Дисертації з теми "Biomedical signal sensor"

WebAssembly is a new binary code specification, which was initially designed to complement JavaScript in web applications. WebAssembly is inherently portable and small, designed for multiplatform usage. Therefore, WebAssembly modules can be created to support embedded system-based biomedical sensor operation. However, WebAssembly has its own limitations to compensate with its portability. In this thesis, we show how WebAssembly modules can be applied to the basic biomedical modalities of body temperature, heart rate, and breathing pattern.  We show how the implementation performed, and what challenges were met during the development. It is concluded that WebAssembly can be applied for achieving safe and effective biomedical sensor devices, although with some limitations.
WebAssembly är ett nytt binärt maskinkodsformat, ursprungligen skapat för att komplettera JavaScript i webbapplikationer.  WebAssemblys kod är liten och kan lätt användas på flera plattformar. Därför kan WebAssembly-moduler skapas för att stödja inbyggda system för biomedicinska sensorer. WebAssembly har dock sina egna begränsningar på grund av sin portabilitet.  I denna avhandling visar vi hur WebAssembly-moduler kan användas på enkla biomedicinska mätningar av kroppstemperatur, hjärtfrekvens och andningsmönster. Vi visar hur implementeringen genomfördes och vilka utmaningar som möttes under utvecklingen. Slutsatsen är att WebAssembly kan tillämpas för att skapa säkra och effektiva biomedicinska sensorenheter, även om det finns en del begränsningar.

Parkinson’s disease is associated with reduced coordination between respiration and locomotion. For the neurological rehabilitation research, it requires a long-time monitoring system, which enables the online analysis of the patient’s vegetative locomotor coordination. In this work a real time step detector using three-dimensional accelerometer signal for the patients with Parkinson‘s disease is developed. This step detector is a complement for a recently developed system included of intelligent, wirelessly communicating sensors. The system helps to focus on the scientific questions whether this coordination may serve as a measure for the rehabilitation progress of PD patients.
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Магістерська робота містить основну частину на 110 аркушах, 22 ілюстрацій, 22 таблиці кількість джерел за переліком посилань 53 джерела. Об’єктом дослідження є процес зняття електрокардіограми людини. Предметом дослідження є електроди для моніторингу біомедичних сигналів. Метою роботи є огляд роботи електродів в комплексі із датчиком ЕКГ для цифрової електронної лабораторії і запропонування оптимального варіанту електродів для подальшого застосування. Методом дослідження є теоретичний огляд існуючих різновидів біомедичних електродів та можливості їх технічного вдосконалення, а також практична перевірка роботи електродів у цифровій електронній лабораторії. Результатом роботи є отримані зображення ЕКГ при різних дослідженнях з використанням існуючих електродів та визначення оптимального варіанту електродів для застосування. Новизна результатів роботи полягає у застосуванні їх до цифрової електронної лабораторії, де будуть проводитися подальші дослідження та у визначенні вектору подальших досліджень у напрямку сухих ємнісних голчастих електродів. Результати даної роботи можуть бути використанні для подальшого їх застосування у лабораторних роботах та для проектування комбінованого типу електродів. Можливі напрямки продовження досліджень: проектування комбінованого типу сухих ємнісних голчастих електродів. Галузь застосування: навчальна цифрова електронна лабораторія, медицина.
Master's work contains the main part of 110 sheets, 22 illustrations, 22 tables and a number of sources by the list of references 53 source. The object of research is the process of taking human's electrocardiogram. The subject of the study is electrodes for monitoring biomedical signals. The aim of the work is to review the work of electrodes in conjunction with an ECG sensor for a digital electronic laboratory and to offer an optimal variant of electrodes for further application. The research method is a theoretical review of existing varieties of biomedical electrodes and the possibilities for their technical improvement, as well as practical verification of the work of electrodes in a digital electronic laboratory. The result of the work is the obtained ECG images in various studies using existing electrodes and the determination of the optimal variant of electrodes for use. The novelty of the results of the work is to apply them to a digital electronic laboratory, where further research will be carried out and in determining the vector of further research in the direction of dry capacitive needle electrodes. The results of this work can be used for their further application in laboratory work and for the design of a combined type of electrodes. Possible directions for the continuation of research: design of a combined type of dry capacitive needle electrodes. Field of application: educational Digital Electronic Laboratory, Medicine.

