Continuous monitoring of vital parameters for clinically valid assessment of human health status

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2019The lack of devices suitable for acquiring accurate and reliable measures of patients' physiolog-ical signals in a remote and continuous manner together with the advances in data acquisition technol-ogies during the last decades, have led to the emergence of wearable devices for healthcare. Wearable devices enable remote, continuous and long-term health monitoring in unattended setting. In this con-text, the Swiss Federal Laboratories for Material Science and Technology (Empa) developed a wearable system for long-term electrocardiogram measurements, referred to as textile belt. It consists of a chest strap with two embroidered textile electrodes. The validity of Empa’s system for electrocardiogram monitoring has been proven in a clinical setting. This work aimed to assess the validity of the textile belt for electrocardiogram monitoring in a home setting and to supplement the existing system with sensors for respiratory monitoring. Another objective was to evaluate the suitability of the same weara-ble, as a multi-sensor system, for activity monitoring. A study involving 12 patients (10 males and 2 females, interquartile range for age of 48–59 years and for body mass indexes of 28.0–35.5 kg.m-2) with suspected sleep apnoea was carried out. Overnight electrocardiogram was measured in a total of 28 nights. The quality of recorded signals was assessed using signal-to-noise ratio, artefacts detection and Poincaré plots. Study data were compared to data from the same subjects, acquired in the clinical setting. For respiratory monitoring, optical fibre-based sensors of different geometries were integrated into the textile belt. Signal processing algorithms for breathing rate and tidal volume estimation based on respiratory signals acquired by the sensors were developed. Pilot studies were conducted to compare the different approaches for respiratory monitoring. The quality of respiratory signals was determined based on signal segments “sinusoidality”, evaluated through the calculation of the cross-correlation between signal segments and segment-specific reference waves. A method for accelerometry-based lying position recognition was proposed, and the proof of concept of activity intensity classification through the combination of subjects’ inertial acceleration, heart rate and breathing rate data, was presented. Finally, a study with three participants (1 male and 2 females, aged 21 ± 2 years, body mass index of 20.3 ± 1.5 kg.m-2) was conducted to assess the validity of the textile belt for respiratory and activity monitoring. Electrocardiogram signals acquired by the textile belt in the home setting were found to have better quality than the data acquired by the same device in the clinical setting. Although a higher artefact percentage was found for the textile belt, signal-to-noise ratio of electrocardiogram signals recorded by the textile belt in the home setting was similar to that of signals acquired by the gel electrodes in the clinical setting. A good agreement was found between the RR-intervals derived from signals recorded in home and clinical settings. Besides, for artefact percentages greater than 3%, visual assessment of Poincaré plots proved to be effective for the determination of the primary source of artefacts (noise or ectopic beats). Acceleration data allowed posture recognition (i.e. lying or standing/sitting, lying position) with an accuracy of 91% and positive predictive value of 80%. Lastly, preliminary results of physical activity intensity classification yielded high accuracy, showing the potential of the proposed method. The textile belt proved to be appropriate for long-term, remote and continuous monitoring of subjects’ physical and physiological parameters. It can monitor not only electrocardiogram, but also breathing rate, body posture and physical activity intensity, having the potential to be used as tool for disease prediction and diagnose support.Contexto: A falta de dispositivos adequados para a monitorização de sinais fisiológicos de um modo remoto e contínuo, juntamente com avanços tecnológicos na área de aquisição de dados nas últimas décadas, levaram ao surgimento de wearable devices, i.e. dispositivos vestíveis, no sector da saúde. Wearable devices possibilitam a monitorização do estado de saúde, de uma forma remota, contínua e de longa duração. Quando feito em ambiente domiciliar, este tipo de monitorização (i.e. contínua, remota e de longa duração) tem várias vantagens: diminui a pressão posta sobre o sistema de saúde, reduz despesas associadas ao internamento e acelera a resposta a emergências, permitindo deteção precoce e prevenção de condições crónicas. Neste contexto, a Empa, Laboratórios Federais Suíços de Ciência e Tecnologia de Materiais, desenvolveu um sistema vestível para a monitorização de eletrocardiograma de longa duração. Este sistema