81 research outputs found
The Neural Mechanisms of Sleep and Migraine
Whilst a bidirectional relationship between sleep and migraine has long been postulated, this remains mainly speculative, and the underlying neural mechanisms remain to be determined. In this thesis we sought to explore this with clinical and preclinical methodologies. It was hypothesised that disrupted sleep-wake and nociception-regulating neural networks including key brainstem and diencephalic structures alter the thresholds for attack initiation and increase migraine susceptibility.Firstly, we used a meta-analytic approach to determine whether migraine patients have altered sleep, identifying that they have poorer subjective sleep quality and altered sleep physiology including reduced rapid-eye-movement sleep, compared to healthy controls. By collating data from users of the Migraine Buddy application (Healint Ltd.) and conducting Bayesian regression models we explored whether changes in sleep were predictors of an attack and conversely whether experiencing an attack would predict changes in subsequent sleep. We determined that interrupted sleep and deviations from typical sleep were potential predictors of a next day migraine attack but having an attack did not predict sleep duration.Secondly, we utilised mouse models of sleep deprivation and demonstrated that this led to orofacial mechanical allodynia - a commonly reported migraine phenotype indicative of sensitisation of the trigeminovascular system. Mechanistic insight was provided in that orexin-A, a hypothalamic arousal-promoting peptide which stabilises sleep-wake transitions reversed this phenotype.Finally, we explored whether familial natural short sleepers (FNSS) which are reported to have increased orexin expression, are less susceptible to migraine-related phenotypes using a transgenic mouse line harbouring the P384R mutation in the hDEC2 gene. We observed no significant differences in migraine-related phenotypes at baseline, however, when exposed to a clinical migraine trigger (nitroglycerin) FNSS mice demonstrated reduced orofacial hypersensitivity and photophobia, indicative of decreased migraine susceptibility. FNSS also displayed alterations in metabolites underlying energy metabolism and oxidative stress, suggesting a potential link between metabolism and headache pathophysiology.Taken together, the data in this thesis has shed light on the relationship between sleep and migraine, highlighting alterations in sleep as a potential precipitant of migraine attacks, and identifying genetic mechanisms underlying sleep regulation which may curtail migraine development, as well as possible therapeutic targets based on the orexinergic system. Although further work is needed to fully understand this neural basis, this has promising clinical implications and has furthered our understanding of migraine pathophysiology.<br/
Enabling Wearable Hemodynamic Monitoring Using Multimodal Cardiomechanical Sensing Systems
Hemodynamic parameters such as blood pressure and stroke volume are instrumental to understanding the pathogenesis of cardiovascular disease. Unfortunately, the monitoring of these hemodynamic parameters is still limited to in-clinic measurements and cumbersome hardware precludes convenient, ubiquitous use. To address this burden, in this work, we explore seismocardiogram-based wearable multimodal sensing techniques to estimate blood pressure and stroke volume. First, the performance of a multimodal, wrist-worn device capable of obtaining noninvasive pulse transit time measurements is used to estimate blood pressure in an unsupervised, at-home setting. Second, the feasibility of this wrist-worn device is comprehensively evaluated in a diverse and medically underserved population over the course of several perturbations used to modulate blood pressure through different pathways. Finally, the ability of wearable signals—acquired from a custom chest-worn biosensor—to noninvasively quantify stroke volume in patients with congenital heart disease is examined in a hospital setting. Collectively, this work demonstrates the advancements necessary towards enabling noninvasive, longitudinal, and accurate measurements of these hemodynamic parameters in remote settings, which offers to improve health equity and disease monitoring in low-resource settings.Ph.D
Continuous Cardiorespiratory Monitoring Using Ballistocardiography From Load Cells Embedded in a Hospital Bed
The objective of this research is to explore signal processing and machine learning techniques to allow continuous monitoring of cardiorespiratory parameters using the ballistocardiogram (BCG) signals recorded with sensors embedded in a hospital bed. First, the heart rate (HR) estimation algorithms were presented. The first is signal processing-based HR estimation with array processing for multi-channel combination. The second uses a deep learning (DL) model that transforms BCG signals into an interpretable triangular waveform, from which heartbeat locations can be estimated. Second part of the work focuses on estimating respiratory rate (RR) and respiratory volume (RV) using the respiration waveforms derived from the low-frequency components of the load cell signals. Lastly, this work presents two models for blood pressure (BP) estimation -- 1) Conventional pulse transit time (PTT)-based model and 2) DL-based model, both using multi-channel BCG and the photoplethysmogram (PPG) signals to extract features. Overall, this work established methods to enable non-invasive and continuous monitoring of standard vital signs utilizing the sensors already embedded in commonly-deployed commercially available hospital beds. Such technologies could potentially improve the continuous assessment of the patients' physiologic state without adding an extra burden on the caregivers.Ph.D
Biosensors for Diagnosis and Monitoring
Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field
Sustainable Technology and Elderly Life
