Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

Cyclist performance assessment based on WSN and cloud technologies

Mobility in big cities is a growing problem and the use of bicycles has been a solution which, together with new sharing services, helps to motivate users. There are also more and more users practicing sports involving the use of bicycles. It was in this context that the present dissertation was developed, a distributed sensor system for monitoring cyclists. With the support of a wireless sensor network connected to the internet and, using a set of smart sensors as end-nodes, it is possible to obtain data that will help the cyclist to improve his performance. The coach can monitor and evaluate the performance to improve their training sessions. The health status condition during training it is also monitored using cardiac and respiratory assessment sensors. The information from the nodes of the wireless sensor network is uploaded, via the internet connection, to the Firebase platform. An Android mobile application has been developed, this allows trainers to register cyclists, plan routes and observe the results collected by the network. With the inclusion of these technologies, the coach and the athlete may analyze the performance of a session and compare it with the previous training results. New training sessions may be established according to the athlete's needs. The effectiveness of the proposed system was experimentally tested and several results are included in this dissertation.A mobilidade nas grandes cidades é um problema crescente e a utilização das bicicletas tem vindo a ser uma solução que, em conjunto com novos serviços de partilha, ajudam a motivar os utilizadores. Há também cada vez mais utilizadores a praticar desportos que envolvem a utilização da bicicleta. Foi neste contexto que a presente dissertação foi desenvolvida, um sistema de sensores distribuídos para monitorização de ciclistas. Com o suporte de uma rede de sensores sem fios ligada á internet e, utilizando um conjunto de sensores inteligentes como nós, é possível obter dados que vão ajudar o ciclista a melhorar o seu desempenho. O treinador consegue monitorizar e avaliar o desempenho para aperfeiçoar as sessões de treino. A condição do estado de saúde é também monitorizada utilizando sensores de avaliação cardíaca e de respiratória. A informação proveniente dos nós da rede de sensores sem fios é carregada, através da ligação á internet, para a plataforma Firebase. Foi desenvolvida uma aplicação móvel Android, que permite que os treinadores registem ciclistas, planeiem rotas e observem os resultados recolhidos pela rede. Com a inclusão destas tecnologias, o treinador e o ciclista podem analisar o desempenho de uma sessão e compara-lo com os resultados do treino anterior. Podem ser estabelecidas novas sessões de treino de acordo com as necessidades do atleta. A eficácia do sistema proposto foi testada experimentalmente e os vários resultados foram incluídos nesta dissertação

Reducción de interferencia cardíaca en señales MMG diafragmáticas de un protocolo de carga incremental sostenida mediante filtrado adaptativo RLS

En este trabajo se aplicó el filtrado adaptativo empleando el algoritmo RLS para reducir la interferencia de origen cardíaco en las señales mecanomiográficas diafragmáticas (MMGdi) registradas durante un protocolo de carga incremental sostenida. La señal MMGdi fue dividida en tramos con y sin ruido cardíaco, CRC y SRC, respectivamente. En cada tramo se estudio el comportamiento de la densidad espectral de potencia (DEP), y los parámetros de amplitud RMS y ARV para cada una de las cargas inspiratorias que conforman el test. Los resultados obtenidos, empleando filtro adaptativo de orden =50, con el algoritmo RLS y valores de - = 1, permiten reducir considerablemente la interferencia cardíaca en las señales MMGdi.Postprint (published version

Quantifying individual variation in fine-scale time and energy trade-offs in breeding grey seals: How do differing behavioural types solve these trade-offs?