consiste num cinto peitoral com dois elétrodos têxteis integrados. Os elétrodos têxteis são feitos de fio de polietileno tereftalato revestido com prata e uma ultrafina camada de titânio no topo. De modo a garantir a aquisição de sinais de alta qualidade, o cinto tem nele integrado um reservatório de água que liberta vapor de água para humidificar os elétrodos. Este reservatório per-mite a monitorização contínua de eletrocardiograma por 5 a 10 dias, sem necessitar de recarga. A vali-dade do cinto para a monitorização de eletrocardiograma em ambiente clínico já foi provada. Objetivo: Este trabalho teve por objetivo avaliar a validade do cinto para a monitorização de eletrocar-diograma em ambiente domiciliar e complementar o sistema existente com sensores para monitorização respiratória. Um outro objetivo foi analisar a adequação do cinto, como um sistema multisensor, para monitorização da atividade física. Métodos: Um estudo com 12 pacientes com suspeita de apneia do sono (10 homens e 2 mulheres, am-plitude interquartil de 48–59 anos para a idade e de 28.0–35.5 kg.m-2 para o índice de massa corporal) foi conduzido para avaliar a qualidade do sinal de eletrocardiograma medido em ambiente domiciliar. O sinal de eletrocardiograma dos pacientes foi monitorizado continuamente, num total de 28 noites. A qualidade dos sinais adquiridos foi analisada através do cálculo da razão sinal-ruído; da deteção de ar-tefactos, i.e., intervalos RR com um valor inviável de um ponto de vista fisiológico; e de gráficos de Poincaré, um método de análise não linear da distribuição dos intervalos RR registados. Os dados ad-quiridos neste estudo foram comparados com dados dos mesmos pacientes, adquiridos em ambiente hospitalar. Para a monitorização respiratória, sensores feitos de fibra óptica foram integrados no cinto. Al-gorítmicos para a estimar a frequência respiratória e o volume corrente dos sujeitos tendo por base o sinal medido pelas fibras ópticas foram desenvolvidos neste trabalho. As diferentes abordagens foram comparadas através de estudos piloto. Diferentes métodos para avaliação da qualidade do sinal adquirido foram sugeridos. Um método de reconhecimento da postura corporal através do cálculo de ângulos de orientação com base na aceleração medida foi proposto. A prova de conceito da determinação da intensidade da atividade física pela combinação de informações relativas á aceleração inercial e frequências cardíaca e respiratória dos sujeitos, é também apresentada neste trabalho. Um estudo foi conduzido para avaliar a validade do cinto para monitorização da respiração e da atividade física. O estudo contou com 10 parti-cipantes, dos quais 3 vestiram o cinto para monitorização da respiração (1 homem e 2 mulheres, idade 21 ± 2 anos, índice de massa corporal 20.3 ± 1.5 kg.m-2). Resultados: O estudo feito com pacientes com suspeita de apneia do sono revelou que os sinais eletro-cardiográficos adquiridos pelo cinto em ambiente domiciliar foram de melhor qualidade que os sinais adquiridos pelo mesmo dispositivo em ambiente hospitalar. Uma percentagem de artefacto de 2.87% ±4.14% foi observada para os dados adquiridos pelos elétrodos comummente usados em ambiente hospi-talar, 7.49% ± 10.76% para os dados adquiridos pelo cinto em ambiente domiciliar e 9.66% ± 14.65% para os dados adquiridos pelo cinto em ambiente hospitalar. Embora tenham tido uma maior percenta-gem de artefacto, a razão sinal-ruído dos sinais eletrocardiográficos adquiridos pelo cinto em ambiente domiciliar foi semelhante á dos sinais adquiridos pelos elétrodos de gel em ambiente hospitalar. Resul-tados sugerem uma boa concordância entre os intervalos RR calculados com base nos eletrocardiogra-mas registados em ambientes hospitalar e domiciliar. Além disso, para sinais com percentagem de arte-facto superior a 3%, a avaliação visual dos gráficos de Poincaré provou ser um bom método para a determinação da fonte primária de artefactos (batimentos irregulares ou ruído). A monitorização da aceleração dos sujeitos permitiu o reconhecimento da postura corporal (isto é, deitado ou sentado/em pé) com uma exatidão de 91% e valor preditivo positivo de 80%. Por fim, a classificação da intensidade da atividade física baseado na aceleração inercial e frequências cardíaca e respiratória revelou elevada exatidão, mostrando o potencial desta técnica. Conclusão: O cinto desenvolvido pela Empa provou ser apropriado para monitorização de longa-dura-ção de variáveis físicas e fisiológicos, de uma forma remota e contínua. O cinto permite não só monito-rizar eletrocardiograma, mas também frequência respiratória, postura corporal e intensidade da atividade física. Outros estudos devem ser conduzidos para corroborar os resultados e conclusões deste trabalho. Outros sensores poderão ser integrados no cinto de modo a possibilitar a monitorização de outras vari-áveis fisiológicas de relevância clínica. Este sistema tem o potencial de ser usado como uma ferramenta para predição de doenças e apoio ao diagnóstico

