A Low-Cost Algorithm to Find the Minimum Sampling Frequency for Multiple Bandpass Signals

Abstract-We consider the problem of finding the minimum sampling frequency required for non-overlapping, bandpass signals. Recently a novel algorithm with a significantly reduced computational cost has been proposed for this problem. By exploiting a redundancy in this algorithm, we propose a method which further reduces the cost significantly. We use the fact that a valid sampling frequency for a set of bandpass signals must be a valid sampling frequency for any subset of the signals. Several examples are given to illustrate the savings in the computational cost achieved

Multifrequency Aperture-Synthesizing Microwave Radiometer System (MFASMR). Volume 1

Background material and a systems analysis of a multifrequency aperture - synthesizing microwave radiometer system is presented. It was found that the system does not exhibit high performance because much of the available thermal power is not used in the construction of the image and because the image that can be formed has a resolution of only ten lines. An analysis of image reconstruction is given. The system is compared with conventional aperture synthesis systems

Single Epoch Analysis and Bi-hemisphere Study of Magnetoencephalographic (MEG) Signals using Vector Signal Transformation V3 and Magnetic Field Tomography (MFT)

The biomagnetic inverse problem has no unique solution, nevertheless even a cursory look at the features shown in raw signal can often suffice to highlight strong superficial activity. To do a proper single epoch analysis is normally prohibitively expensive in terms of computing demands. Hence the original aim of this thesis was to use simple efficient signal transformations to characterize superficial generators and contrast the single epoch signature with that extracted from the average signal. The results have intrigued us sufficiently to go beyond the original goal and extract very preliminary estimates of activity across the cerebral hemisphere in single trials. The original tool, and one that we have used for much of the work, is a simple vector signal transformation called V3. This signal transformation highlights nearby sources; it is a crude but quick estimator of generators directly from the raw MEG signals. Together with Magnetic Field Tomography (MFT), which relies on distributed source analysis of the MEG signals, we have tackled the following specific problems relating to aspects of normal brain function: efficient estimation of generators of magnetic fields; relationship between the average signal and single trials; and interhemispheric differences and relationship between the activity in the left and right hemispheres of the brain. During the project, we have used as examples auditory evoked MEG measurements obtained from two multichannel systems and applied the V3 and MFT analysis to both the average and single trial signals. In particular, we chose the 40-Hz (or gamma band) auditory response as the study subject. We found that in single epochs similar patterns of high frequency activity are observed in the area around the auditory cortex well before, close to and well after stimulus onset; the sequence of events observed in the average can only represent the evolution of events in single trials in a statistical way; and deep and central areas of the brain may be the seeds for the main deflections observed in the auditory responses

A Novel Electrocardiogram Segmentation Algorithm Using a Multiple Model Adaptive Estimator

This thesis presents a novel electrocardiogram (ECG) processing algorithm design based on a Multiple Model Adaptive Estimator (MMAE) for a physiological monitoring system. Twenty ECG signals from the MIT ECG database were used to develop system models for the MMAE. The P-wave, QRS complex, and T-wave segments from the characteristic ECG waveform were used to develop hypothesis filter banks. By adding a threshold filter-switching algorithm to the conventional MMAE implementation, the device mimics the way a human analyzer searches the complex ECG signal for a useable temporal landmark and then branches out to find the other key wave components and their timing. The twenty signals and an additional signal from an animal exsanuinaiton experiment were then used to test the algorithm. Using a conditional hypothesis-testing algorithm, the MMAE correctly identified the ECG signal segments corresponding to the hypothesis models with a 96.8% accuracy-rate for the 11539 possible segments tested. The robust MMAE algorithm also detected any misalignments in the filter hypotheses and automatically restarted filters within the MMAE to synchronize the hypotheses with the incoming signal. Finally, the MMAE selects the optimal filter bank based on incoming ECG measurements. The algorithm also provides critical heart-related information such as heart rate, QT, and PR intervals from the ECG signal. This analyzer could be easily added as a software update to the standard physiological monitors universally used in emergency vehicles and treatment facilities and potentially saving thousands of lives and reducing the pain and suffering of the injured

ELECTRO-MECHANICAL DATA FUSION FOR HEART HEALTH MONITORING

Heart disease is a major public health problem and one of the leading causes of death worldwide. Therefore, cardiac monitoring is of great importance for the early detection and prevention of adverse conditions. Recently, there has been extensive research interest in long-term, continuous, and non-invasive cardiac monitoring using wearable technology. Here we introduce a wearable device for monitoring heart health. This prototype consists of three sensors to monitor electrocardiogram (ECG), phonocardiogram (PCG), and seismocardiogram (SCG) signals, integrated with a microcontroller module with Bluetooth wireless connectivity. We also created a custom printed circuit board (PCB) to integrate all the sensors into a compact design. Then, flexible housing for the electronic components was 3D printed using thermoplastic polyurethane (TPU). In addition, we developed peak detection algorithms and filtering programs to analyze the recorded cardiac signals. Our preliminary results show that the device can record all three signals in real-time. Initial results for signal interpretation come from a recurrent neural network (RNN) based machine learning algorithm, Long Short-Term Memory (LSTM), which is used to monitor and identify key features in the ECG data. The next phase of our research will include cross-examination of all three sensor signals, development of machine learning algorithms for PCG and SCG signals, and continuous improvement of the wearable device

