Pervasive blood pressure monitoring using Photoplethysmogram (PPG) Sensor

Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95\%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.N/

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

Photoplethysmography in noninvasive cardiovascular assessment

The electro-optic technique of measuring the cardiovascular pulse wave known as photoplethysmography (PPG) is clinically utilised for noninvasive characterisation of physiological components by dynamic monitoring of tissue optical absorption. There has been a resurgence of interest in this technique in recent years, driven by the demand for a low cost, compact, simple and portable technology for primary care and community-based clinical settings, and the advancement of computer-based pulse wave analysis techniques. PPG signal provides a means of determining cardiovascular properties during the cardiac cycle and changes with ageing and disease. This thesis focuses on the photoplethysmographic signal for cardiovascular assessment. The contour of the PPG pulse wave is influenced by vascular ageing. Contour analysis of the PPG pulse wave provides a rapid means of assessing vascular tone and arterial stiffness. In this thesis, the parameters extracted from the PPG pulse wave are examined in young adults. The results indicate that the contour parameters of the PPG pulse wave could provide a simple and noninvasive means to study the characteristic change relating to arterial stiffness. The pulsatile component of the PPG signal is due to the pumping action of the heart, and thus could reveal the circulation changes of a specific vascular bed. Heart rate variability (HRV) represents one of the most promising quantitative markers of cardiovascular control. Calculation of HRV from the peripheral pulse wave using PPG, called pulse rate variability (PRV), is investigated. The current work has confirmed that the PPG signal could provide basic information about heart rate (HR) and its variability, and highly suggests a good alternative to understanding dynamics pertaining to the autonomic nervous system (ANS) without the use of an electrocardiogram (ECG) device. Hence, PPG measurement has the potential to be readily accepted in ambulatory cardiac monitoring due to its simplicity and comfort. Noncontact PPG (NPPG) is introduced to overcome the current limitations of contact PPG. As a contactless device, NPPG is especially attractive for physiological monitoring in ambulatory units, NICUs, or trauma centres, where attaching electrodes is either inconvenient or unfeasible. In this research, a prototype for noncontact reflection PPG (NRPPG) with a vertical cavity surface emitting laser (VCSEL) as a light source and a high-speed PiN photodiode as a photodetector is developed. The results from physiological experiments suggest that NRPPG is reliable to extract clinically useful information about cardiac condition and function. In summary, recent evidence demonstrates that PPG as a simple noninvasive measurement offers a fruitful avenue for noninvasive cardiovascular monitoring. Key words: Photoplethysmography (PPG), Cardiovascular assessment, Pulse wave contour analysis, Arterial stiffness, Heart rate (HR), Heart rate variability (HRV), Pulse rate variability (PRV), Autonomic nervous system (ANS), Electrocardiogram (ECG).EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Long-term monitoring of respiratory metrics using wearable devices

Recently, there has been an increased interest in monitoring health using wearable sensors technologies however, few have focused on breathing. The utility of constant monitoring of breathing is currently not well understood, both for general health as well as respiratory conditions such as asthma and chronic obstructive pulmonary disease (COPD) that have significant prevalence in society. Having a wearable device that could measure respiratory metrics continuously and non-invasively with high adherence would allow us to investigate the significance of ambulatory breathing monitoring in health and disease management. The purpose of this thesis was to determine if it was feasible to continuously monitor respiratory metrics. To do this, we identified pulse oximetry to provide the best balance between use of mature signal processing methods, commercial availability, power efficiency, monitoring site and perceived wearability. Through a survey, it was found users would monitor their breathing, irrespective of their health status using a smart watch. Then it was found that reducing the duty cycle and power consumption adversely affected the reliability to capture accurate respiratory rate measurements through pulse oximetry. To account for the decreased accuracy of PPG derived respiratory rate at higher rates, a long short-term memory (LSTM) network and a U-Net were proposed, characterised and implemented. In addition to respiratory rate, inspiration time, expiration time, inter-breath intervals and the Inspiration:Expiration ratio were also predicted. Finally, the accuracy of these predictions was validated using pilot data from 11 healthy participants and 11 asthma participants. While percentage bias was low, the 95\% limits of agreement was high. While there is likely going to be enthusiastic uptake in wearable device use, it remains unseen whether clinical utility can be achieved, in particular the ability to forecast respiratory status. Further, the issues of sensor noise and algorithm performance during activity was not calculated. However, this body of work has investigated and developed the use of pulse oximetry, classical signal processing and machine learning methodologies to extract respiratory metrics to lay a foundation for both the hardware and software requirements in future clinical research

Brain Tumour Classification Using Deep Features from Structural MRI

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Development of a new electromechanical probe for hemodynamic parameters assessment