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

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

Proceedings of the NASA Conference on Space Telerobotics, volume 1

The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

Contextualized Monitoring in the Marine Environment

Marine mammal monitoring has seen improvements in the last few decades with advances made to both the monitoring hardware and post-processing computation methods. The addition of tag-based hydrophones, Fastloc GPS units, and an ever-increasing array of IMU sensors, coupled with the use of energetics proxies such as Overall Dynamic Body Acceleration (ODBA), has led to new insights into marine mammal swimming behavior that would not be possible using traditional secondary-observer methods. However, these advances have primarily been focused on and implemented in wild animal tracking, with less attention paid to the managed environment. This is a particularly important gap, as the cooperative nature of managed animals allows for research on swimming kinematics and energetics behavior with an intricacy that is difficult to achieve in the wild. While proxy-based methods are useful for relative inter-or-intra-animal comparisons, they are not robust enough for absolute energetics estimates for the animals, which can limit our understanding of their metabolic patterns. Proxies such as ODBA are based on filtered on-animal IMU data, and measure the aggregate high-pass acceleration as an estimate for the magnitude of the animal’s activity level at a given point in time. Depending on its body structure and locomotive gait, tag placement on the animal and the specific filtering techniques used can significantly impact the results. Any relation made to energetics is then strictly a mapping: a relation that may apply well to an individual or group under specific experimental conditions, but is not generalizable. To address this gap, this dissertation presents new tag-based hardware and data processing methods for persistently estimating cetacean swimming kinematics and energetics, which are functional in both managed and wild settings. Unfortunately, localization techniques for managed environments have not been thoroughly explored, so a new animal tracking method is required to spatially contextualize information on swimming behavior. State-of-the-art wild cetacean localization operates via sparse GPS updates upon animal surfacings, and can be paired with biologging-tag-based odometry for a continuous track. Such an approach is hindered by the structure and scale of managed environments: GPS suffers from increased error near and within buildings, and current odometry methods are insufficiently precise for habitat scales where locations of interest might be separated by meters, rather than kilometers (such as in the wild). There is then a need for a tracking method that uses an alternate source of absolute animal locations that can achieve the high precision necessary for meaningful results given the spatial scale. To this end, this dissertation presents a novel animal localization framework, based on tracking and sensor filtering techniques from the field of robotics that have been tailored for use in this setting. Overall, this research targets two main gaps: 1) the lack of persistent, absolute estimates of animal swimming energetics and kinematics, and 2) the lack of a robust, precise localization method for managed cetaceans. To address these gaps, the hardware and animal tracking methods developed to enable the rest of the dissertation are first defined. Next, a physics-based approach to directly monitor cetacean swimming energetics is both presented and implemented to study animal propulsion patterns under varying effort conditions. Finally, a high-fidelity 3D monitoring framework is introduced for tracking institutionally-managed cetaceans, and is applied alongside the energetics estimation method to provide a first look at the potential of spatially-contextualized animal monitoring.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169756/1/gabaldon_1.pd

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science