Obstructive sleep apnoea/hypopnoea syndrome (OSAHS) is characterised by repeated cycles of upper airway obstruction during sleep, leading to diurnal symptoms. Individuals with Down syndrome (DS) are predisposed to this as the DS phenotype overlaps with OSAHS risk factors. Around 2-4% of the general adult population and 55% of children with DS have OSAHS but, to date, no large-scale study has assessed OSAHS prevalence or efficacy of treatment in DS adults. This study aimed to: 1) Systematically assess subjective and objective OSAHS prevalence; 2) Assess the effectiveness of continuous positive airway pressure (CPAP) in an adult DS population. Standard questionnaires including pictorial Epworth Sleepiness Scale (pESS) and Developmental Behaviour Checklist for Adults (DBC-A) were sent to UK adults aged ≥16yr with DS and their caregivers. All questionnaire responders were invited to undergo home polygraphy. Symptomatic adults with DS with ≥10 apnoeas/hypopnoeas per hour in bed (AH) on home polygraphy were invited to participate in a prospective randomised controlled trial (RCT) of CPAP v. lifestyle advice, with review at 1, 3, 6 and 12m. Participants in the lifestyle arm were offered CPAP at 1m. Standard measurements of sleepiness, behaviour, cognitive function and general health were undertaken. Standard statistical analyses were conducted, with significance set at p < 0.001 to control for multiple testing. Of 5270 questionnaires sent, 1105 responses were valid (21%). Responders (55% males) were overweight/obese young adults: mean BMI 29.0±6.8kg/m2; mean age 28±9 years. Women had a higher BMI (p < 0.0001), but collar size was greater in men (p < 0.0001). Mean pESS scores were broadly within the normal range (7±5/24). No significant gender differences in OSAHS symptoms were noted. Individuals with probable OSAHS had higher pESS and DBC-A scores, and significantly more symptoms of OSAHS. Subjective OSAHS prevalence was estimated at 35%. Of the 790 individuals invited, 149 underwent polygraphy, with 134 valid studies obtained: mean AH 21.8(10.9-42.7); mean oximetry desaturation index (ODI) 6.6(2.3-20.0). No significant gender differences were observed. Forty-two percent of participants met standard clinical diagnostic criteria for OSAHS. Twenty-eight eligible adults with DS (19 male) were randomised: age 28±9yr; BMI 31.5±7.9kg/m2; AH 28.6(14.8-47.9); ODI 7.3(1.8-21.9); pESS 11±6/24. Groups did not differ significantly at baseline. By 12m, 4 participants had withdrawn (all remaining participants on CPAP). The pESS (p=0.001), DBC-A Disruptive (p < 0.0001) and Kaufmann Brief Intelligence Test verbal subscale (p=0.001) scores improved significantly. This first large study of OSAHS prevalence in the adult DS population estimates a prevalence of 35-42% - around 10 times higher than in the general adult population. Sustained, significant improvements in sleepiness, cognitive function and behavioural/emotional outcomes with CPAP use over a 12m period were demonstrated during this first RCT of CPAP in adults with DS. A larger trial of CPAP in this population is warranted.

Introduction: Obstructive sleep apnoea (OSA) is common and is caused by repetitive obstruction of the upper airway during sleep. OSA is associated with increased cardiovascular morbidity and mortality and is an independent risk factor for hypertension. The immediate physiological effects of OSA include intermittent hypoxia, repeated arousal from sleep and intra-thoracic pressure swings. The resulting activation of the sympathetic nervous system, systemic inflammation and oxidative stress may result in increased arterial stiffness and endothelial dysfunction, potentially explaining any causal link between OSA and cardiovascular disease (CVD). Continuous positive airway pressure (CPAP) therapy improves excessive daytime sleepiness (EDS) and in non-randomised studies, reduces cardiovascular mortality. Prior to starting this study, there was a limited amount of evidence suggesting that CPAP therapy improved arterial stiffness and endothelial function, but the effects in subjects without pre-existing CVD were unclear. Aims: i) to determine whether CPAP therapy has an effect upon measures of arterial stiffness and endothelial function in patients with OSA, in the absence of known CVD. ii) To compare arterial stiffness and endothelial function in a subset of patients with OSAHS (defined as OSA and EDS), with a group of well-matched control subjects. Methods: Fifty three patients with OSA, defined as an apnoea/hypopnoea index of ≥15, and without known CVD, entered a double-blind placebo-controlled crossover trial of 12 weeks CPAP therapy, of whom forty three completed the study protocol. Sham CPAP was used in the placebo arm of the study and vascular assessments were made at baseline and after each arm of the study. Arterial stiffness was determined by measuring aortic distensibility using cardiovascular magnetic resonance imaging and by measuring the augmentation index (AIx) and aortic pulse wave velocity (PWV) by applanation tonometry. Endothelial function was assessed non-invasively by measuring vascular reactivity after administration of salbutamol and glyceryl trinitrate. In a subset of twenty patients with OSAHS, arterial stiffness and endothelial function at baseline were compared to readings obtained from healthy control subjects, matched on a one-to-one basis for age, sex and BMI. Results: Patients with OSAHS (n=20) had increased arterial stiffness [AIx 19.3(10.9) vs. 12.6(10.2) %; p=0.017] and impaired endothelial function, measured as the change in AIx following salbutamol [-4.3(3.2) vs. -8.0(4.9) %; p=0.02] compared to controls. Twelve weeks of CPAP therapy had no significant effect upon any measure of arterial stiffness or endothelial function in patients with OSA (n=43). A trend towards a reduction in AIx following CPAP therapy was seen, but this was non-significant. There was a reduction in systolic blood pressure following CPAP therapy [126(12) vs. 129(14) mmHg]. Sub group analysis showed CPAP to have no effect on arterial stiffness or endothelial function in patients with EDS or in patients using CPAP for ≥4 hours per night. Conclusions: This study demonstrates that even in the absence of known CVD, patients with OSAHS have evidence of increased arterial stiffness and impaired endothelial function. However, in patients with OSA, free from CVD, CPAP therapy did not lead to an improvement in any measure of arterial stiffness or endothelial function after 12 weeks.