The coming years will see an exponential increase in the proportion of elderly people in our society. This accelerated growth brings with it major challenges in relation to the sustainability of the system. There are different aspects where these changes will have a special incidence: health systems and their monitoring; the development of a framework in which the elderly can develop their daily lives satisfactorily; and in the design of intelligent cities adapted to the future sociodemographic profile. The discussion of the challenges faced, together with the current technological evolution, can show possible ways of meeting the challenges. There are different aspects where these changes will have a special incidence: health systems and their monitoring; the development of a framework in which the elderly can develop their daily lives satisfactorily; and in the design of intelligent cities adapted to the future sociodemographic profile. This special issue discusses various ways in which sustainable technologies can be applied to improve the lives of the elderly. Six articles on the subject are featured in this volume. From a systematic review of the literature to the development of gamification and health improvement projects. The articles present suggestive proposals for the improvement of the lives of the elderly. The volume is a resource of interest for the scientific community, since it shows different research gaps in the current state of the art. But it is also a document that can help social policy makers and people working in this domain to planning successful projects
Contributions to Context-Aware Smart Healthcare: A Security and Privacy Perspective
Les tecnologies de la informació i la comunicació han canviat les nostres vides de manera irreversible. La indústria sanità ria, una de les indústries més grans i de major creixement, està dedicant molts esforços per adoptar les últimes tecnologies en la prà ctica mèdica dià ria. Per tant, no és sorprenent que els paradigmes sanitaris estiguin en constant evolució cercant serveis més eficients, eficaços i sostenibles. En aquest context, el potencial de la computació ubiqua mitjançant telèfons intel·ligents, rellotges intel·ligents i altres dispositius IoT ha esdevingut fonamental per recopilar grans volums de dades, especialment relacionats amb l'estat de salut i la ubicació de les persones. Les millores en les capacitats de detecció juntament amb l'aparició de xarxes de telecomunicacions d'alta velocitat han facilitat la implementació d'entorns sensibles al context, com les cases i les ciutats intel·ligents, capaços d'adaptar-se a les necessitats dels ciutadans. La interacció entre la computació ubiqua i els entorns sensibles al context va obrir la porta al paradigma de la salut intel·ligent, centrat en la prestació de serveis de salut personalitzats i de valor afegit mitjançant l'explotació de grans quantitats de dades sanità ries, de mobilitat i contextuals. No obstant, la gestió de dades sanità ries, des de la seva recollida fins a la seva anà lisi, planteja una sèrie de problemes desafiants a causa del seu carà cter altament confidencial. Aquesta tesi té per objectiu abordar diversos reptes de seguretat i privadesa dins del paradigma de la salut intel·ligent. Els resultats d'aquesta tesi pretenen ajudar a la comunitat cientÃfica a millorar la seguretat dels entorns intel·ligents del futur, aixà com la privadesa dels ciutadans respecte a les seves dades personals i sanità ries.Las tecnologÃas de la información y la comunicación han cambiado nuestras vidas de forma irreversible. La industria sanitaria, una de las industrias más grandes y de mayor crecimiento, está dedicando muchos esfuerzos por adoptar las últimas tecnologÃas en la práctica médica diaria. Por tanto, no es sorprendente que los paradigmas sanitarios estén en constante evolución en busca de servicios más eficientes, eficaces y sostenibles. En este contexto, el potencial de la computación ubicua mediante teléfonos inteligentes, relojes inteligentes, dispositivos wearables y otros dispositivos IoT ha sido fundamental para recopilar grandes volúmenes de datos, especialmente relacionados con el estado de salud y la localización de las personas. Las mejoras en las capacidades de detección junto con la aparición de redes de telecomunicaciones de alta velocidad han facilitado la implementación de entornos sensibles al contexto, como las casas y las ciudades inteligentes, capaces de adaptarse a las necesidades de los ciudadanos. La interacción entre la computación ubicua y los entornos sensibles al contexto abrió la puerta al paradigma de la salud inteligente, centrado en la prestación de servicios de salud personalizados y de valor añadido mediante la explotación significativa de grandes cantidades de datos sanitarios, de movilidad y contextuales. No obstante, la gestión de datos sanitarios, desde su recogida hasta su análisis, plantea una serie de cuestiones desafiantes debido a su naturaleza altamente confidencial. Esta tesis tiene por objetivo abordar varios retos de seguridad y privacidad dentro del paradigma de la salud inteligente. Los resultados de esta tesis pretenden ayudar a la comunidad cientÃfica a mejorar la seguridad de los entornos inteligentes del futuro, asà como la privacidad de los ciudadanos con respecto a sus datos personales y sanitarios.Information and communication technologies have irreversibly changed our lives. The healthcare industry, one of the world’s largest and fastest-growing industries, is dedicating many efforts in adopting the latest technologies into daily medical practice. It is not therefore surprising that healthcare paradigms are constantly evolving seeking for more efficient, effective and sustainable services. In this context, the potential of ubiquitous computing through smartphones, smartwatches, wearables and IoT devices has become fundamental to collect large volumes of data, including people's health status and people’s location. The enhanced sensing capabilities together with the emergence of high-speed telecommunication networks have facilitated the implementation of context-aware environments, such as smart homes and smart cities, able to adapt themselves to the citizens needs. The interplay between ubiquitous computing and context-aware environments opened the door to the so-called smart health paradigm, focused on the provision of added-value personalised health services by meaningfully exploiting vast amounts of health, mobility and contextual data. However, the management of health data, from their gathering to their analysis, arises a number of challenging issues due to their highly confidential nature. In particular, this dissertation addresses several security and privacy challenges within the smart health paradigm. The results of this dissertation are intended to help the research community to enhance the security of the intelligent environments of the future as well as the privacy of the citizens regarding their personal and health data
Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals
Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference 0.99, BA ratio 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar
Sensors for Vital Signs Monitoring
Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data
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