Sistema para medição e análise de balistocardiografia baseado em MEMS

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresO bombeamento do sangue estimulado pelo coração provoca uma variação do centro de massa do corpo, dando origem ao aparecimento de micromovimentos devido às forças de repulsão para que este mantenha o seu momento físico. Um sistema de balistocardiografia convenciona um método não invasivo, que tira proveito desses micromovimentos produzindo um sinal representativo do comportamento mecânico do sistema cardiovascular e do corpo. Nas últimas décadas, os avanços tecnológicos possibilitaram o desenvolvimento de sistemas de medição de BCG (balistocardiografia) de maior capacidade de diagnóstico, já que antigamente caíram em desuso devido ao aparecimento do ECG (Eletrocardiograma) e da Ressonância Magnética. Recentemente, têm sido desenvolvidos alguns sistemas para medição de BCG em diferentes abordagens, no entanto ainda apresentam certos inconvenientes uma vez que impõem algum limite prático na medição devido à posição desconfortável do utilizador, ou relativamente às características do sensor utilizado que exige a necessidade de estar em contacto com o corpo para uma aquisição plausível do sinal. Por outro lado, o progresso a nível da microtecnologia e do desenvolvimento de acelerómetros MEMS tem possibilitado a criação de sensores de elevada resolução. Entre eles, surge o desenvolvimento de acelerómetros MEMS baseados no tempo de pull-in que utilizam o tempo como mecanismo de transdução da aceleração, permitindo alcançar resoluções na ordem dos micro-g. Surge assim a oportunidade de implementar um sistema para aquisição de sinais de BCG que integre um acelerómetro MEMS baseado na medição de tempos de pull-in. Esta dissertação reflete o dimensionamento e implementação desse sistema de medição de BCG assim como o desenvolvimento de software para aquisição e visualização do sinal medido em tempo real. Com o intuito de averiguar a qualidade do dispositivo desenvolvido na deteção dos sinais de BCG, são implementadas métricas para identificação das ondas típicas desse sinal e que permitem determinar alguns eventos referentes ao comportamento cardíaco. Estes procedimentos habilitam a utilização deste sistema na realização de análises clinicas para uma investigação mais consistente da capacidade de diagnóstico desta técnica.The heart pumping causes a variation of the body's center of mass, which creates micro movements due to the repulsive forces that keep the physical momentum. A ballistocardiography system is a non-invasive method, which takes advantage of these micro movements producing a representative signal of the mechanical behavior of the cardiovascular system and body. In recent decades, technological advances have enabled the development of ballistocardiography (BCG) measurement systems with reasonable performance, as opposed to the initial systems that revealed weaknesses and have fallen into disuse due to the appearance of the ECG (electrocardiogram) and Magnetic Resonance. Recently some BCG systems have been developed using different technological approaches, however they still present drawbacks related to signal acquisition such as uncomfortable user position during measurements, or using sensors that need to be in contact with the body to a plausible signal acquisition. On the other hand, the progress level of microtechnology and MEMS accelerometers has enabled the creation of high-resolution sensors. Among them, MEMS accelerometers based on the pull-in time, using time as the acceleration transduction mechanism, have been demonstrated and enable the measurement of micro-g signals. This raises the opportunity of implementing an acquisition system for BCG signals that incorporate a MEMS accelerometer based on the measurement of pull-in time. This dissertation addresses the design and implementation of such BCG measuring system as well as the development of software for the acquisition and visualization of the measured signal in real time. To determine the quality of the device developed in the detection of BCG signals, metrics for identification of the typical waves of the signal and for determining some events related to cardiac performance are also implemented. These procedures enable the use of this system to perform clinical analysis aiming a more consistent study of the diagnostic capability of this technique

High-resolution seismocardiogram acquisition and analysis system

Several devices and measurement approaches have recently been developed to perform ballistocardiogram (BCG) and seismocardiogram (SCG) measurements. The development of a wireless acquisition system (hardware and software), incorporating a novel high-resolution micro-electro-mechanical system (MEMS) accelerometer for SCG and BCG signals acquisition and data treatment is presented in this paper. A small accelerometer, with a sensitivity of up to 0.164 µs/µg and a noise density below 6.5 µg/ Hz is presented and used in a wireless acquisition system for BCG and SCG measurement applications. The wireless acquisition system also incorporates electrocardiogram (ECG) signals acquisition, and the developed software enables the real-time acquisition and visualization of SCG and ECG signals (sensor positioned on chest). It then calculates metrics related to cardiac performance as well as the correlation of data from previously performed sessions with echocardiogram (ECHO) parameters. A preliminarily clinical study of over 22 subjects (including healthy subjects and cardiovascular patients) was performed to test the capability of the developed system. Data correlation between this measurement system and echocardiogram exams is also performed. The high resolution of the MEMS accelerometer used provides a better signal for SCG wave recognition, enabling a more consistent study of the diagnostic capability of this technique in clinical analysis.This work is supported by FCT with the reference project UID/EEA/04436/2013, COMPETE 2020 with the code POCI-01-0145-FEDER-006941

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Geoeconomic variations in epidemiology, ventilation management, and outcomes in invasively ventilated intensive care unit patients without acute respiratory distress syndrome: a pooled analysis of four observational studies

Background: Geoeconomic variations in epidemiology, the practice of ventilation, and outcome in invasively ventilated intensive care unit (ICU) patients without acute respiratory distress syndrome (ARDS) remain unexplored. In this analysis we aim to address these gaps using individual patient data of four large observational studies. Methods: In this pooled analysis we harmonised individual patient data from the ERICC, LUNG SAFE, PRoVENT, and PRoVENT-iMiC prospective observational studies, which were conducted from June, 2011, to December, 2018, in 534 ICUs in 54 countries. We used the 2016 World Bank classification to define two geoeconomic regions: middle-income countries (MICs) and high-income countries (HICs). ARDS was defined according to the Berlin criteria. Descriptive statistics were used to compare patients in MICs versus HICs. The primary outcome was the use of low tidal volume ventilation (LTVV) for the first 3 days of mechanical ventilation. Secondary outcomes were key ventilation parameters (tidal volume size, positive end-expiratory pressure, fraction of inspired oxygen, peak pressure, plateau pressure, driving pressure, and respiratory rate), patient characteristics, the risk for and actual development of acute respiratory distress syndrome after the first day of ventilation, duration of ventilation, ICU length of stay, and ICU mortality. Findings: Of the 7608 patients included in the original studies, this analysis included 3852 patients without ARDS, of whom 2345 were from MICs and 1507 were from HICs. Patients in MICs were younger, shorter and with a slightly lower body-mass index, more often had diabetes and active cancer, but less often chronic obstructive pulmonary disease and heart failure than patients from HICs. Sequential organ failure assessment scores were similar in MICs and HICs. Use of LTVV in MICs and HICs was comparable (42·4% vs 44·2%; absolute difference -1·69 [-9·58 to 6·11] p=0·67; data available in 3174 [82%] of 3852 patients). The median applied positive end expiratory pressure was lower in MICs than in HICs (5 [IQR 5-8] vs 6 [5-8] cm H2O; p=0·0011). ICU mortality was higher in MICs than in HICs (30·5% vs 19·9%; p=0·0004; adjusted effect 16·41% [95% CI 9·52-23·52]; p<0·0001) and was inversely associated with gross domestic product (adjusted odds ratio for a US$10 000 increase per capita 0·80 [95% CI 0·75-0·86]; p<0·0001). Interpretation: Despite similar disease severity and ventilation management, ICU mortality in patients without ARDS is higher in MICs than in HICs, with a strong association with country-level economic status