The Use of Respiratory Effort Improves an ECG-Based Deep Learning Algorithm to Assess Sleep-Disordered Breathing

BACKGROUND: Sleep apnea is a prevalent sleep-disordered breathing (SDB) condition that affects a large population worldwide. Research has demonstrated the potential of using electrocardiographic (ECG) signals (heart rate and ECG-derived respiration, EDR) to detect SDB. However, EDR may be a suboptimal replacement for respiration signals.METHODS: We evaluated a previously described ECG-based deep learning algorithm in an independent dataset including 198 patients and compared performance for SDB event detection using thoracic respiratory effort versus EDR. We also evaluated the algorithm in terms of apnea-hypopnea index (AHI) estimation performance, and SDB severity classification based on the estimated AHI.RESULTS: Using respiratory effort instead of EDR, we achieved an improved performance in SDB event detection (F1 score = 0.708), AHI estimation (Spearman's correlation = 0.922), and SDB severity classification (Cohen's kappa of 0.62 was obtained based on AHI).CONCLUSION: Respiratory effort is superior to EDR to assess SDB. Using respiratory effort and ECG, the previously described algorithm achieves good performance in a new dataset from an independent laboratory confirming its adequacy for this task.</p

Combining wearables and nearables for patient state analysis

Recently, ambient patient monitoring using wearable and nearable sensors is becoming more prevalent, especially in the neurodegenerative (Rett syndrome) and sleep disorder (Obstructive sleep apnea) populations. While wearables capture localized physiological data such as pulse rate, wrist acceleration and brain signals, nearables record global passive data including body movements, ambient sound and environmental variables. Together, wearables and nearables provide a more comprehensive understanding of the patient state. The processing of data captured from wearables and nearables have multiple challenges including handling missing data, time synchronization between sensors and developing data fusion techniques for multimodal analysis. The research described in this thesis addresses these issues while working on data captured in the wild. First, we describe a Rett syndrome severity estimator using a wearable biosensor and uncover physio-motor biomarkers. Second, we present the applications of an edge computing and ambient data capture system for home and clinical environments. Finally, we describe a transfer learning and multimodal data fusion based sleep-wake detector for a mixed-disorder elderly population. We show that combining data from wearables and nearables improves the performance of sleep-wake detection in terms of the F1-score and the Cohen’s kappa compared to the unimodal models.Ph.D

Enhancing the Diagnosis and Management of Obstructive Sleep Apnoea in Atrial Fibrillation Patients

Background: Atrial fibrillation (AF), is the most common sustained cardiac arrhythmia, and significantly increases the risk of stroke and cardiovascular mortality. It is strongly associated with obstructive sleep apnoea (OSA). Aims: 1. Examine the epidemiology of OSA in a hospital cohort with AF. 2. Compare the diagnostic accuracy of clinical screening tools for OSA in patients with AF. 3. Compare cardiac autonomic function in AF patients with and without OSA. 4. Conduct a pilot study of mandibular advancement splint (MAS) therapy for OSA in AF patients. Methods: 107 AF patients were recruited. The diagnostic accuracy of screening tools including a level 3 (portable) sleep study device as compared to polysomnography in AF patients was assessed. Cardiac autonomic function as a potential mechanistic link between OSA and AF was assessed using Heart Rate Variability (HRV). A pilot study of OSA treatment in AF patients using MAS therapy was conducted. Results: 62.6% of patients were newly diagnosed with OSA. Patients with moderate to severe OSA showed an increased BMI, neck circumference and Mallampati score, but were not significantly different in terms of daytime somnolence. Oxygen desaturation index (ODI) derived from a Level 3 portable sleep study device performed best for the diagnosis of moderate to severe and severe OSA, with excellent diagnostic accuracy (AUC 0.899, 95% CI 0.838 – 0.960 and AUC 0.925, 95% CI 0.859 – 0.991 respectively). We found a chronic increase in parasympathetic nervous activity in paroxysmal AF patients with OSA. MAS therapy showed high rates of acceptance, compliance and efficacy in AF patients. Conclusions: This thesis contributes to our understanding of the association between AF and OSA across a spectrum o

Sleep-disordered breathing in patients with implanted cardiac devices: validation of the ApneaScanTM algorithm and implications for prognosis

