Sensor de fotopletismografia por reflexão sem fios: projeto e desenvolvimento de hardware

Nos anos oitenta do último século começaram a surguir oxímetros wearable que se estabeleceram como um standart para a monitorização da saturação de oxigénio no sangue e actividade cardíaca, de forma não intrusiva. Os referidos oxímetros medem a percentagem de hemoglobina totalmente saturada com oxigénio (SPO2), transmitindo luz com comprimentos de onda diferentes, vermelha e infra-vermelha, através dos tecidos. Os dispositivos wearable atuais são frequentemente desenhados de forma modular, em que o módulo de medição e de display são integrados num único dispositivo. O armazenamento e tratamento de dados é difícil uma vez que são dispositivos de tamanho reduzido; baixo consumo de energia; baixo custo e baixa capacidade de processamento de dados. Tendo em conta que a quantidade de dados recolhidos é relativamente baixa, a sua transmissão de forma wireless é conveniente. Nesta dissertação é desenvolvido e testado um oxímetro de pulso em modo refletivo capaz de cálcular a saturação de oxigénio no sangue, o batimento cardíaco e enviar os dados de forma wireless para outros dispositivos. O hardware desenvolvido engloba quatro módulos funcionais: fonte de alimentação constituída por um conversor DC-DC e um regulador de tensão linear, circuito de carga e monitorização da bateria que controla os ciclos de carga e descarga da bateria, um módulo de rádio frequência que permite que o oxímetro comunique com outros dispositivos de forma wireless e um microcontrolador responsável por gerir todas as comunicações e pelo processamento de sinal. O sofware desenvolvido divide-se em duas partes: uma interface gráfica escrita em Matlab que permite a comunicação entre o computador e o oxímetro e o firmware do microcontrolador que engloba todos os algoritmos de cálculo do SPO2, do batimento cardíaco, drivers de periféricos, gestão das comunicações e aquisição e processamento dos dados.Ever since the early 80s from the last century, wearable oximeters appear as the established standard for non-invasive monitoring of arterial oxygen saturation (SpO2) and heart activity wearable oximeters can monitor arterial SpO2, which is the percentage of arterial hemoglobin that is fully saturated with oxygen, by transmitting red and infrared light through the finger, where it is sensed. The current wearable oximeters are frequently designed as single modular devices, namely, the measurement and display modules are integrated on a single device, which are responsible for several problems. Such devices lack effective data management functions and by being limited by size, power consumption and cost, advanced operating systems cannot be embedded to such wearable oximeters, making difficult to store and manage data. Bearing in mind that the amount of data pulse wave signal collected is small, transmit it wirelessly is convenient and effective. In this thesis a reflective pulse oximeter is developed and tested capable of assessing the oxygen blood saturation (SpO2), the heart rate and send the acquired data through wireless communication to other devices. The developed hardware comprises four functional modules: the power supply made of a DCDC converter and a linear voltage regulator, the charging circuit and battery monitoring system which controls the charging and discharging cycles of the battery, a radio-frequency module that allows the device to connect through wireless communication to other devices and a microcontroller responsible for the management of the communications and for the signal processing. The software developed in this thesis is made of two parts. One being the Matlab graphical interface that allows the communication between the oximeter and the PC while the other one being the microcontroller which comprises all the algorithms of SpO2, heart rate, management of the communication, drivers, and data acquisition and processing

A Photoplethysmography System Optimised for Pervasive Cardiac Monitoring

Photoplethysmography is a non-invasive sensing technique which infers instantaneous cardiac function from an optical measurement of blood vessels. This thesis presents a photoplethysmography based sensor system that has been developed speci fically for the requirements of a pervasive healthcare monitoring system. Continuous monitoring of patients requires both the size and power consumption of the chosen sensor solution to be minimised to ensure the patients will be willing to use the device. Pervasive sensing also requires that the device be scalable for manufacturing in high volume at a build cost that healthcare providers are willing to accept. System level choice of both electronic circuits and signal processing techniques are based on their sensitivity to cardiac biosignals, robustness against noise inducing artefacts and simplicity of implementation. Numerical analysis is used to justify the implementation of a technique in hardware. Circuit prototyping and experimental data collection is used to validate a technique's application. The entire signal chain operates in the discrete-time domain which allows all of the signal processing to be implemented in firmware on an embedded processor which minimised the number of discrete components while optimising the trade-off between power and bandwidth in the analogue front-end. Synchronisation of the optical illumination and detection modules enables high dynamic range rejection of both AC and DC independent light sources without compromising the biosignal. Signal delineation is used to reduce the required communication bandwidth as it preserves both amplitude and temporal resolution of the non-stationary photoplethysmography signals allowing more complicated analytical techniques to be performed at the other end of communication channel. The complete sensing system is implemented on a single PCB using only commercial-off -the-shelf components and consumes less than 7.5mW of power. The sensor platform is validated by the successful capture of physiological data in a harsh optical sensing environment