Surface-plasmon-resonance (SPR) sensors are widely used in biological, chemical, medical, and environmental sensing. This dissertation describes the design and development of dual-mode, self-referencing SPR sensors supporting two surface-plasmon modes (long- and short-range) which can differentiate surface binding interactions from bulk index changes at a single sensing location. Dual-mode SPR sensors have been optimized for surface limit of detection (LOD). In a wavelength interrogated optical setup, both surface plasmons are simultaneously excited at the same location and incident angle but at different wavelengths. To improve the sensor performance, a new approach to dual-mode SPR sensing is presented that offers improved differentiation between surface and bulk effects. By using an angular interrogation, both surface plasmons are simultaneously excited at the same location and wavelength but at different angles. Angular interrogation offers at least a factor of 3.6 improvement in surface and bulk cross-sensitivity compared to wavelength-interrogated dual-mode SPR sensors. Multi-mode SPR sensors supporting at least three surface-plasmon modes can differentiate a target surface effect from interfering surface effects and bulk index changes. This dissertation describes a tri-mode SPR sensor which supports three surface plasmon resonance modes at one single sensing position, where each mode is excited at a different wavelength. The tri-mode SPR sensor can successfully differentiate specific binding from the non-specific binding and bulk index changes.

There is an ever-increasing need to enhance the capability of sensor technology for health, structural and environmental monitoring. One area of great concern is new strains of microbial organism and the spread of infectious diseases that requires rapid identification and detection in vivo and in vitro. Another area of major concern, worldwide, is the threat of chemical and biological terrorism. This points out onto necessity of improovement of existing and development of novel detection technologies based on nanomaterials. Nanophotonics-based sensors utilizing nanostructured multiple probes provide the ability for simultaneous detection of different biomedical and ecological objects as well as the ability for remote sensing where necessary. A useful future approach can utilize nanoscale optoelectronics with hybrid detection methods involving both photonics and electronics.

While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well as active behavior states, using data acquired from a Signal Solutions (Lexington, KY) piezoelectric cage floor system. The NREM/REM classification system presented an 81% agreement with human EEG scorers, indicating a useful, high throughput alternative to the widely used EEG acquisition method.

