Improved Confidence Interval Estimation for Oscillometric Blood Pressure Measurement by Combining Bootstrap-After-Jackknife Function with Non-Gaussian Models

Confidence intervals (CIs) are generally not provided along with estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) measured using oscillometric blood pressure devices. No criteria exist to determine the CI from a small sample set of oscillometric blood pressure measurements. We provide an extended methodology to improve estimation of CIs of SBP and DBP based on a nonparametric bootstrap-after-jackknife function and a Bayesian approach. We use the nonparametric bootstrap-after-jackknife function to reduce maximum amplitude outliers. Improved pseudomaximum amplitudes (PMAs) and pseudoenvelopes (PEs) are derived from the pseudomeasurements. Moreover, the proposed algorithm uses an unfixed ratio obtained by employing non-Gaussian models based on the Bayesian technique to estimate the SBP and DBP ratios for individual subjects. The CIs obtained through our proposed approach are narrower than those obtained using the traditional Student t-distribution method. The mean difference (MD) and standard deviation (SD) of the SBP and DBP estimates using our proposed approach are better than the estimates obtained by conventional fixed ratios based on the PMA and PE (PMAE)

Enabling human physiological sensing by leveraging intelligent head-worn wearable systems

This thesis explores the challenges of enabling human physiological sensing by leveraging head-worn wearable computer systems. In particular, we want to answer a fundamental question, i.e., could we leverage head-worn wearables to enable accurate and socially-acceptable solutions to improve human healthcare and prevent life-threatening conditions in our daily lives? To that end, we will study the techniques that utilise the unique advantages of wearable computers to (1) facilitate new sensing capabilities to capture various biosignals from the brain, the eyes, facial muscles, sweat glands, and blood vessels, (2) address motion artefacts and environmental noise in real-time with signal processing algorithms and hardware design techniques, and (3) enable long-term, high-fidelity biosignal monitoring with efficient on-chip intelligence and pattern-driven compressive sensing algorithms. We first demonstrate the ability to capture the activities of the user's brain, eyes, facial muscles, and sweat glands by proposing WAKE, a novel behind-the-ear biosignal sensing wearable. By studying the human anatomy in the ear area, we propose a wearable design to capture brain waves (EEG), eye movements (EOG), facial muscle contractions (EMG), and sweat gland activities (EDA) with a minimal number of sensors. Furthermore, we introduce a Three-fold Cascaded Amplifying (3CA) technique and signal processing algorithms to tame the motion artefacts and environmental noises for capturing high-fidelity signals in real time. We devise a machine-learning model based on the captured signals to detect microsleep with a high temporal resolution. Second, we will discuss our work on developing an efficient Pattern-dRiven Compressive Sensing framework (PROS) to enable long-term biosignal monitoring on low-power wearables. The system introduces tiny on-chip pattern recognition primitives (TinyPR) and a novel pattern-driven compressive sensing technique (PDCS) that exploits the sparsity of biosignals. They provide the ability to capture high-fidelity biosignals with an ultra-low power footprint. This development will unlock long-term healthcare applications on wearable computers, such as epileptic seizure monitoring, microsleep detection, etc. These applications were previously impractical on energy and resource-constrained wearable computers due to the limited battery lifetime, slow response rate, and inadequate biosignal quality. Finally, we will further explore the possibility of capturing the activities of a blood vessel (i.e., superficial temporal artery) lying deep inside the user's ear using an ear-worn wearable computer. The captured optical pulse signals (PPG) are used to develop a frequent and comfortable blood pressure monitoring system called eBP. In contrast to existing devices, eBP introduces a novel in-ear wearable system design and algorithms to eliminate the need to block the blood flow inside the ear, alleviating the user's discomfort

Cuffless ambulatory blood pressure measurement using the photoplethysmogram and the electrocardiogram