A Photoplethysmography System Optimised for Pervasive Cardiac Monitoring

Photoplethysmography is a non-invasive sensing technique which infers instantaneous cardiac function from an optical measurement of blood vessels. This thesis presents a photoplethysmography based sensor system that has been developed speci fically for the requirements of a pervasive healthcare monitoring system. Continuous monitoring of patients requires both the size and power consumption of the chosen sensor solution to be minimised to ensure the patients will be willing to use the device. Pervasive sensing also requires that the device be scalable for manufacturing in high volume at a build cost that healthcare providers are willing to accept. System level choice of both electronic circuits and signal processing techniques are based on their sensitivity to cardiac biosignals, robustness against noise inducing artefacts and simplicity of implementation. Numerical analysis is used to justify the implementation of a technique in hardware. Circuit prototyping and experimental data collection is used to validate a technique's application. The entire signal chain operates in the discrete-time domain which allows all of the signal processing to be implemented in firmware on an embedded processor which minimised the number of discrete components while optimising the trade-off between power and bandwidth in the analogue front-end. Synchronisation of the optical illumination and detection modules enables high dynamic range rejection of both AC and DC independent light sources without compromising the biosignal. Signal delineation is used to reduce the required communication bandwidth as it preserves both amplitude and temporal resolution of the non-stationary photoplethysmography signals allowing more complicated analytical techniques to be performed at the other end of communication channel. The complete sensing system is implemented on a single PCB using only commercial-off -the-shelf components and consumes less than 7.5mW of power. The sensor platform is validated by the successful capture of physiological data in a harsh optical sensing environment

Optical direct detection of thermal vibrations of ultralow stiffness micro-nano structures.

A direct detection optical vibrometer is constructed around an 850 nm laser and a quadrant photodetector (QPD). The limit of detection is 0.2 fW which corresponds to a minimum amplitude of 0.1 Å. The vibrometer is used to measure the thermal vibration spectra of low stiffness micromechanical structures have nanometer features. One structure measured is a cantilevered 30 μm diameter glass fiber. Vibration amplitudes as low as 1.1 Å are measured. The thermal vibration spectra show fundamental resonances at 80-250 Hz and a signal to noise ratio (SNR) of 23-55 dB. Young’s modulus of glass in the cantilevers, estimated from the spectra, agree to within 3 % of the manufacturer’s value, which is somewhat more accurate than force-elongation measurements made of 50-100 mm long fibers which differ by 5 %. Mass changes due to adhering small drops of liquids to the tip of the fiber cantilevers shifts the resonant frequency with a sensitivity of 120 ng. The mass detection limit would decrease by 2-3 orders by increasing the length of the time series data. The intended purpose of the vibrometer development is the measurement of the thermal vibration of polymer bead-on-string (BOS) fibers with enough sensitivity to detect time-varying changes in the spectra that relate to molecular-level and temperature dependent changes, such as evaporation, solidification, crystallization and strain-dependent chain reorganizations of the polymer material. Time dependent variations in the BOS spectra are observed in vibrometer measurements that, if attributable to material properties, would represent 2.5-5.2 % change in elastic modulus, 20-40 % loss in water mass due to evaporation, with the minimum detectable change in these properties being 0.06 % for the measured spectra. The vibrometer provides an important tool for the real-time study of changing properties of BOS fibers, as well as other low stiffness microstructures, especially those composed of polymers and other soft mater