Stroke is the largest cause of severe adult complex disability, caused when the blood supply to the brain is interrupted, either by a clot or a burst blood vessel. It is characterised by deficiencies in movement and balance, changes in sensation, impaired motor control and muscle tone, and bone deformity. Clinically applied stroke management relies heavily on the observational opinion of healthcare workers. Despite the proven validity of a few clinical outcome measures, they remain subjective and inconsistent, and suffer from a lack of standardisation. Motion capture of the upper limb has also been used in specialised laboratories to obtain accurate and objective information, and monitor progress in rehabilitation. However, it is unsuitable in environments that are accessible to stroke patients (for example at patients’ homes or stroke clubs), due to the high cost, special set-up and calibration requirements. The aim of this research project was to validate and assess the sensitivity of a relatively low cost, wearable, compact and easy-to-use monitoring system, which uses inertial sensors in order to obtain detailed analysis of the forearm during simple functional exercises, typically used in rehabilitation. Forearm linear and rotational motion were characterised for certain movements on four healthy subjects and a stroke patient using a motion capture system. This provided accuracy and sensitivity specifications for the wearable monitoring system. With basic signal pre-processing, the wearable system was found to report reliably on acceleration, angular velocity and orientation, with varying degrees of confidence. Integration drift errors in the estimation of linear velocity were unresolved. These errors were not straightforward to eliminate due to the varying position of the sensor accelerometer relative to gravity over time. The cyclic nature of rehabilitation exercises was exploited to improve the reliability of velocity estimation with model-based Kalman filtering, and least squares optimisation techniques. Both signal processing methods resulted in an encouraging reduction of the integration drift in velocity. Improved sensor information could provide a visual display of the movement, or determine kinematic quantities relevant to the exercise performance. Hence, the system could potentially be used to objectively inform patients and physiotherapists about progress, increasing patient motivation and improving consistency in assessment and reporting of outcomes.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Recently, automation systems have been widely investigated. Nowadays, they are present in our lives when shopping, banking, working at home or office. Technology innovations have been increased and embedded into medical and biological equipments, where patients can be better monitored for treatment and diagnosis. These allow precise and ergonomic equipments be designed, especially when using wireless sensor networks. It is developed in this work a biomedical signal acquisition system by suing a wireless sensor network and the Zigbee technology for communication. It was implemented a system for acquiring and processing biomedical data by using commercial sensor modules for wireless communication to a host computer. Also, it was developed a graphical interface in order to manage the sensors of the network and to display the acquired signals to the user. This work has integrated there types of sensors, such as blood oxygenation, heart rate and body temperature. The sensors were chosen due to their easy accessibility and by the fact these type of signals are the most monitored in medicine. Performance tests of sensors network were made to investigate the transmission, reception and data visualization, as well as the communication distance. Also, signal acquisitions were performed in 3 healthy volunteers aged 28, 25 and 65 and the results were compared with the signals acquired by commercial equipments. The results showed that the performance of the blood oxygenation sensor was similar for the three volunteers when compared to the commercial systems. On the other hand, the measured heartbeat by the proposed system showed a greater variation. The body temperature sensor showed reliable readings with a maximum error of approximately 2%. The communication distance of the network was approximately 13 meters in an environment with walls and without the use of routers. It can be concluded that the use of Zigbee sensor network for monitoring bioelectrical signals can be easily implemented and embedded to medical equipments due to its great flexibility when compared to systems which use wired technologies.
A automação de sistemas vem se disseminando muito nos últimos anos, estando presente em nosso dia a dia quando fazemos compras, vamos ao banco ou mesmo estando em nossas casas ou trabalho. Neste contexto vem crescendo o número de oportunidades de se inserir novas tecnologias e automação também na área da medicina, onde o monitoramento de pacientes torna diagnósticos mais fáceis, precisos e ergonômicos, principalmente quando utilizamos redes de transmissão de dados sem fios. Neste trabalho foi desenvolvido um sistema de aquisição de sinais biomédicos sem fio em uma rede de sensores utilizando comunicação Zigbee. Foi implementado uma plataforma de aquisição e processamento de dados biomédicos, utilizando módulos sensores de comunicação sem fio com um computador. Também, um software foi desenvolvido para gerenciar os dispositivos presentes na rede e visualizar os sinais adquiridos ao usuário. Este trabalho integrou sensores de oxigenação do sangue, batimentos cardíacos e temperatura corporal, os quais foram escolhidos por serem considerados sinais vitais de fácil acesso. Testes de desempenho da rede de sensores foram realizados a fim de verificar a transmissão, recepção e visualização dos dados, bem como a distância de comunicação. Também, aquisição de sinais foram realizados em 3 voluntários saudáveis com idades de 28, 25 e 65 anos e os resultados foram comparados com os sinais adquiridos por equipamentos comerciais. Os resultados obtidos mostraram que o sensor de oxigenação do sangue apresentou desempenho similar para os 3 voluntários quando comparados ao sistema comercial. O sensor de batimentos cardíacos apresentou maior variação entre os valores médios pelo sistema proposto. O sensor de temperatura corporal apresentou leituras com um erro sistêmico de aproximadamente 2%. A utilização do protocolo de comunicação Zigbee em uma rede de sensores biomédicos permitiu o monitoramento contínuo de pacientes com maior flexibilidade de uso quando comparado a sistemas convencionais com tecnologias com fios. O alcance da rede chegou a aproximadamente 13 metros em um ambiente com paredes, sem o uso de roteadores. Outros sinais podem ser facilmente adicionados ao sistema e monitorados pela rede de sensores.

In this research, a proton exchange membrane fuel cell (PEMFC) sensor was investigated for specific detection of volatile organic compounds (VOCs) for point-of-care (POC) diagnosis of the physiological conditions of humans. A PEMFC is an electrochemical transducer that converts chemical energy into electrical energy. A Redox reaction takes place at its electrodes whereas the volatile biomolecules (e.g. ethanol) are oxidized at the anode and ambient oxygen is reduced at the cathode. The compounds which were the focus of this investigation were ethanol (C2H5OH) and isoflurane (C3H2ClF5O), but theoretically, the sensor is not limited to only those VOCs given proper calibration. Detection in biosensing, which needs to be carried out in a controlled system, becomes complex in a multivariate environment. Major limitations of all types of biosensors would include poor selectivity, drifting, overlapping, and degradation of signals. Specific detection of VOCs in multi-dimensional environments is also a challenge in fuel cell sensing. Humidity, temperature, and the presence of other analytes interfere with the functionality of the fuel cell and provide false readings. Hence, accurate and precise quantification of VOC(s) and calibration are the major challenges when using PEMFC biosensor. To resolve this problem, a statistical model was derived for the calibration of PEMFC employing multivariate analysis, such as the “Principal Component Regression (PCR)” method for the sensing of VOC(s). PCR can correlate larger data sets and provides an accurate fitting between a known and an unknown data set. PCR improves calibration for multivariate conditions as compared to the overlapping signals obtained when using linear (univariate) regression models. Results show that this biosensor investigated has a 75% accuracy improvement over the commercial alcohol breathalyzer used in this study when detecting ethanol. When detecting isoflurane, this sensor has an average deviation in the steady-state response of ~14.29% from the gold-standard infrared spectroscopy system used in hospital operating theaters. The significance of this research lies in its versatility in dealing with the existing challenge of the accuracy and precision of the calibration of the PEMFC sensor. Also, this research may improve the diagnosis of several diseases through the detection of concerned biomarkers.

