subjects during wakefulness. The genioglossus activity is recorded with custom-made sublingual EMG electrode molded out of silicone. Mechanical vibrations are applied to the lower jaw at 8 and 12 Hz with an amplitude of 5 mm in the first experiment, and to the sub-mandibular area at three different intensities (0.2-0.9 mm, 21-33 Hz) in the second experiment. The effects of sub-mandibular mechanical vibrations are also investigated in severe obstructive sleep apnea patients during a whole night sleep study. The major findings of this study are that the genioglossus reflexively responds to the mechanical vibrations applied to the mandible and the sub-mandibular skin surface in healthy subjects during wakefulness and the sub-mandibular stimulations during sleep terminate the apnea earlier and decrease the level of hypoxia with smaller micro arousals

Invasive and non-invasive assessment of upper airway obstruction and respiratory effort with nasal airflow and esophageal pressure analysis during sleep

La estimación del esfuerzo respiratorio durante el sueño es de una importancia crítica para la identificación correcta de eventos respiratorios en los trastornos respiratorios del sueño (TRS), el diagnóstico correcto de las patologías relacionadas con los TRS y las decisiones sobre la terapia correspondiente. Hoy en día el esfuerzo respiratorio suele ser estimado mediante la polisomnografía (PSG) nocturna con técnicas imprecisas y mediante la evaluación manual por expertos humanos, lo cual es un proceso laborioso que conlleva limitaciones significativas y errores en la clasificación. El objetivo principal de esta tesis es la presentación de nuevos métodos para la estimación automático, invasiva y no-invasiva del esfuerzo respiratorio y cambios en la obstrucción de las vías aéreas superiores (VAS). En especial, la aplicación de estos métodos debería permitir, entre otras cosas, la diferenciación automática invasiva y no-invasiva de eventos centrales y obstructivos durante el sueño. Con este propósito se diseñó y se obtuvo una base de datos de PSG nocturna completamente nueva de 28 pacientes con medición sistemática de presión esofágica (Pes). La Pes está actualmente considerada como el gold-standard para la estimación del esfuerzo respiratorio y la identificación de eventos respiratorios en los TRS. Es sin embargo una técnica invasiva y altamente compleja, lo cual limita su uso en la rutina clínica. Esto refuerza el valor de nuestra base de datos y la dificultad que ha implicado su adquisición. Todos los métodos de procesado propuestos y desarrollados en esta tesis están consecuentemente validados con la señal gold-standard de Pes para asegurar su validez.En un primer paso, se presenta un sistema automático invasivo para la clasificación de limitaciones de flujo inspiratorio (LFI) en los ciclos inspiratorios. La LFI se ha definido como una falta de aumento en flujo respiratorio a pesar de un incremento en el esfuerzo respiratorio, lo cual suele resultar en un patrón de flujo respiratorio característico (flattening). Un total de 38,782 ciclos respiratorios fueron automáticamente extraídos y analizados. Se propone un modelo exponencial que reproduzca la relación entre Pes y flujo respiratorio de una inspiración y permita la estimación objetiva de cambios en la obstrucción de las VAS. La capacidad de caracterización del modelo se estima mediante tres parámetros de evaluación: el error medio cuadrado en la estimación de la resistencia en la presión pico, el coeficiente de determinación y la estimación de episodios de LFI. Los resultados del modelo son comparados a los de los dos mejores modelos en la literatura. Los resultados finales indican que el modelo exponencial caracteriza la LFI y estima los niveles de obstrucción de las VAS con la mayor exactitud y objetividad. Las anotaciones gold-standard de LFI obtenidas, fueron utilizadas para entrenar, testear y validar un nuevo clasificador automático y no-invasivo de LFI basa en la señal de flujo respiratorio nasal. Se utilizaron las técnicas de Discriminant Analysis, Support Vector Machines y Adaboost para la clasificación no-invasiva de inspiraciones con las características extraídas de los dominios temporales y espectrales de los patrones de flujo inspiratorios. Este nuevo clasificador automático no-invasivo también identificó exitosamente los episodios de LFI, alcanzando una sensibilidad de 0.87 y una especificidad de 0.85. La diferenciación entre eventos respiratorios centrales y obstructivos es una de las acciones más recurrentes en el diagnostico de los TRS. Sin embargo únicamente la medición de Pes permite la diferenciación gold-standard de este tipo de eventos. Recientemente se han propuesto nuevas técnicas para la diferenciación no-invasiva de apneas e hipopneas. Sin embargo su adopción ha sido lenta debido a su limitada validación clínica, ya que la creación manual por expertos humanos de sets gold-standard de validación representa un trabajo laborioso. En esta tesis se propone un nuevo sistema para la diferenciación gold-standard automática y objetiva entre hipopneas obstructivas y centrales. Expertos humanos clasificaron manualmente un total de 769 hypopneas en 28 pacientes para crear un set de validación gold-standard. Como siguiente paso se extrajeron características específicas de cada hipopnea para entrenar y testear clasificadores (Discriminant Analysis, Support Vector Machines y adaboost) para diferenciar entre hipopneas centrales y obstructivas mediante la señal gold-standard Pes. El sistema de diferenciación automática alcanzó resultados prometedores, obteniendo una sensibilidad, una especificad y una exactitud de 0.90. Por lo tanto este sistema parece prometedor para la diferenciación automática, gold-standard de hipopneas centrales y obstructivas. Finalmente se propone un sistema no-invasivo para la diferenciación automática de hipopneas centrales y obstructivas. Se propone utilizar la señal de flujo respiratorio para la diferenciación utilizando características de los ciclos inspiratorios de cada hipopnea, entre ellos los patrones flattening. Este