The 2nd International Electronic Conference on Applied Sciences

This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network

Ultra-Low-Power Sensors and Receivers for IoT Applications

The combination of ultra-low power analog front-ends and CMOS-compatible transducers enable new applications, such as environmental monitors, household appliances, health trackers, etc. that are seamlessly integrated into our daily lives. Furthermore, wireless connectivity allows many of these sensors to operate both independently and collectively. These techniques collectively fulfil the recent surge of internet-of-things (IoT) applications that have the potential to fundamentally change daily life for millions of people.In this dissertation, the circuit and system design of wireless receivers and sensors is presented that explores the challenges of implementing long lifespan, high accuracy, and large coverage range IoT sensor networks. The first is a wake-up receiver (WuRX), which continuously monitors the RF environment to wake up a higher-power radio upon detection of a predetermined RF signature. This work both improves sensitivity and reduces power over prior art through a multi-faceted design featuring an impedance transformation network with large passive voltage gain, an active envelope detector with high input impedance to facilitate large passive voltage gain, a low-power precision comparator, and a low-leakage digital baseband correlator.Although pushing the prior WuRX performance boundary by orders of magnitude, the first work shows moderate sensitivity, inferior temperature robustness, and large area with external lumped components. Thus, the second work shows a miniaturized WuRX that is temperature-compensated, yet still consumes only nano-watt power and millimeter area while operating at 9 GHz. To further reduce the area, a global common-mode feedback is utilized across the envelope detector and baseband amplifier that eliminates the need for off-chip ac-coupling components. Multiple temperature-compensation techniques are proposed to maintain constant bandwidth of the signal path and constant clock frequency. Both WuRXs operate at 0.4 V supply, consume near-zero power and achieve ~-70 dBm sensitivity.Lastly, the first reported CMOS 2-in-1 relative humidity and temperature sensor is presented. A unified analog front-end interfaces on-chip transducers and converts the inputs into a frequency vis a high-linearity frequency-locked loop. An incomplete-settling switched-capacitor-based Wheatstone bridge is proposed to sense the inputs in a power-efficient fashion

Study of the effect of Remote Ischaemic Preconditioning (RIPC) on the early and late phase of hepatic ischaemia reperfusion injury and the role of haemoxygenase in RIPC.

Reperfusion following ischaemia results in endothelial and parenchymal injury through a complex cascade of events. This often occurs in human liver transplantation as well as with major liver resections and is referred to as Ischaemia Reperfusion Injury (IRI). Ischaemic Preconditioning (IPC) is an adaptive response in which tolerance to prolonged ischaemia is induced in a target organ by prior brief periods of ischaemia. Benefits of IPC have been demonstrated in experimental models and in preliminary human clinical trials. In remote ischaemic preconditioning (RIPC) brief ischaemia involves a remote organ. RIPC has been demonstrated to reduce warm liver I/R injury in an experimental model by our research group and clinical evaluation is ongoing. The effect of RIPC on the late phase of I/R and its mechanism have not been investigated. This thesis evaluates the effect of RIPC on both the early and late phases of liver warm I/R injury with the hypothesis that beneficial effects are induced by haemoxygenase- 1(HO-1), a free radical scavenger which is involved in degradation of haem and production of the vasodilator CO. Male Sprague Dawley rats were subjected to 45 mins of partial hepatic (70 %) ischaemia followed by 3 hrs of reperfusion to investigate the early phase of hepatic IR and 24 hrs of reperfusion to study the late phase of hepatic IR. RIPC was performed with four cycles of 5 min ischaemia and 5 min reperfusion of the right hind limb before sustained ischaemia. Pyrrolidine dithiocarbamate (PDTC) and Zinc Protoporrphyrrin (ZnPP) were administered to induce and block haem oxygenase synthesis. Changes to the microcirculation, leucocyte adherence and apoptosis were assessed by intra-vital microscopy. Hepatocellular injury was assessed by standard liver function tests. HO-1 protein was demonstrated by immunohistochemistry (IHC) and measured by Western blot. RIPC improved liver sinusoid perfusion, reduced leucocyte adherence and apoptosis in both the early and late phases of IRI. Hepatocellular injury was reduced. RIPC increased HO-1 production in the liver, particularly in hepatic macrophages, as demonstrated by IHC. PDTC treatment (HO-1 inducer) reproduced the protective effect of RIPC whereas HO-1 inhibition with ZnPP abolished the protective effect. The response to HO-1 induction and inhibition indicate that HO-1 has a key role in the protective effect of RIPC. Establishing the inducing agent for HO-1 may lead to new pharmacological approaches to preconditioning and the protection of the liver from IR injury. Studies on RIPC and liver warm I/R using HO-1 knockout mice would clarify the pathways involved in RIPC