Aims Sleep-disordered breathing (SDB) is common in heart failure (HF) and frequently undiagnosed. The ApneaScanTM algorithm, available on certain ICD and CRT devices, uses changes in transthoracic impedance with breathing to quantify SDB. This research tests 3 hypotheses: 1) The ApneaScanTM algorithm can accurately detect moderate-to-severe SDB in patients with HF 2) There is minimal night-to-night variability in the ApneaScanTM-determined severity of SDB 3) Those with moderate-to-severe SDB, assessed by ApneaScanTM, have a higher rate of adverse cardiovascular events than those without. Methods Patients with EF≤40% and ICD or CRT devices incorporating ApneaScanTM were recruited. For hypothesis 1, 54 subjects underwent a successful sleep polygraphy study and simultaneous download of ApneaScanTM data. 22 subjects (44%) had undiagnosed moderate-to-severe SDB. The area under the ROC curve was 0.84 for the diagnosis of moderate-to-severe SDB. The optimal ApneaScan cut-off was 30.5/hour (sensitivity 95%, specificity 69%, positive predictive value 68%, negative predictive value 95%). For hypothesis 2, ApneaScanTM data over 28- and 92-nights in 35 patients was reviewed. There was minimal variability in SDB and no significant difference between durations. For hypothesis 3, 72 patients were followed up at a median of 532 (IQR 386-736) days.Mean event-free survival was 660±344 days (95% CI 535-785 days) in the insignificant SDB group and 854±413 days (95% CI 730-978 days) in the significant SDB group (p=0.25 by log rank test). Conclusions ApneaScanTM, with an optimal cut-off of 30.5 events/hour, is a sensitive means of screening for SDB in patients with HF with a high negative predictive value. Readings above 30.5/hour require further investigation with a sleep study. Night-to-night variability in SDB is minimal and repeat sleep studies should be reserved for those with ‘borderline’ AHI. In this cohort, the presence of SDB was not associated with adverse cardiovascular outcomes. Recruitment is on-going to test this further.Open Acces

The diagnosis and treatment of sleep disordered breathing in patients with cardiovascular disease in England: current pathways and barriers to optimal care

Cardiovascular disease (CVD) is a major health burden accounting for more than 30% of deaths worldwide, but there have been significant advances in its management in recent years. These have been adopted into clinical practice guidelines, however, there is a mismatch between the widely perceived ‘best practice’ and how patients are actually managed in clinical practice. In most healthcare systems, the delivery of care is not standardised. Sleep disordered breathing (SDB) is highly prevalent in patients with CVD and can further potentiate their cardiovascular risk and lead to adverse cardiovascular mortality. A literature review of the association between cardiovascular disease and SDB will be evaluated in relation to pathophysiology, screening, diagnosis and treatment in this thesis. The current evidence for the management of SDB in CVD will also be reviewed. SDB has been traditionally considered as a discipline in respiratory medicine, therefore there are diagnosis and treatment challenges and most patients with SDB are undiagnosed and untreated. Patients with both CVD and SDB are likely to have multiple comorbidities requiring complex management strategies. Thus, the main aim of this thesis is to identify these practice barriers to diagnosis and treatment in patients with SDB and CVD, using both quantitative and qualitative methodology. Publicly available data sources related to SDB (such as Hospital Episode Statistics [HES data] and NHS RightCare), were used help understand the variation in service provision and diagnostic rates. To identify the barriers to diagnosis and treatment of patients with SDB and CVD, mixed-methods were used (i.e. both quantitative and qualitative methodology). For primary care, previously conducted GP and patient surveys were analysed and semi-structured interviews of healthcare professionals were carried out to identify barriers in secondary and tertiary care. In the past two decades, large number of QI tools have been widely in the management of cardiovascular disease with aim of overcoming barriers, however, we do not know whether they change cardiovascular outcome. Thus, a secondary aim of this thesis is to identify effective QI methodology and utilise them to improve and redesign local practice. A systematic review (of randomised/cluster controlled trials) was also carried out with the aim of exploring the impact of QI methodology on CVD outcome. Although the current evidence suggests that treating patients with SDB using PAP therapy may not have strong benefits as previously thought, the diagnosis of SDB is still important in patients with CVD because it reflects a group with higher CV risk. There are a variety of barriers that could delay the diagnosis and treatment of SDB, such as the lack of local access to sleep studies, lack of guidelines and hard outcome data, patient perceptions and cultural barriers between HPs. QI methods can be used to potentially overcome these barriers and care pathways seems to be the most effective.Open Acces