Bio-Radar Applications for Remote Vital Signs Monitoring

Nowadays, most vital signs monitoring techniques used in a medical context and/or daily life routines require direct contact with skin, which can become uncomfortable or even impractical to be used regularly. Radar technology has been appointed as one of the most promising contactless tools to overcome these hurdles. However, there is a lack of studies that cover a comprehensive assessment of this technology when applied in real-world environments. This dissertation aims to study radar technology for remote vital signs monitoring, more specifically, in respiratory and heartbeat sensing. Two off-the-shelf radars, based on impulse radio ultra-wideband and frequency modu lated continuous wave technology, were customized to be used in a small proof of concept experiment with 10 healthy participants. Each subject was monitored with both radars at three different distances for two distinct conditions: breathing and voluntary apnea. Signals processing algorithms were developed to detect and estimate respiratory and heartbeat parameters, assessed using qualitative and quantitative methods. Concerning respiration, a minimum error of 1.6% was found when radar respiratory peaks signals were directly compared with their reference, whereas a minimum mean absolute error of 0.3 RPM was obtained for the respiration rate. Concerning heartbeats, their expression in radar signals was not as clear as the respiration ones, however a minimum mean absolute error of 1.8 BPM for heartbeat was achieved after applying a novel selective algorithm developed to validate if heart rate value was estimated with reliability. The results proved the potential for radars to be used in respiratory and heartbeat contactless sensing, showing that the employed methods can be already used in some mo tionless situations. Notwithstanding, further work is required to improve the developed algorithms in order to obtain more robust and accurate systems.Atualmente, a maioria das técnicas usadas para a monitorização de sinais vitais em contexto médicos e/ou diário requer contacto direto com a pele, o que poderá tornar-se incómodo ou até mesmo inviável em certas situações. A tecnologia radar tem vindo a ser apontada como uma das mais promissoras ferramentas para medição de sinais vitais à distância e sem contacto. Todavia, são necessários mais estudos que permitam avaliar esta tecnologia quando aplicada a situações mais reais. Esta dissertação tem como objetivo o estudo da tecnologia radar aplicada no contexto de medição remota de sinais vitais, mais concretamente, na medição de atividade respiratória e cardíaca. Dois aparelhos radar, baseados em tecnologia banda ultra larga por rádio de impulso e em tecnologia de onda continua modulada por frequência, foram configurados e usados numa prova de conceito com 10 participantes. Cada sujeito foi monitorizado com cada um dos radar em duas situações distintas: respirando e em apneia voluntária. Algorit mos de processamento de sinal foram desenvolvidos para detetar e estimar parâmetros respiratórios e cardíacos, avaliados através de métodos qualitativos e quantitativos. Em relação à respiração, o menor erro obtido foi de 1,6% quando os sinais de radar respiratórios foram comparados diretamente com os sinais de referência, enquanto que, um erro médio absoluto mínimo de 0,3 RPM foi obtido para a estimação da frequência respiratória via radar. A expressão cardíaca nos sinais radar não se revelou tão evidente como a respiratória, no entanto, um erro médio absoluto mínimo de 1,8 BPM foi obtido para a estimação da frequência cardíaca após a aplicação de um novo algoritmo seletivo, desenvolvido para validar a confiança dos valores obtidos. Os resultados obtidos provaram o potencial do uso de radares na medição de atividade respiratória e cardíaca sem contacto, sendo esta tecnologia viável de ser implementada em situações onde não existe muito movimento. Não obstante, os algoritmos desenvolvidos devem ser aperfeiçoados no futuro de forma a obter sistemas mais robustos e precisos

Sleep studies in mice - open and closed loop devices for untethered recording and stimulation

Sleep is an important biological processes that has been studied extensively to date. Research in sleep typically involves mice experiments that use heavy benchtop equipment or basic neural loggers to record ECoG/EMG signals which are then processed offline in workstations. These systems limit the complexity of experiments that can be carried out to only simple open loop recordings, due to either the tethered setup used, which restricts animal movements, or the lack of devices that can offer more advanced features without compromising its portability. With rising popularity in exploring more physiological features that can affect sleep, such as temperature, whose importance has been highlighted in several papers [1][2][3] and advances in optogenetic stimulation, allowing high temporal and spatial neural control, there is now an unprecedented demand for experimental setups using new closed loop paradigms. To address this, this thesis presents compact and lightweight neural logging devices that are not only capable of measuring ECoG and EMG signals for core sleep analysis but also capable of taking high resolution temperature recordings and delivering optogenetic stimulus with fully adjustable parameters. Together with its embedded on-board automatic sleep stage scoring algorithm, the device will allow researchers for the first time to be able to quickly uncover the role a neural circuit plays in sleep regulation through selective neural stimulation when the animal is under the target sleep vigilance state. Original contributions include: the development of two novel multichannel neural logging devices, one for core sleep analysis and another for closed loop experimentation; the development and implementation of a lightweight, fast and highly accurate automatic on-line sleep stage scoring algorithm; and the development of a custom optogenetic coupler that is compatible with most current optogenetic setups for LED-Optical fibre coupling.Open Acces

A Site-Specific Indoor Wireless Propagation Model

In this thesis, we explore the fundamental concepts behind the emerging field of site-specific propagation modeling for wireless communication systems. The first three chapters of background material discuss, respectively, the motivation for this study, the context of the study, and signal behavior and modeling in the predominant wireless propagation environments. A brief survey of existing ray-tracing based site-specific propagation models follows this discussion, leading naturally to the work of new model development undertaken in our thesis project. Following the detailed description of our generalized wireless channel modeling, various interference cases incorporating with this model are thoroughly discussed and results presented at the end of this thesis

Realization Limits of Impulse-Radio UWB Indoor Localization Systems

In this work, the realization limits of an impulse-based Ultra-Wideband (UWB) localization system for indoor applications have been thoroughly investigated and verified by measurements. The analysis spans from the position calculation algorithms, through hardware realization and modeling, up to the localization experiments conducted in realistic scenarios. The main focus was put on identification and characterization of limiting factors as well as developing methods to overcome them