C-Trend parameters and possibilities of federated learning

Abstract. In this observational study, federated learning, a cutting-edge approach to machine learning, was applied to one of the parameters provided by C-Trend Technology developed by Cerenion Oy. The aim was to compare the performance of federated learning to that of conventional machine learning. Additionally, the potential of federated learning for resolving the privacy concerns that prevent machine learning from realizing its full potential in the medical field was explored. Federated learning was applied to burst-suppression ratio’s machine learning and it was compared to the conventional machine learning of burst-suppression ratio calculated on the same dataset. A suitable aggregation method was developed and used in the updating of the global model. The performance metrics were compared and a descriptive analysis including box plots and histograms was conducted. As anticipated, towards the end of the training, federated learning’s performance was able to approach that of conventional machine learning. The strategy can be regarded to be valid because the performance metric values remained below the set test criterion levels. With this strategy, we will potentially be able to make use of data that would normally be kept confidential and, as we gain access to more data, eventually develop machine learning models that perform better. Federated learning has some great advantages and utilizing it in the context of qEEGs’ machine learning could potentially lead to models, which reach better performance by receiving data from multiple institutions without the difficulties of privacy restrictions. Some possible future directions include an implementation on heterogeneous data and on larger data volume.C-Trend-teknologian parametrit ja federoidun oppimisen mahdollisuudet. Tiivistelmä. Tässä havainnointitutkimuksessa federoitua oppimista, koneoppimisen huippuluokan lähestymistapaa, sovellettiin yhteen Cerenion Oy:n kehittämään C-Trend-teknologian tarjoamaan parametriin. Tavoitteena oli verrata federoidun oppimisen suorituskykyä perinteisen koneoppimisen suorituskykyyn. Lisäksi tutkittiin federoidun oppimisen mahdollisuuksia ratkaista yksityisyyden suojaan liittyviä rajoitteita, jotka estävät koneoppimista hyödyntämästä täyttä potentiaaliaan lääketieteen alalla. Federoitua oppimista sovellettiin purskevaimentumasuhteen koneoppimiseen ja sitä verrattiin purskevaimentumasuhteen laskemiseen, johon käytettiin perinteistä koneoppimista. Kummankin laskentaan käytettiin samaa dataa. Sopiva aggregointimenetelmä kehitettiin, jota käytettiin globaalin mallin päivittämisessä. Suorituskykymittareiden tuloksia verrattiin keskenään ja tehtiin kuvaileva analyysi, johon sisältyi laatikkokuvioita ja histogrammeja. Odotetusti opetuksen loppupuolella federoidun oppimisen suorituskyky pystyi lähestymään perinteisen koneoppimisen suorituskykyä. Menetelmää voidaan pitää pätevänä, koska suorituskykymittarin arvot pysyivät alle asetettujen testikriteerien tasojen. Tämän menetelmän avulla voimme ehkä hyödyntää dataa, joka normaalisti pidettäisiin salassa, ja kun saamme lisää dataa käyttöömme, voimme lopulta kehittää koneoppimismalleja, jotka saavuttavat paremman suorituskyvyn. Federoidulla oppimisella on joitakin suuria etuja, ja sen hyödyntäminen qEEG:n koneoppimisen yhteydessä voisi mahdollisesti johtaa malleihin, jotka saavuttavat paremman suorituskyvyn saamalla tietoja useista eri lähteistä ilman yksityisyyden suojaan liittyviä rajoituksia. Joitakin mahdollisia tulevia suuntauksia ovat muun muassa heterogeenisen datan ja suurempien tietomäärien käyttö

Deep Learning Algorithms for Time Series Analysis of Cardiovascular Monitoring Systems

This thesis investigates and develops methods to enable ubiquitous monitoring of the most examined cardiovascular signs, blood pressure, and heart rate. Their continuous measurement can help improve health outcomes, such as the detection of hypertension, heart attack, or stroke, which are the leading causes of death and disability. Recent research into wearable blood pressure monitors sought predominately to utilise a hypothesised relationship with pulse transit time, relying on quasiperiodic pulse event extractions from photoplethysmography local signal characteristics and often used only a fraction of typically bivariate time series. This limitation has been addressed in this thesis by developing methods to acquire and utilise fused multivariate time series without the need for manual feature engineering by leveraging recent advances in data science and deep learning methods that showed great data analysis potential in other domains

Sleep dependent memory consolidation in mild cognitive impairment subtypes

Sleep plays a crucial role in the overnight consolidation of newly learnt information in young adults, however the sleep-memory relationship in older adults is less understood. Age-associated memory decline as well as sleep disturbances are a concern for up to 60% of older people. Greater non-rapid eye movement (NREM) sleep neurophysiology such as slow waves and spindles have been postulated to be important for overnight memory consolidation, however, these associations are unclear in those at greater risk of dementia, namely in Mild Cognitive Impairment (MCI). Furthermore, it is unclear whether structural brain integrity for regions important for sleep and memory in ageing such as the hippocampus and medial prefrontal cortex, are associated with OMC in this ‘at-risk’ population. The overall aims of this study were to determine if there are differences in memory consolidation in older adults with and without MCI (and their subtypes), and examine associations between overnight memory consolidation with NREM sleep neurophysiology, and structural brain integrity using neuroimaging. Using a 256-channel high density EEG and a novel task of spatial navigation memory, the implications of these findings speak to the design of clinical trials targeting sleep in older adults, to determine the impact and functions of sleep as a modifiable risk factor for cognitive decline