Este trabalho descreve o desenvolvimento de um equipamento de biofeedback para utilização por fisioterapeutas no tratamento de pacientes portadores de pé equino. Normalmente o pé equino está relacionado a uma das sequelas de um acidente vascular cerebral e é caracterizado pela dificuldade no controle motor do membro afetado, muitas vezes apresentando limitações relacionadas à sensibilidade e dificuldade em manter o calcanhar em contato com o solo durante a marcha. O equipamento foi construído com base no conceito de biofeedback, apresentando ao paciente, em tempo real, informações relacionadas à intensidade de força aplicada nos membros inferiores. O equipamento é composto de sensores de força flexíveis alojados em um par de sandálias, ligados a um circuito eletrônico microcontrolado. Todo o processamento é realizado por um MSP430, e a comunicação é feita por sinais de rádio através de um módulo CC1101. Os dados são apresentados em uma interface de software em um computador portátil a uma taxa de atualização de 20 Hz. O software possui diversas funcionalidades para atender tanto as necessidades dos fisioterapeutas quanto dos pacientes. Do ponto de vista do paciente, a interface apresenta um visual simples e objetivo, facilitando o entendimento das variações de força, apresentadas na forma de barras verticais. Foram utilizados também recursos visuais e sonoros para enfatizar aos pacientes quando os objetivos estipulados para seu tratamento foram atingidos. Outras funcionalidades foram desenvolvidas para que os fisioterapeutas tenham a opção de customizar a interface de biofeedback de acordo com a necessidade de cada paciente, além de possibilitar o armazenamento de todos os dados dos sensores para análises posteriores. O protótipo foi testado em 20 voluntários durante 10 sessões de fisioterapia, sendo 10 portadores da disfunção pé equino e outros 10 normais para compor um grupo de controle. O objetivo principal do protocolo fisioterapêutico foi melhorar a manutenção do contato como o solo no calcanhar afetado. Os valores de pressão no calcanhar afetado de todas as das sessões foram adquiridos a uma taxa de 20 Hz, armazenados e analisados segundo as seguintes métricas: média, máximos e somas acumuladas. Também foi realizada a avaliação de Berg para todos os voluntários. Os resultados demonstraram que, para as métricas analisadas e também para a avaliação de Berg, houve uma evolução em todos os pacientes.
This work describes the development of a biofeedback device to be used by physiotherapists during treatment of patients with equinus foot. Normally the equinus foot is related to one of the sequelae of a stroke and is characterized by difficulty in motor control of the affected limb, often resulting in limitations on sensitivity. The equipment was built on the concept of biofeedback, presenting to the patient, in real time, information related to the intensity of the forces applied to the lower limbs. The equipment consists of flexible force sensors accommodated in a pair of sandals, connected to a microcontrolled electronic circuit. The whole processing is performed by an MSP430, and the communication is done by radio signals through a CC1101 module. The data is presented in a software interface on a laptop computer at a refresh rate of 20 Hz. The software has many features to meet both the needs of physiotherapists as patients. From the point of view of patients, the interface was designed to be simple and objective, facilitating the understanding of force variations that change dynamically at the exact moment when the sensors are pressed. Visual signs and sound were also used to emphasize to patients when the goals established for their treatment has been reached. Other features were developed specifically for physicaltherapists, giving them the option to customize the biofeedback interface according to the needs of each patient, and also allowing the storage of all data transferred by the sensors for further analysis. A prototype was developed and tested on 20 volunteers over 10 sessions of physical therapy, 10 patients with equinus foot dysfunction, and 10 normal people to compose a control group. At the end of the treatment period, the data were analyzed by session, according to the average forces, the maximum values reached, the accumulated sums and Berg assessments. The results revealed an evolution in all patients, some of them in a greater degree and others in lower degree of progress.