Obstructive sleep apnoea hypopnoea syndrome (OSAHS) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The reference standard of clinical diagnosis, called Polysomnography (PSG), requires a full-night hospital stay connected to over 15 measuring channels requiring physical contact with sensors. The vast quantity of physiological data acquired during the PSG has to be manually scored by a qualified technologist to assess the presence or absence of the decease. The PSG is inconvenient, time consuming, expensive and unsuited for community screening. The limited PSG facilities around the world have resulted in long waiting lists and a large fraction of patients remain undiagnosed at present. There has been a flurry of recent activities in developing a portable technology to resolve this need. All the devices have at least one sensor that requires physical contact with the subject. Unattended systems have not led to sufficiently high sensitivity/specificity levels to be used in a routine home monitoring or a community screening exercise. OSAHS is a sleep respiratory disorder principally caused by functional deficiencies occurring in the upper airways during sleep. These conditions and the reduced muscle tone during sleep, cause the muscles in the upper airways to collapse partially or completely thus resulting in episodes of hypopnoea and apnoea respectively. During the process leading to collapse of upper airways, upper airways act as an acoustic filter frequently producing snoring sounds. The process of snore sound production leads us to hypothesise that snore sounds should contain information on changes occurring in the upper airways during the OSAHS. Snoring almost always accompanies the OSAHS and is universally recognised as its earliest symptom. At present, however, the quantitative analysis of snore sounds is not a practice in clinical OSAHS detection. The vast potential of snoring in the diagnosis/screening of the OSAHS remains unused. Snoring-based technology opens up opportunities for building community-screening devices that do not depend on contact instrumentation. In this thesis, we present our work towards developing a snore–based non-contact instrumentation for the diagnosis/screening of the OSAHS. The primary task in the analysis of Snore Related Sounds (SRS) would be to segment the SRS data as accurately as possible into three main classes, snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. A new algorithm was developed, based on pattern recognition for the SRS segmentation. Four features derived from the SRS were considered to classify samples of the SRS into three classes. We also investigated the performance of the algorithm with three commonly-used noise reduction (NR) techniques in speech processing, Amplitude Spectral Subtraction (ASS), Power Spectral Subtraction (PSS) and Short Time Spectral Amplitude (STSA) Estimation. It was found that the noise reduction, together with a proper choice of features, could improve the classification accuracy to 96.78%. A novel model for the SRS was proposed for the response of a mixed-phase system (total airways response, TAR) to a source excitation at the input. The TAR/source model is similar to the vocal tract/source model in speech synthesis and is capable of capturing the acoustical changes brought about by the collapsing upper airways in the OSAHS. An algorithm was developed, based on the higher-order-spectra (HOS) to jointly estimate the source and the TAR, preserving the true phase characteristics of the latter. Working on a clinical database of signals, we show that the TAR is indeed a mixed phased signal and second-order statistics cannot fully characterise it. Nocturnal speech sounds can corrupt snore recordings and pose a challenge to the snore-based OSAHS diagnosis. The TAR could be shown to detect speech segments embedded in snores and derive features to diagnose the OSAHS. Finally presented is a novel technique for diagnosing the OSAHS, based solely on multi-parametric snore sound analysis. The method comprises a logistic regression model fed with a range of snore parameters derived from its features — the pitch and Total Airways Response (TAR) estimated using a Higher Order Statistics (HOS) based algorithm. The model was developed and its performance validated on a clinical database consisting of overnight snoring sounds simultaneously recorded during a hospital PSG using a high fidelity sound recording setup. The K-fold cross validation technique was used for validating the model. The validation process achieved an 89.3% sensitivity with 92.3% specificity (the area under the Receiver Operating Characteristic (ROC) curve was 0.96) in classifying the data sets into the two groups, the OSAHS (AHI >10) and the non-OSAHS. These results are superior to the existing results and unequivocally illustrate the feasibility of developing a snore-based non-contact OSAHS screening device.