Lactation is one of the most energetically demanding periods of any female mammal’s life history, where individuals strike a balance with limited resources between their daily activity and towards the growth of their offspring, while still maintaining enough energy stores to maintain themselves in the process. Capital breeding systems mean that females must sustain themselves and their offspring while fasting exclusively on energy reserves acquired beforehand. Female phocids as a result must deal with pressures of a brief terrestrial existence where trade-offs in time, behaviour, energy, and responsiveness to the environment can have tangible consequences to short-term fitness and health. The aim of this thesis was to use new techniques, specifically animal-borne accelerometers and heart rate monitors, to track behaviour and physiology and assess the inherent trade-offs therein through the core duration of lactation in a capital breeding phocid, the grey seal (Halichoerus grypus). Female grey seals were equipped with biologging devices on the Isle of May over three consecutive breeding seasons. Using accelerometry and heart rate techniques, I aimed (1) to remotely classify behaviour using machine learning techniques, (2) to assess trade-offs in time-activity for the duration of lactation, (3) to build a holistic picture of energy allocation within the species, and (4) to develop new methods for tracking heart rate and breathing for terrestrial mammals using grey seals as a model. I also assessed the effect that consistent individual variability in behaviour, stress-coping styles, may have on the methods developed here and how they may drive behaviour and energy trade-offs over time. Accelerometers presented a useful way to remotely track several key behaviours, accurately classifying the core static behaviours over lactation. Consistent individual differences in stress-coping styles, as determined from measures of heart rate variability, modulated almost every aspect of behaviour and physiology measured in this study. More specifically, consistent trade-offs were identified for grey seal mothers between balancing time spent in a state of rest against remaining vigilant across multiple contexts, but also that these individual differences drove how individuals manage and expend that energy, ultimately resulting in differences in short-term fitness outcomes. Effort towards nursing, however, appeared to be largely fixed. Individual differences in energy management also appear to result in different levels of plasticity to environmental pressures, suggesting that future ambient conditions may not be suitable for breeding seals. This thesis also successfully detected breathing rates on land, revealing new evidence as to the energy saving and water conservation benefits of regularly engaging in periods of breath-hold while at rest. Overall, this thesis has provided new tools for exploring behaviour and physiology, and the inherent trade-offs therein, with minimal disturbance to lactating phocid seals. These differences, while minute in the scope of evolutionary constraints, may be among the most important drivers for the success and survival of populations in the face of greater environmental variability as the climate continues to change

Soviet space biology and medicine

Review of Soviet space biology and medicin

Aerospace Medicine and Biology: Cumulative index, 1979

This publication is a cumulative index to the abstracts contained in the Supplements 190 through 201 of 'Aerospace Medicine and Biology: A Continuing Bibliography.' It includes three indexes-subject, personal author, and corporate source

Aerospace medicine and biology, an annotated bibliography. volume xi- 1962-1963 literature

Aerospace medicine and biology - annotated bibliography for 1962 and 196

12th Man in Space Symposium: The Future of Humans in Space. Abstract Volume

The National Aeronautics and Space Administration (NASA) is pleased to host the 12th IAA Man in Space Symposium. A truly international forum, this symposium brings together scientists, engineers, and managers interested in all aspects of human space flight to share the most recent research results and space agency planning related to the future of humans in space. As we look out at the universe from our own uniquely human perspective, we see a world that we affect at the same time that it affects us. Our tomorrows are highlighted by the possibilities generated by our knowledge, our drive, and our dreams. This symposium will examine our future in space from the springboard of our achievements

Cuffless Blood Pressure Monitoring: Estimation of the Waveform and its Prediction Interval