Fluorescence microscopy has revolutionized medicine and biological science with its ability to study the behavior and chemical expressions of living cells. Fluorescent probes can label cell components or cells of a particular type. Clinically the impact of fluorescence imaging can be seen in the diagnosis of cancers, AIDS, and other blood related disorders. Although fluorescence imaging devices have been established as a vital tool in medicine, the size, cost, and complexity of fluorescence microscopes limits their use to central laboratories. The work described in this thesis overcomes these limitations by developing a low cost integrated fluorescence microscope so single use fluorescence microscopy assays can be developed. These assays will enable at-home testing, diagnostics in resource limited settings, and improved emergency medicine.

abstract: Research on developing new algorithms to improve information on brain functionality and structure is ongoing. Studying neural activity through dipole source localization with electroencephalography (EEG) and magnetoencephalography (MEG) sensor measurements can lead to diagnosis and treatment of a brain disorder and can also identify the area of the brain from where the disorder has originated. Designing advanced localization algorithms that can adapt to environmental changes is considered a significant shift from manual diagnosis which is based on the knowledge and observation of the doctor, to an adaptive and improved brain disorder diagnosis as these algorithms can track activities that might not be noticed by the human eye. An important consideration of these localization algorithms, however, is to try and minimize the overall power consumption in order to improve the study and treatment of brain disorders. This thesis considers the problem of estimating dynamic parameters of neural dipole sources while minimizing the system's overall power consumption; this is achieved by minimizing the number of EEG/MEG measurements sensors without a loss in estimation performance accuracy. As the EEG/MEG measurements models are related non-linearity to the dipole source locations and moments, these dynamic parameters can be estimated using sequential Monte Carlo methods such as particle filtering. Due to the large number of sensors required to record EEG/MEG Measurements for use in the particle filter, over long period recordings, a large amounts of power is required for storage and transmission. In order to reduce the overall power consumption, two methods are proposed. The first method used the predicted mean square estimation error as the performance metric under the constraint of a maximum power consumption. The performance metric of the second method uses the distance between the location of the sensors and the location estimate of the dipole source at the previous time step; this sensor scheduling scheme results in maximizing the overall signal-to-noise ratio. The performance of both methods is demonstrated using simulated data, and both methods show that they can provide good estimation results with significant reduction in the number of activated sensors at each time step.
Dissertation/Thesis
M.S. Electrical Engineering 2012

abstract: Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications.
Dissertation/Thesis
M.S. Electrical Engineering 2013

碩士
國立高雄應用科技大學
電子與資訊工程研究所碩士班
96
In this thesis, we developed the wireless sensor network (WSN) for biomedical signals monitoring system using ZigBee. Because ZigBee is provided with lower power consumption, small volume, high expansion, and two-way transmission, etc. It is generally adopted to the communication network and control fields for home care, digital home, industrial, and security control, etc. This WSN system is implemented biomedical signal measurement module in the client, which can measure blood pressure (BP), heart rate (HR), electrocardiogram (ECG), and electroencephalogram (EEG), etc. The output of the biomedical signal measurement modules are connected and transmitted to ZigBee wireless transmission module. The measured biomedical signal is transmitted to the received system which is attached to the ZigBee module. The received system can receive, store and monitor biomedical signals. It is also applied radio frequency identification (RFID) to maintain medical case data. It can provide the remote wireless mobile network system to the monitor, analyze, and alarm functions for measured biomedical signals. The system can be extended to hospitals and home care observers. It is integrated for the ubiquitous monitor applications via the wireless mobile remote network system.

The last two decades have shown a boom in the field of wearable sensing technology. Particularly in the consumer industry, growing trends towards personalized health have pushed new devices to report many vital signs, with a demand for high accuracy and reliability. The most common technique used to gather these vitals is photoplethysmography or PPG. PPG devices are ideal for wearable applications as they are simple, power-efficient, and can be implemented on almost any area of the body. Traditionally PPGs were utilized for capturing just heart rate, however, recent advancements in hardware and digital processing have led to other metrics including respiratory rate (RR) and peripheral oxygen saturation (SpO2), to be reported as well. Our research investigates the potential for wearable devices to be used for outpatient apnea monitoring, and particularly the ability to detect opioid misuse resulting in respiratory depression. Ultimately, the long-term goal of this work is to develop a wearable device that can be used in the rehabilitation process to ensure both accountability and safety of the wearer. This document details contributions towards this goal through the design, development, and evaluation of a device called “Kick Ring”. Primarily, we investigate the ability of Kick Ring to record heartrate (HR), RR, and SpO2. Moreover, we show that the device can calculate RR in real time and can provide an immediate indication of abnormal events such as respiratory depression. Finally, we explore a novel method for reporting apnea events through the use of several PPG characteristics. Kick Ring reliably gathers respiratory metrics and offers a combination of features that does not exist in the current wearables space. These advancements will help to move the field forward, and eventually aid in early detection of life-threatening events.