Obstructive sleep apnoea hypopnoea syndrome (OSAHS) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The reference standard of clinical diagnosis, called Polysomnography (PSG), requires a full-night hospital stay connected to over 15 measuring channels requiring physical contact with sensors. The vast quantity of physiological data acquired during the PSG has to be manually scored by a qualified technologist to assess the presence or absence of the decease. The PSG is inconvenient, time consuming, expensive and unsuited for community screening. The limited PSG facilities around the world have resulted in long waiting lists and a large fraction of patients remain undiagnosed at present. There has been a flurry of recent activities in developing a portable technology to resolve this need. All the devices have at least one sensor that requires physical contact with the subject. Unattended systems have not led to sufficiently high sensitivity/specificity levels to be used in a routine home monitoring or a community screening exercise. OSAHS is a sleep respiratory disorder principally caused by functional deficiencies occurring in the upper airways during sleep. These conditions and the reduced muscle tone during sleep, cause the muscles in the upper airways to collapse partially or completely thus resulting in episodes of hypopnoea and apnoea respectively. During the process leading to collapse of upper airways, upper airways act as an acoustic filter frequently producing snoring sounds. The process of snore sound production leads us to hypothesise that snore sounds should contain information on changes occurring in the upper airways during the OSAHS. Snoring almost always accompanies the OSAHS and is universally recognised as its earliest symptom. At present, however, the quantitative analysis of snore sounds is not a practice in clinical OSAHS detection. The vast potential of snoring in the diagnosis/screening of the OSAHS remains unused. Snoring-based technology opens up opportunities for building community-screening devices that do not depend on contact instrumentation. In this thesis, we present our work towards developing a snore–based non-contact instrumentation for the diagnosis/screening of the OSAHS. The primary task in the analysis of Snore Related Sounds (SRS) would be to segment the SRS data as accurately as possible into three main classes, snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. A new algorithm was developed, based on pattern recognition for the SRS segmentation. Four features derived from the SRS were considered to classify samples of the SRS into three classes. We also investigated the performance of the algorithm with three commonly-used noise reduction (NR) techniques in speech processing, Amplitude Spectral Subtraction (ASS), Power Spectral Subtraction (PSS) and Short Time Spectral Amplitude (STSA) Estimation. It was found that the noise reduction, together with a proper choice of features, could improve the classification accuracy to 96.78%. A novel model for the SRS was proposed for the response of a mixed-phase system (total airways response, TAR) to a source excitation at the input. The TAR/source model is similar to the vocal tract/source model in speech synthesis and is capable of capturing the acoustical changes brought about by the collapsing upper airways in the OSAHS. An algorithm was developed, based on the higher-order-spectra (HOS) to jointly estimate the source and the TAR, preserving the true phase characteristics of the latter. Working on a clinical database of signals, we show that the TAR is indeed a mixed phased signal and second-order statistics cannot fully characterise it. Nocturnal speech sounds can corrupt snore recordings and pose a challenge to the snore-based OSAHS diagnosis. The TAR could be shown to detect speech segments embedded in snores and derive features to diagnose the OSAHS. Finally presented is a novel technique for diagnosing the OSAHS, based solely on multi-parametric snore sound analysis. The method comprises a logistic regression model fed with a range of snore parameters derived from its features — the pitch and Total Airways Response (TAR) estimated using a Higher Order Statistics (HOS) based algorithm. The model was developed and its performance validated on a clinical database consisting of overnight snoring sounds simultaneously recorded during a hospital PSG using a high fidelity sound recording setup. The K-fold cross validation technique was used for validating the model. The validation process achieved an 89.3% sensitivity with 92.3% specificity (the area under the Receiver Operating Characteristic (ROC) curve was 0.96) in classifying the data sets into the two groups, the OSAHS (AHI >10) and the non-OSAHS. These results are superior to the existing results and unequivocally illustrate the feasibility of developing a snore-based non-contact OSAHS screening device.