Cuffless blood pressure (BP) estimation devices are receiving considerable attention as tools for improving the management of hypertension, a condition that affects 1.13 billion people worldwide. It is an approach that can provide continuous BP monitoring, which is not possible with existing non-invasive tools. Therefore, it yields a more comprehensive picture of the patient’s state. Cuffless BP monitoring relies on surrogate models of BP and the information encoded in alternative physiological measures, such as photoplethysmography (PPG) or electrocardiography (ECG), to continuously estimate BP. Existing models have typically relied upon pulse-wave delay between two arterial segments or other pulse waveform features in the estimation process. However, the models available in the literature (1) provide an estimation of the systolic BP (SBP), diastolic BP (DBP), and mean BP (MAP) only, (2) are validated solely in controlled environments, and (3) do not assign a confidence metric to the estimates. At this point, cuffless methods are not used by clinicians due to their inaccuracy, the validation inadequacy, and/or the unevaluated uncertainty of the existing methods. The first objective of this thesis is to develop a cuffless modeling approach to estimate the BP waveform from ECG and PPG, and extract important BP features, such as the SBP, DBP, and MAP. Access to the full waveform has significant advantages over previous cuffless BP estimation tools in terms of accuracy and access to additional cardiovascular health markers (e.g., cardiac output), as well as potentially providing arterial stiffness. The second objective of this thesis is to validate cuffless BP estimation during activities of daily living, an uncontrolled environment, but also in more challenging physiological conditions such as during exercise. Such validation is important to increase confidence in cuffless BP monitoring, it also helps understand the limitation of the method and how they would affect clinical outcomes. Finally, in an effort to improve confidence in the cuffless BP estimation framework (third objective), a prediction interval (PI) estimation method is introduced. For potential clinical uses, it is imperative to assess the uncertainty of the BP estimate for acute outcome evaluation and it is even more so if cuffless BP is to be employed outside of the clinic. In this thesis, user-specific nonlinear autoregressive models with exogenous inputs (NARX) are implemented using an artificial neural network (ANN) to predict the BP waveforms using ECG and/or PPG signals as inputs. To validate the NARX-based BP estimation framework during activities of daily living, data were collected during six-hours testing phase wherein the participants go about their normal daily living activities. Data are further collected at four-month and six-month time points to validate long-term performance. To broaden the range of BP in the training data, subjects followed a short procedure consisting of sitting, standing, walking, Valsalva maneuvers, and static handgrip exercises. To evaluate the uncertainty of the BP estimates, one-class support vector machines (OCSVM) models are trained to cluster data in terms of the percentage of outliers. New BP estimates are then assigned to a cluster using the OCSVMs hyperplanes, and the PIs are estimated using the BP error standard deviation associated with different training data clusters. The OCSVM is used to estimate the PI for three BP model architectures: NARX models, feedforward ANN models, and pulse arrival time (PAT models). The three BP estimations from the models are fused using the covariance intersection fusion algorithm, which improves BP and PI estimates in comparison with individual model performance. The proposed method models the BP as a dynamical system leading to better accuracy in the estimation of SBP, DBP and MAP when compared to the PAT model. Moreover, the NARX model, with its ability to provide the BP waveform, yields more insight into patient health. The NARX model demonstrates superior accuracy and correlation with “ground truth” SBP and DBP measures compared to the PAT models and a clear advantage in estimating the large range of BP. Preliminary results show that the NARX models can accurately estimate BP even months apart from the training. Preliminary testing suggests that it is robust against variabilities due to sensor placement. The employed model fusion architecture establishes a method for cuffless BP estimation and its PI during activities of daily living that can be used for continuous monitoring and acute hypotension and hypertension detection. The NARX model, with its capacity to estimate a large range of BP, is next tested during moderate and heavy intensity exercise. Participants performed three cycling exercises: a ramp-incremental exercise test to exhaustion, a moderate and a heavy pseudorandom binary sequence exercise tests on an electronically braked cycle ergometer. Subject-specific and population-based NARX models are compared with feedforward ANN models and PAT (and heart rate) models. Population-based NARX models, when trained on 11 participants’ three cycling tests (tested on the participant left out of training), perform better than the other models and show good capability at estimating large changes in MAP. A limitation of the approach is the incapability of the models to track consistent decreases in BP during the exercise caused by a decrease in peripheral resistance since this information is apparently not encoded in either the forehead PPG or ECG signals. Nevertheless, the NARX model shows good precision during the whole 21 minutes testing window, a precision that is increased when using a shorter evaluation time window, and that can potentially be even further increased if trained on more data. The validation protocols and the use of a confidence metric developed in this thesis is of great value for such health monitoring application. Through such methodology, it is hoped that cuffless BP estimation becomes, one day, a well-established BP measurement method