Gender Differences in Obstructive Sleep Apnea

The overall aim of this thesis was to understand gender differences in obstructive sleep apnea (OSA) and use this information to develop a tailored therapy for female patients. Specific aims were to determine whether gender differences commonly reported in the literature are present in mild OSA and upper airway resistance syndrome (UARS) patient groups, and whether symptoms could be linked to respiratory parameters in these groups. The final aim was to develop, test and validate a new AutoSet treatment for female OSA patients. CHAPTER 1 of this thesis provides a detailed review of gender differences in the prevalence, symptoms, clinical experience, and health outcomes of OSA and UARS patients, with a focus on the implications of different scoring rules. CHAPTER 2 reviews of quality of life questionnaires from 259 untreated patients with mild OSA. Females reported statistically significantly higher levels of sleepiness, fatigue, insomnia, and anxiety/depression compared to males. CHAPTER 3 of this thesis reviews polygraphy data from patients with mild OSA. Male patients were found to have significantly more breathing disturbances than females, however many of these difference disappeared when updated scoring criteria were used. Some weak correlations were found between respiratory parameters and symptoms; however, no clear conclusions could be drawn. CHAPTER 4 outlines the development of a new AutoSet device designed for female- specific breathing patterns. The remaining chapters (CHAPTER 5, and CHAPTER 6) of this thesis describe the testing and validation activities undertaken on the AutoSet F, including a clinical trial to test efficacy; a bench test to compare performance against other commercially available devices; a controlled product launch to validate the features of the algorithm; and finally a clinical trial which demonstrated improvements in sleep efficacy and quality of life over a three-month usage period. In summary, this thesis has shown that at the mild end of the OSA spectrum females are more symptomatic than males, even though respiratory differences in the genders are less pronounced than those described in moderate-to-severe patients. An AutoSet designed specifically for female OSA patients was successful in demonstrating efficacy and clinical effectiveness

Statistical Models for Detecting Existence of Obstructive Sleep Apnea, Predicting Its Severity, and Forecasting Future Episodes

This dissertation presents three statistical models based on data mining and nonlinear time-series analysis techniques as an alternative method for the diagnosis and treatment of obstructive sleep apnea disease (OSA). From a diagnosis perspective, our method reduces the time and cost associated with the conventional method by first screening a non-OSA subject from the population, then individually determining the OSA�s severity by utilizing the data from a single-lead electrocardiogram (ECG) device that is worn overnight at the subject�s location. Our OSA forecasting model can be used to activate an OSA therapy device such as a continuous positive airway pressure (CPAP) machine or a hypoglossal nerve stimulator (HNS) as needed or before an OSA episode so that the latter can be averted in real time.In particular, our contributions are: 1) Detect the existence of OSA in an individual based on the pattern of biological physiology and simple clinical data with a low false negative rate and reasonable accuracy (FNR: 5.3%, Accuracy: 84.47%). People with some degree of probability of having OSA will be confirmed by the next model. 2) Determine the OSA severity by classifying the OSA episode (event) from one-lead ECG data collected overnight (accuracy: 92.26% with 10,052 equally sampled events from 24 subjects). The advantage of our model is that the variations (i.e., different body build, age, gender, activity, health conditions, and race) have very little effect on the prediction because the neighboring patterns in the reconstructed phase spaces have very little or no correlation to those variations. This benefit can be seen from our model�s performance compared to two other models that exist in the literature. 3) Forecast an incoming OSA episode in real time using the one-lead ECG data (accuracy: 92%, 88%, and 87% for 1, 5, and 10 minutes ahead). This forecasting model with any appropriate OSA episode prevention device (i.e., HNS, and just-in-time CPAP) will allow for an effective OSA treatment method for CPAP nonadherence OSA sufferers. 4) Develop a wearable device that can collect the biological data via a single-lead ECG as a home sleep test (HST) device.Industrial Engineering & Managemen

Investigate the Prevalence and Association of Metabolic Syndrome and Its Components in Patients with OSA and Without OSA: A Hospital Based Cross Sectional Study

INTRODUCTION: The combination of obstructive sleep apnea and metabolic syndrome has been termed as syndrome z. Sleep related breathing disorders and metabolic syndrome are on increasing trend because of epidemic of obesity. Beyond their epidemiologic relationship, growing evidence suggests that OSA may be causally related to metabolic syndrome. AIMS AND OBJECTIVES: To estimate the prevalence of metabolic syndrome in patients with OSA To estimate the association of metabolic syndrome and its components in patients with and without OSA. MATERIALS AND METHODS: Hospital based prospective cross sectional analytical study. OBSERVATION: In this study prevalence of metabolic syndrome was 57.4 percent among OSA group in south Indian hospital based population and 34.7 percent in the patients without OSA. In our study prevalevalence of metabolic syndrome was higher in the patients with severe osa than mild and moderate osa. We compared various variables of the metabolic syndrome independently with obstructive sleep apneA. of the various variables of metabolic syndrome like fasting blood glucose, blood pressure, HDL cholesterol and abdominal circumference diastolic blood pressure and HDL cholesterol shows significant association with metabolic syndrome with p value of less than 0.005. CONCLUSION: The clinical implications are that there is a high prevalence of metabolic syndrome in patients presenting to sleep clinics with symptoms suggestive of Obstructive sleep apnea, irrespective of whether they have obstructive sleep apnea or not. The prevalence of MS is even higher if they have obstructive sleep apnea