Blood pressure (BP), as with other vital signs such as heart rate and respiratory rate, exhibits endogenous oscillations over a period of approximately 24 hours, a phenomenon known as circadian rhythmicity. This rhythm typically reaches a nadir during sleep, however, different BP circadian rhythm phenotypes exist depending on the magnitude and direction of the nocturnal change. Analysis of these phenotypes has been shown to be an independent indicator for the onset of cardiovascular disease, the leading cause of non-communicable mortality and morbidity worldwide. However, currently the established technique for monitoring BP over 24 hours in the general population requires an inflatable cuff wrapped around the upper arm. This procedure is highly disruptive to sleep and daily life, and therefore rarely performed in primary care. Although commercial cuffless BP devices do exist, their accuracy has been questioned, and consequently, the clinical community do not recommend their use. In this thesis, I investigated techniques to measure BP in an ambulatory environment without an inflatable cuff using two signals commonly acquired by wearable sensors: the photoplethysmogram (PPG) and the electrocardiogram (ECG). Given the diverse mechanisms by which the autonomic nervous system regulates BP, I developed methodologies using data from multiple individuals with BP perturbed by various, diverse, mechanisms. To identify surrogate measures of BP derived from the PPG and ECG signals, I designed a clinical study in which significant BP changes were induced through a pharmacological intervention in thirty healthy volunteers. Using data from this study, I established that changes in the pulse arrival time (PAT, the time delay between fiducial points on the ECG and PPG waveforms) and morphological features of the PPG waveform could provide reliable cuffless indicators for changes in BP. Even at rest, however, these signals are confounded by factors such as the pre-ejection period (PEP) and signal measurement noise. Additionally, accurate absolute measurements of BP required calibration using a reference BP device. Subsequently, I conducted a circadian analysis of these surrogate measures of BP using a large cohort of 1,508 patients during the 24-hour period prior to their discharge from an intensive care unit. Through this circadian analysis I suggest that PAT and a subset of features from the PPG waveform exhibit a phenotypically modified circadian rhythm in synchronicity with that of BP. Additionally, I designed a novel ordinal classification algorithm, which utilised circadian features of these signals, in order to identify BP circadian rhythm profiles in a calibration-free manner. This method may provide a cost-effective initial assessment of BP phenotypes in the general population. Notably, estimating absolute BP values using PPG and ECG signals in the ICU resulted in clinically significant mean absolute errors of 9.26 (5.01) mmHg. Finally, I designed a clinical study to extend the work towards cuffless ambulatory BP estimation in a cohort of fifteen healthy volunteers. Hybrid calibration strategies (where model personalisation was handled by user demographics, commonly utilised by commercial cuffless devices) led to clinically significant errors when estimating absolute values of BP, mean absolute error = 9.62 (19.73) mmHg. For the majority of individuals, a more appropriate estimation of BP values was achieved through an individual calibration strategy whereby idiosyncratic models were trained on personalised data, mean absolute error = 5.45 (6.40) mmHg. However, for a handful of individuals, notable estimation errors (>10 mmHg) still persisted using this strategy largely as a result of motion artifacts, inherent intra- and inter-individual variability in PPG features, and inadequate training data. Overall, I suggest that while beat-by-beat measurements of BP can be obtained using PPG and ECG signals, their accuracy is significantly limited in an ambulatory environment. This limitation, combined with the impracticality of individual calibration (due to the low tolerance for ABPM), suggest that cuffless ambulatory blood pressure measurement using the PPG and ECG signals may be infeasible. Nevertheless, macro assessments of cardiovascular health, such as an individual's BP phenotype, may be comparatively more accurately predicted using these signals with the potential to be recorded without calibration. Through further research on the relationship between the circadian rhythms of BP and the PPG and ECG waveforms, it is promising that these signals may be able to assist in detecting deterioration in cardiovascular health in the general population

Analysis of Dietary Patterns in Relation to Cardiovascular Risk Factors

Background: Compared to the examination of food and nutrient intakes, the identification of dietary patterns provides an overall picture of our diet considering combinations foods and meals, and contextualizing diet in relation to other lifestyle factors. Due to the inherent heterogeneity in people’s eating preferences, accurate characterization of dietary patterns remains a challenge. Efficient strategies to reduce complex and multidimensional nutritional data into meaningful dietary patterns are needed, in particular to study the association between diet and chronic disease. Aims and objectives: The overall aim of this work is to identify dietary patters and their association with prevalent cardiometabolic risk factors in different populations. For this, two objectives were defined: 1) To apply “a-priori” and “a-posteriori” dietary and meal pattern analyses to characterise the nutritional quality of the diet, 2) To quantity the impact of the adherence to distinctive dietary patterns, the type and frequency of meals, eating occasions, and synergistic associations of multiple lifestyles on traditional cardio-metabolic biomarkers, and particularly the presence and extent of subclinical atherosclerosis in asymptomatic adults. Materials and methods: Analyses were carried out using baseline data from two observational prospective cohorts and a National-representative survey. The Aragon Workers Health Study (AWHS) and the Progression of Early Subclinical Atherosclerosis (PESA) study, both involved populations of middle aged men and women and aimed to assess the determinants of subclinical atherosclerosis progression. The National Adult Nutrition Survey (NANS), measured habitual food and beverage consumption, lifestyle and health status among a National representative sample of adults living in the republic of Ireland. Results: “A-posteriori” (Principal Components Analysis, Cluster Analysis and Latent Class Analysis), “a-priori” (adherence to alternate Mediterranean Diet Index) dietary patterns analyses, and quantification of dietary intakes at specific eating occasions (breakfast) were used to describe diet. A Western Dietary Pattern (WDP), characterised by higher intakes of red meat, fast food, dairy and cereals, was associated with lower high-density lipoprotein cholesterol (HDL-c) and apolipoprotein A1 levels. A Mediterranean Dietary Pattern (MDP), characterised by higher intakes of vegetables, fruits, fish, white meat, nuts and olive oil was observed to be related to a more favourable plasma lipid profile and was significantly associated with lower prevalence of plaques in femoral arteries independently of the presence of other conventional risk factors. Higher adherence to MDP combined with non-smoking, and moderate alcohol consumption resulted in a further reduction in the risk of subclinical atherosclerosis. A Social-Business eating pattern, characterised by high consumption of red meat, pre-made foods, snacks, alcohol, and sugar sweetened beverages, frequent eating out behaviour was associated with a worse CVD risk profile and significantly higher prevalence and extension of subclinical atherosclerosis. Dietary habits significantly differed on weekends. Thus those participants who preferred meat and eggs for breakfast rather than having a cereal, and skipped light meal later during the day, were more likely to follow unhealthy overall dietary pattern, have higher diastolic blood pressure and increased serum ferritin. Moreover, skipping breakfast is not only a marker of overall unhealthy dietary pattern and lifestyle, it is also significantly associated with increased prevalence of non-coronary and generalized atherosclerosis. On the other hand regular breakfast consumption was associated with higher overall dietary quality. Conclusion: “A-posteriori” and “a-priori” analyses of dietary patterns are useful techniques to characterise the dietary habits commonly followed within a given population and their relationship with CVD markers. In combination with the investigation of daily meal consumption and at specific eating occasions, this approach could lead to improved public health guidelines and recommendations to improve diet, and overall lifestyle and curb the increasing burden of CVD