sistema automático no-invasivo es una combinación de los sistemas anteriormente presentados y se valida mediante las anotaciones gold-standard obtenidas mediante la señal de Pes por expertos humanos. Los resultados de este sistema son comparados a los resultados obtenidos por expertos humanos que utilizaron un nuevo algoritmo no-invasivo para la diferenciación manual de hipopneas. Los resultados del sistema automático no-invasivo son prometedores y muestran la viabilidad de la metodología empleada. Una vez haya sido validado extensivamente, se ha propuesto este algoritmo para su utilización en dispositivos de terapia de TRS desarrollados por uno de los socios cooperantes en este proyecto.The assessment of respiratory effort during sleep is of major importance for the correct identification of respiratory events in sleep-disordered breathing (SDB), the correct diagnosis of SDB-related pathologies and the consequent choice of treatment. Currently, respiratory effort is usually assessed in night polysomnography (NPSG) with imprecise techniques and manually evaluated by human experts, resulting in a laborious task with significant limitations and missclassifications.The main objective of this thesis is to present new methods for the automatic, invasive and non-invasive assessment of respiratory effort and changes in upper airway (UA) obstruction. Specifically, the application of these methods should, in between others, allow the automatic invasive and non-invasive differentiation of obstructive and central respiratory events during sleep.For this purpose, a completely new NPSG database consisting of 28 patients with systematic esophageal pressure (Pes) measurement was acquired. Pes is currently considered the gold-standard to assess respiratory effort and identify respiratory events in SDB. However, the invasiveness and complexity of Pes measurement prevents its use in clinical routine, underlining the importance of this new database. . . All the processing methods developed in this thesis will consequently be validated with the gold-standard Pes-signal in order to ensure their clinical validity.In a first step, an (invasive) automatic system for the classification of inspiratory flow limitation (IFL) in the inspiratory cycles is presented.IFL has been defined as a lack of increase in airflow despite increasing respiratory effort, which normally results in a characteristic inspiratory airflow pattern (flattening). A total of 38,782 breaths were extracted and automatically analyzed. An exponential model is proposed to reproduce the relationship between Pes and airflow of an inspiration and achieve an objective assessment of changes in upper airway obstruction. The characterization performance of the model is appraised with three evaluation parameters: mean-squared-error when estimating resistance at peakpressure,coefficient of determination and assessment of IFL episodes. The model's results are compared to the two best-performing models in the literature. The results indicated that the exponential model characterizes IFL and assesses levels of upper airway obstruction with the highest accuracy and objectivity.The obtained gold-standard IFL annotations were then employed to train, test and validate a new automatic, non-invasive IFL classification system by means of the nasal airflow signal. Discriminant Analysis, Support Vector Machines and Adaboost algorithms were employed to objectively classify breaths non-invasively with features extracted from the time and frequency domains of the breaths' flow patterns. The new non-invasive automatic classification system also succeeded identifying IFL episodes, achieving a sensitivity of 0.87 and a specificity of 0.85.The differentiation between obstructive and central respiratory events is one of the most recurrent tasks in the diagnosis of sleep disordered breathing, but only Pes measurement allows the gold-standard differentiation of these events. Recently new techniques have been proposed to allow the non-invasive differentiation of hypopneas. However, their adoption has been slow due to their limited clinical validation, as the creation of manual, gold-standard validation sets by human experts is a cumbersome procedure. In this study, a new system is proposed for an objective automatic, gold-standard differentiation between obstructive and central hypopneas with the esophageal pressure signal. An overall of 769 hypopneas of 28 patients were manually scored by human experts to create a gold-standard validation set. Then, features were extracted from each hypopnea to train and test classifiers (Discriminant Analysis, Support Vector Machines and adaboost classifiers) to differentiate between central and obstructive hypopneas with the gold-standard esophageal pressure signal. The automatic differentiation system achieved promising results, with a sensitivity of 0.82, a specificity of 0.87 and an accuracy of 0.85. Hence, this system seems promising for an automatic, goldstandard differentiation between obstructive and central hypopneas.Finally, a non-invasive system is proposed for the automatic differentiation of central and obstructive hypopneas. Only the airflow signal is used for the differentiation, as features of the inspiratory cycles of the hypopnea, such as the flattening patterns, is used. The automatic, non-invasive system represents a combination of the systems that have been presented before and it was validated with the gold-standard scorings obtained with the Pes-signal by human experts. The outcome is compared to the results obtained by human scorers that applied a new non-invasive algorithm for the manual differentiation of hypopneas. The non-invasive system's results are promising and show the viability of this technique. Once validated, this algorithm has been proposed to be used in therapy devices developed by one of the partner institutions cooperating in this project

Review on biomedical sensors, technologies, and algorithms for diagnosis of sleep-disordered breathing: Comprehensive survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB