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

New methods for continuous non-invasive blood pressure measurement

Hlavním cílem této práce je nalezení nové metodiky pro měření kontinuálního neinvazivního krevního tlaku na základě rychlosti šíření pulzní vlny v krevním řečišti. Práce se opírá o rešerši zabývající se základním modelem pro stanovení kontinuálního neinvazivního krevního tlaku na základě měření zpoždění pulzní vlny a jeho rozšířením. Z informací získaných z rešerše se upravila metodika měření doby zpoždění pulzní vlny / rychlosti šíření pulzní vlny, aby bylo možné docílit přesnějších výsledků a omezit tak lidský faktor, který způsobuje významnou nepřesnost vlivem nedokonalého rozmístění senzorů. Rešerše se rovněž podrobně zabývá modely pro stanovení kontinuálního neinvazivního krevního tlaku a jejich úprav zajištujících zvýšení přesnosti. Mezi úpravy modelů zejména patří vstupní parametry popisující krevní oběh - systémový cévní odpor, elasticita cév, tuhost cév. Práce se taky zabývá úpravami stávajícího modelu krevního řečiště pro bližší přizpůsobení fyzického modelu k reálnému cévnímu systému lidského těla. Mezi tyto úpravy patří i funkce baroreflexu či simulace různé tvrdosti stěny umělých cévních segmentů. Protože se jedná o simulační model krevního řečiště, důležitým krokem je také měření tlakové a objemové pulzní vlny, kde není možné využít konvenční senzory pro fotopletysmografii kvůli absenci částic pohlcující světlo. Na základě experimentálního měření pro různé nastavení modelu krevního řečiště bylo provedeno měření pulzní vlny pomocí tlakových a kapacitních senzorů s následným zpracováním měřených signálů a detekcí příznaků charakterizující pulzní vlnu. Na základě příznaku byly stanoveny predikční regresní modely, které vykazovaly dostatečnou přesnost jejich určení, a tak následovaly dvě metody pro získání parametru o tvrdosti cévní stěny na základě měřitelných parametrů. První metodou byl predikční regresní model, který vykazoval přesnost 74,1 % a druhou metodou byl adaptivní neuro-fuzzy inferenční systém, který vykazoval přesnost 98,7 %. Tyto stanovení rychlosti pulzní vlny bylo ověřeno dalším přímým měřením pulzní vlny a výsledky byly srovnány. Výsledkem disertační práce je určení rychlosti šíření pulzní vlny s využitím pouze jednoho pletysmografického senzoru bez nutnosti měření na dvou různých místech s přesným měřením vzdálenosti a možnosti aplikace v klinické praxi.The main objective of this work is to find a new methodology for measuring continuous non-invasive blood pressure based on the pulse wave velocity in the vascular system. The work is based on the literature research of the basic model for the determination of non-invasive continuous blood pressure based on the measurement of pulse transit time. From the information obtained from the review, the methodology of measuring the pulse transit time/pulse wave velocity was modified in order to achieve more accurate results and to reduce the human factor that causes significant inaccuracy due to imperfect sensor placement. The review discusses in detail the models for continuous non-invasive blood pressure estimation and their modifications to ensure increased accuracy. In particular, model modifications include input parameters describing blood circulation - systemic vascular resistance, vascular elasticity, and vascular stiffness. The thesis deals with modifications to the existing physical vascular model to more closely mimic the real vascular system of the human body. These modifications include the baroreflex function or the simulation of different wall hardness of artificial arterial segments. As this is a simulation model of the vascular system, the measurement of pressure and volume pulse wave is also an important step, where it is not possible to use photoplethysmography method due to the absence of light absorbing particles. Based on the experimental measurements for different settings of the vascular model, pulse wave measurements were performed using pressure and capacitive sensors with subsequent processing of the measured signals and detection of the pulse wave features. Predictive regression models were established based on the pulse wave features and showed sufficient accuracy in their determination, followed by two methods for obtaining the parameter on the hardness of the vascular wall based on the measurable parameters. The first method was a predictive regression model, which showed an accuracy of 74.1 %, and the second method was an adaptive neuro-fuzzy inference system, which showed an accuracy of 98.7 %. These pulse wave velocity determinations were verified by further direct pulse wave measurements and the results were compared. The dissertation results in the determination of pulse wave propagation velocity using only one plethysmographic sensor without the need for measurements at two different locations with accurate distance measurements and the possibility of application in clinical practice.450 - Katedra kybernetiky a biomedicínského inženýrstvívyhově

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

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Molecular phylogeny of horseshoe crab using mitochondrial Cox1 gene as a benchmark sequence

An effort to assess the utility of 650 bp Cytochrome C oxidase subunit I (DNA barcode) gene in delineating the members horseshoe crabs (Family: xiphosura) with closely related sister taxa was made. A total of 33 sequences were extracted from National Center for Biotechnological Information (NCBI) which include horseshoe crabs, beetles, common crabs and scorpion sequences. Constructed phylogram showed beetles are closely related with horseshoe crabs than common crabs. Scorpion spp were distantly related to xiphosurans. Phylogram and observed genetic distance (GD) date were also revealed that Limulus polyphemus was closely related with Tachypleus tridentatus than with T.gigas. Carcinoscorpius rotundicauda was distantly related with L.polyphemus. The observed mean Genetic Distance (GD) value was higher in 3rd codon position in all the selected group of organisms. Among the horseshoe crabs high GC content was observed in L.polyphemus (38.32%) and lowest was observed in T.tridentatus (32.35%). We conclude that COI sequencing (barcoding) could be used in identifying and delineating evolutionary relatedness with closely related specie

Crab and cockle shells as heterogeneous catalysts in the production of biodiesel

In the present study, the waste crab and cockle shells were utilized as source of calcium oxide to transesterify palm olein into methyl esters (biodiesel). Characterization results revealed that the main component of the shells are calcium carbonate which transformed into calcium oxide upon activated above 700 °C for 2 h. Parametric studies have been investigated and optimal conditions were found to be catalyst amount, 5 wt.% and methanol/oil mass ratio, 0.5:1. The waste catalysts perform equally well as laboratory CaO, thus creating another low-cost catalyst source for producing biodiesel. Reusability results confirmed that the prepared catalyst is able to be reemployed up to five times. Statistical analysis has been performed using a Central Composite Design to evaluate the contribution and performance of the parameters on biodiesel purity