Dissertação de Mestrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.As doenças cardiovasculares (DCVs) causam milhões de mortes todos os anos, sendo a principal causa de morte no mundo inteiro. A hipertensão é um dos mais relevantes factores de risco das doenças cardiovasculares. Assim sendo, é muito importante o desenvolvimento de um método de diagnóstico que seja barato, fácil utilização, preciso e capaz de detectar alterações precoces da performance do sistema cardiovascular, permitindo, desta forma, aumentar a probabilidade de sobrevivência. A análise da forma de onda da pressão sanguínea central fornece informações clínicas relevantes uma vez que patologias cardiovasculares alteram a sua forma de onda. Este projecto de investigação foca-se no desenvolvimento de um novo sensor hemodinâmico não-invasivo que integra um sensor piezoeléctrico e um acelerómetro ligados a um circuito demodulador. O sensor acessa a forma de onda da pressão sanguínea, simulada através das bancadas de teste desenvolvidas ao longo deste projecto. Numa fase inicial, os sinais resultantes são adquiridos recorrendo á utilização dos módulos de aquisição USB NI-6008 ou USB NI-6210 associado a um gerador arbitrário de formas de onda (Agilent), a uma fonte de alimentação e a um computador. Numa fase posterior foi utilizado um dispositivo multifuncional capaz gerar, guardar, converter, medir e analisar sinais analógicos e digitais (Digilent) e um computador. Algoritmos capazes de processar os sinais foram desenvolvidos utilizando o Matlab. Os resultados das avaliações da performance do sistema são apresentados ao longo da dissertação, incluindo os testes de validação efectuados nas bancadas de teste e a descrição da metodologia aplicada à análise dos sinais recolhidos. Testes experimentais provaram a eficiência da caixa de aquisição e da última versão da bancada de teste, permitindo adquirir, com precisão, sinais referentes à pressão arterial e à sua forma de onda. Palavras-Chave Doenças Cardiovasculares, Hipertensão, Forma de onda da pressão sanguínea da Carótida, Sensor Piezoeléctrico, Acelerómetro, Modulação, Desmodulação

Investigations of photoplethysmography in the assessment of haemodynamics, vascular mechanics and haemorheology

Real‐time cardiovascular assessment is vital for monitoring patients at an early stage of cardiovascular diseases (CVDs), at risk of reoccurrence of heart attacks and strokes and during pharmacological and non‐pharmacological treatments. Blood Pressure (BP), Arterial Stiffness (AS) and Blood Viscosity (BV) are three essential parameters that can provide a reliable assessment of hypertension, atherosclerosis, and hyperviscosity associated with the development and progression of cardiovascular pathologies to complex stages. The currently available methods designed for evaluation of such parameters incur limitations and challenges that stand as an obstacle to the development of non‐invasive, portable and reliable all‐in‐one device intended for personal use. This project engaged in novel fundamental and rigorous in vivo and in vitro investigations in an effort to shed more light on the photoplethysmographic signals (AC and DC) during induced changes of BP, AS, and BV. The underlying hypothesis is to show for the first time that Photoplethysmography (PPG) has the potential to non‐invasively assess, in a qualifying and quantifying manner, the above parameters. Positives outcomes from such approach will establish the potential of the PPG as a preferential monitoring (screening and possible diagnosis) technique for the assessment of CVDs. Novel miniature PPG sensors were developed along with a state of the art PPG processing unit, a data acquisition system and a customised manuscript for offline signal analyses. ECG and temperature processing systems were also designed and developed for use in the in vivo investigations. State of the art in vitro experimental rig was developed to mimic the human circulation under a wide range of flow conditions. A pilot volunteer investigation highlighted the effect of a cold pressor test in one hand on the PPG signals from both hands. The results indicated that there are changes in flow regulation mechanisms and hemodynamics besides the expected vasoconstriction effects of local cooling. These findings led to the controlled in vitro experiments. The in vitro investigations were completed in four stages where the potential of the PPG to provide a measure of blood pressure values, volume elastic modulus (Ev) and to detect fluid viscosity and haemorheological changes. Results from the in vitro investigations highlighted that Adjusted Pulse Volume (APV) was found to be the optimum method for measuring BP values using Red (R) and Infrared (IR) wavelengths as validated under a range of BP values simulating hypotensive to hypertensive scenarios. The correlation was significant with Rsquare ranging between 0.96 and 0.99 for different arterial models and circulating fluids. Moreover, a proposed mathematical derivation allowed the PPG to provide a direct measure of AS using Ev. The method showed strong agreement with the gold standard measurement of material testings, the Instron device, with a percent error of 0.26% and 1.9% for different arterial models. Furthermore, the PPG signals also responded to changes in rheological characteristics in relation to fluid viscosities, the presence of the red blood cells, changes in shear rates and blood clotting. These results strongly suggest that PPG has the potential to be used as a non‐invasive and continuous method for the assessment of cardiovascular disease markers such as blood pressure, arterial stiffness and blood viscosity

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua