Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010-2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

In the spotlight: Bioinstrumentation

Significant advances in the field of biomedical instrumentation have continued to contribute towards improvements in the quality of clinical diagnosis and treatment, which could have important implications for health and welfare. Up to the present, progress in several topics has been reviewed within the In the Spotlight column, including new and noteworthy measurement methods [1], ballistocardiography revisited [2], wearable monitoring [3], and home healthcare technologies [4]. The present review deals with three notable topics from recent publications in the field of bioinstrumentation. These are: (1) new conducting polymer electrodes for in vivo bio-electrical measurements, (2) mHealth technology using mobile communication devices such as mobile phones and personal digital assistants (PDAs), and (3) non-invasive in vivo measurement of blood constituents. These topics are of recent or renewed interest and, where appropriate, progress since the previous reviews [1], [2] will be highlighted. © 2008-2011 IEEE

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

대규모 인구 모델과 단일 가슴 착용형 장치를 활용한 비침습적 연속 동맥 혈압 모니터링 시스템

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2021. 2. 김희찬.최근 수십 년 동안 비침습적 연속 혈압 모니터링에 대한 필요성이 점차 대두되면서 맥파 전달 시간, 맥파 도달 시간, 또는 광용적맥파의 파형으로부터 추출된 다양한 특징들을 이용한 혈압 추정 연구들이 전세계적으로 활발하게 진행되었다. 하지만 대부분의 연구들은 국제 혈압 표준을 만족시키지 못하는 매우 적은 수의 피험자들 만을 대상으로 주로 혈압 추정 모델을 개발 및 검증하였기 때문에 성능의 정확도가 적절하게 검증되지 못했다는 한계점이 있었고, 또한 혈압 추정 파라미터 추출을 위한 생체 신호들을 측정하기 위해 대부분 두 개 이상의 모듈을 필요로 하면서 실용성 측면에서 한계점이 있었다. 첫 번째 연구는 대규모 생체신호 데이터베이스들을 분석함으로써 임상적으로 허용 가능한 수준의 정확도가 적절히 검증된 혈압 추정 모델을 개발하는 것을 목적으로 진행되었다. 본 연구에서는 1376명의 수술 중 환자들의 약 250만 심박 주기에 대해 측정된 두 가지 비침습적 생체신호인 심전도와 광용적맥파를 활용한 혈압 추정 방식들을 분석하였다. 맥파 도달 시간, 심박수, 그리고 다양한 광용적맥파 파형 피처들을 포함하는 총 42 종류의 파라미터들을 대상으로 피처 선택 기법들을 적용한 결과, 28개의 피처들이 혈압 추정 파라미터로 결정되었고, 특히 두 가지 광용적맥파 피처들이 기존에 혈압 추정 파라미터로 가장 주요하게 활용되었던 맥파 도달 시간보다 우월한 파라미터들로 분석되었다. 선정된 파라미터들을 활용하여 혈압의 낮은 주파수 성분을 인공신경망으로 모델링하고, 높은 주파수 성분을 순환신경망으로 모델링 한 결과, 수축기 혈압 에러율 0.05 ± 6.92 mmHg와 이완기 혈압 에러율 -0.05 ± 3.99 mmHg 정도의 높은 정확도를 달성하였다. 또 다른 생체신호 데이터베이스에서 추출한 334명의 중환자들을 대상으로 모델을 외부 검증했을 때 유사한 결과를 획득하면서 세 가지 대표적 혈압 측정 장비 기준들을 모두 만족시켰다. 해당 결과를 통해 제안된 혈압 추정 모델이 1000명 이상의 다양한 피험자들을 대상으로 적용 가능함을 확인하였다. 두 번째 연구는 일상 생활 중 장기간 모니터링이 가능한 단일 착용형 혈압 모니터링 시스템을 개발하는 것을 목적으로 진행되었다. 대부분의 기존 혈압 추정 연구들은 혈압 추정 파라미터 추출을 위해 필요한 생체신호들을 측정하기 위해 두 군데 이상의 신체 지점에 두 개 이상의 모듈을 부착하는 등 실용성 측면에서 한계를 나타냈다. 이를 해결하기 위해 본 연구에서는 심전도와 광용적맥파를 동시에 연속적으로 측정하는 단일 가슴 착용형 디바이스를 개발하였고, 개발된 디바이스를 대상으로 총 25명의 건강한 피험자들로부터 데이터를 획득하였다. 손가락에서 측정된 광용적맥파와 가슴에서 측정된 광용적맥파 간 파형의 특성에 유의미한 차이가 있기 때문에 가슴에서 측정된 광용적맥파에서 추출된 피처들을 대응되는 손가락에서 측정된 광용적맥파 피처들로 특성을 변환하는 전달 함수 모델을 개발하였다. 25명으로부터 획득한 데이터에 전달 함수 모델을 적용시킨 후 혈압 추정 모델을 검증한 결과, 수축기 혈압 에러율 0.54 ± 7.47 mmHg와 이완기 혈압 에러율 0.29 ± 4.33 mmHg로 나타나면서 세 가지 혈압 측정 장비 기준들을 모두 만족시켰다. 결론적으로 본 연구에서는 임상적으로 허용 가능한 수준의 정확도로 장기간 일상 생활이 가능한 비침습적 연속 동맥 혈압 모니터링 시스템을 개발하고 다수의 데이터셋을 대상으로 검증함으로써 고혈압 조기 진단 및 예방을 위한 모바일 헬스케어 서비스의 가능성을 확인하였다.As non-invasive continuous blood pressure monitoring (NCBPM) has gained wide attraction in the recent decades, many studies on blood pressure (BP) estimation using pulse transit time (PTT), pulse arrival time (PAT), and characteristics extracted from the morphology of photoplethysmogram (PPG) waveform as indicators of BP have been conducted. However, most of the studies have used small homogeneous subject pools to generate models of BP, which led to inconsistent results in terms of accuracy. Furthermore, the previously proposed modalities to measure BP indicators are questionable in terms of practicality, and lack the potential for being utilized in daily life. The first goal of this thesis is to develop a BP estimation model with clinically valid accuracy using a large pool of heterogeneous subjects undergoing various surgeries. This study presents analyses of BP estimation methods using 2.4 million cardiac cycles of two commonly used non-invasive biosignals, electrocardiogram (ECG) and PPG, from 1376 surgical patients. Feature selection methods were used to determine the best subset of predictors from a total of 42 including PAT, heart rate, and various PPG morphology features. BP estimation models were constructed using linear regression, random forest, artificial neural network (ANN), and recurrent neural network (RNN), and the performances were evaluated. 28 features out of 42 were determined as suitable for BP estimation, in particular two PPG morphology features outperformed PAT, which has been conventionally seen as the best non-invasive indicator of BP. By modelling the low frequency component of BP using ANN and the high frequency component using RNN with the selected predictors, mean errors of 0.05 ± 6.92 mmHg for systolic blood pressure (SBP), and -0.05 ± 3.99 mmHg for diastolic blood pressure (DBP) were achieved. External validation of the model using another biosignal database consisting of 334 intensive care unit patients led to similar results, satisfying three international standards concerning the accuracy of BP monitors. The results indicate that the proposed method can be applied to large number of subjects and various subject phenotypes. The second goal of this thesis is to develop a wearable BP monitoring system, which facilitates NCBPM in daily life. Most previous studies used two or more modules with bulky electrodes to measure biosignals such as ECG and PPG for extracting BP indicators. In this study, a single wireless chest-worn device measuring ECG and PPG simultaneously was developed. Biosignal data from 25 healthy subjects measured by the developed device were acquired, and the BP estimation model developed above was tested on this data after applying a transfer function mapping the chest PPG morphology features to the corresponding finger PPG morphology features. The model yielded mean errors of 0.54 ± 7.47 mmHg for SBP, and 0.29 ± 4.33 mmHg for DBP, again satisfying the three standards for the accuracy of BP monitors. The results indicate that the proposed system can be a stepping stone to the realization of mobile NCBPM in daily life. In conclusion, the clinical validity of the proposed system was checked in three different datasets, and it is a practical solution to NCBPM due to its non-occlusive form as a single wearable device.Abstract i Contents iv List of Tables vii List of Figures viii Chapter 1 General Introduction 1 1.1 Need for Non-invasive Continuous Blood Pressure Monitoring (NCBPM) 2 1.2 Previous Studies for NCBPM 5 1.3 Issues with Previous Studies 9 1.4 Thesis Objectives 12 Chapter 2 Non-invasive Continuous Arterial Blood Pressure Estimation Model in Large Population 14 2.1 Introduction 15 2.1.1 Electrocardiogram (ECG) and Photoplethysmogram (PPG) Features for Blood Pressure (BP) Estimation 15 2.1.2 Description of Surgical Biosignal Databases 16 2.2 Feature Analysis 19 2.2.1 Data Acquisition and Data Pre-processing 19 2.2.2 Feature Extraction 25 2.2.3 Feature Selection 35 2.3 Construction of the BP Estimation Models 44 2.3.1 Frequency Component Separation 44 2.3.2 Modelling Algorithms 47 2.3.3 Summary of Training and Validation 52 2.4 Results and Discussion 54 2.4.1 Feature Analysis 54 2.4.1.1 Pulse Arrival Time versus Pulse Transit Time 54 2.4.1.2 Feature Selection 57 2.4.2 Optimization of the BP Estimation Models 63 2.4.2.1 Frequency Component Separation 63 2.4.2.2 Modelling Algorithms 66 2.4.2.3 Comparison against Different Modelling Settings 68 2.4.3 Performance of the Best-case BP Estimation Model 69 2.4.4 Limitations 75 2.5 Conclusion 78 Chapter 3 Development of the Single Chest-worn Device for Non-invasive Continuous Arterial Blood Pressure Monitoring 80 3.1 Introduction 81 3.2 Development of the Single Chest-worn Device 84 3.2.1 Hardware Development 84 3.2.2 Software Development 90 3.2.3 Clinical Trial 92 3.3 Development of the Transfer Function 95 3.3.1 Finger PPG versus Chest PPG 95 3.3.2 The Concept of the Transfer Function 97 3.3.3 Data Acquisition for Modelling of the Transfer Function 98 3.4 Results and Discussion 100 3.4.1 Construction of the Transfer Function 100 3.4.2 Test of the BP Estimation Model 101 3.4.3 Comparison with the Previous Study using the Single Chest-worn Device 104 3.4.4 Limitations 106 3.5 Conclusion 108 Chapter 4 Thesis Summary and Future Direction 109 4.1 Summary and Contributions 110 4.2 Future Work 113 Bibliography 115 Abstract in Korean 129 Acknowledgement 132Docto

Cuffless bood pressure estimation

L'hypertension est une maladie qui affecte plus d'un milliard de personnes dans le monde. Il s'agit d'une des principales causes de décès; le suivi et la gestion de cette maladie sont donc cruciaux. La technologie de mesure de la pression artérielle la plus répandue, utilisant le brassard pressurisé, ne permet cependant pas un suivi en continu de la pression, ce qui limite l'étendue de son utilisation. Ces obstacles pourraient être surmontés par la mesure indirecte de la pression par l'entremise de l'électrocardiographie ou de la photopléthysmographie, qui se prêtent à la création d'appareils portables, confortables et peu coûteux. Ce travail de recherche, réalisé en collaboration avec le département d'ingénierie biomédicale de l'université de Lund, en Suède, porte principalement sur la base de données publique Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Waveform Datasetde PhysioNet, largement utilisée dans la littérature portant sur le développement et la validation d'algorithmes d'estimation de la pression artérielle sans brassard pressurisé. Puisque ces données proviennent d'unités de soins intensifs et ont été recueillies dans des conditions non contrôlées, plusieurs chercheurs ont avancé que les modèles d'estimation de la pression artérielle se basant sur ces données ne sont pas valides pour la population générale. Pour la première fois dans la littérature, cette hypothèse est ici mise à l'épreuve en comparant les données de MIMIC à un ensemble de données de référence plus représentatif de la population générale et recueilli selon une procédure expérimentale bien définie. Des tests statistiques révèlent une différence significative entre les ensembles de données, ainsi qu'une réponse différente aux changements de pression artérielle, et ce, pour la majorité des caractéristiques extraites du photopléthysmogramme. De plus, les répercussions de ces différences sont démontrées à l'aide d'un test pratique d'estimation de la pression artérielle par apprentissage machine. En effet, un modèle entraîné sur l'un des ensembles de données perd en grande partie sa capacité prédictive lorsque validé sur l'autre ensemble, par rapport à sa performance en validation croisée sur l'ensemble d'entraînement. Ces résultats constituent les contributions principales de ce travail et ont été soumis sous forme d'article à la revue Physiological Measurement. Un volet additionnel de la recherche portant sur l'analyse du pouls par décomposition (pulse de composition analysis ou PDA) est présenté dans un deuxième temps. La PDA est une technique permettant de séparer l'onde du pouls en une composante excitative et ses réflexions, utilisée pour extraire des caractéristiques du signal dans le contexte de l'estimation de la pression artérielle. Les résultats obtenus démontrent que l'estimation de la position temporelle des réflexions à partir de points de référence de la dérivée seconde du signal donne d'aussi bons résultats que leur détermination par la méthode traditionnelle d'approximation successive, tout en étant beaucoup plus rapide. Une méthode récursive rapide de PDA est également étudiée, mais démontrée comme inadéquate dans un contexte de comparaison intersujet.Hypertension affects more than one billion people worldwide. As one of the leading causes of death, tracking and management of the condition is critical, but is impeded by the current cuff-based blood pressure monitoring technology. Continuous and more ubiquitous blood pressure monitoring may be achieved through simpler, cheaper and less invasive cuff-less devices, performing an indirect measure through electrocardiography or photoplethysmography. Produced in collaboration with the department of biomedical engineering of Lund Universityin Sweden, this work focuses on public data that has been widely used in the literature to develop and validate cuffless blood pressure estimation algorithms: The Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Waveform Dataset from PhysioNet. Because it is sourced from intensive care units and collected in absence of controlled conditions, it has many times been hypothesized that blood pressure estimation models based on its data may not generalize to the normal population. This work tests that hypothesis for the first time by comparing the MIMIC dataset to another reference dataset more representative of the general population and obtained under controlled experimental conditions. Through statistical testing, a majority of photoplethysmogram based features extracted from MIMIC are shown to differ significantly from the reference dataset and to respond differently to blood pressure changes. In addition, the practical impact of those differences is tested through the training and cross validating of machine learning models on both datasets, demonstrating an acute loss of predictive powers of models facing data from outside the dataset used in the training phase. As the main contribution of this work, these findings have been submitted as a journal paper to Physiological Measurement. Additional original research is also presented in relation to pulse decomposition analysis (PDA), a technique used to separate the pulse wave from its reflections, in the context of blood pressure estimation. The results obtained through this work show that when using the timing of reflections as part of blood pressure predictors, estimating those timings from fiducial points in the second derivative works as well as using the traditional and computationally costly successive approximation PDA method, while being many times faster. An alternative fast recursive PDA algorithm is also presented and shown to perform inadequately in an inter-subject comparison context

Remote Assessment of the Cardiovascular Function Using Camera-Based Photoplethysmography

Camera-based photoplethysmography (cbPPG) is a novel measurement technique that allows the continuous monitoring of vital signs by using common video cameras. In the last decade, the technology has attracted a lot of attention as it is easy to set up, operates remotely, and offers new diagnostic opportunities. Despite the growing interest, cbPPG is not completely established yet and is still primarily the object of research. There are a variety of reasons for this lack of development including that reliable and autonomous hardware setups are missing, that robust processing algorithms are needed, that application fields are still limited, and that it is not completely understood which physiological factors impact the captured signal. In this thesis, these issues will be addressed. A new and innovative measuring system for cbPPG was developed. In the course of three large studies conducted in clinical and non-clinical environments, the system’s great flexibility, autonomy, user-friendliness, and integrability could be successfully proven. Furthermore, it was investigated what value optical polarization filtration adds to cbPPG. The results show that a perpendicular filter setting can significantly enhance the signal quality. In addition, the performed analyses were used to draw conclusions about the origin of cbPPG signals: Blood volume changes are most likely the defining element for the signal's modulation. Besides the hardware-related topics, the software topic was addressed. A new method for the selection of regions of interest (ROIs) in cbPPG videos was developed. Choosing valid ROIs is one of the most important steps in the processing chain of cbPPG software. The new method has the advantage of being fully automated, more independent, and universally applicable. Moreover, it suppresses ballistocardiographic artifacts by utilizing a level-set-based approach. The suitability of the ROI selection method was demonstrated on a large and challenging data set. In the last part of the work, a potentially new application field for cbPPG was explored. It was investigated how cbPPG can be used to assess autonomic reactions of the nervous system at the cutaneous vasculature. The results show that changes in the vasomotor tone, i.e. vasodilation and vasoconstriction, reflect in the pulsation strength of cbPPG signals. These characteristics also shed more light on the origin problem. Similar to the polarization analyses, they support the classic blood volume theory. In conclusion, this thesis tackles relevant issues regarding the application of cbPPG. The proposed solutions pave the way for cbPPG to become an established and widely accepted technology

Cuffless Blood Pressure Monitoring: Estimation of the Waveform and its Prediction Interval

Cuffless blood pressure (BP) estimation devices are receiving considerable attention as tools for improving the management of hypertension, a condition that affects 1.13 billion people worldwide. It is an approach that can provide continuous BP monitoring, which is not possible with existing non-invasive tools. Therefore, it yields a more comprehensive picture of the patient’s state. Cuffless BP monitoring relies on surrogate models of BP and the information encoded in alternative physiological measures, such as photoplethysmography (PPG) or electrocardiography (ECG), to continuously estimate BP. Existing models have typically relied upon pulse-wave delay between two arterial segments or other pulse waveform features in the estimation process. However, the models available in the literature (1) provide an estimation of the systolic BP (SBP), diastolic BP (DBP), and mean BP (MAP) only, (2) are validated solely in controlled environments, and (3) do not assign a confidence metric to the estimates. At this point, cuffless methods are not used by clinicians due to their inaccuracy, the validation inadequacy, and/or the unevaluated uncertainty of the existing methods. The first objective of this thesis is to develop a cuffless modeling approach to estimate the BP waveform from ECG and PPG, and extract important BP features, such as the SBP, DBP, and MAP. Access to the full waveform has significant advantages over previous cuffless BP estimation tools in terms of accuracy and access to additional cardiovascular health markers (e.g., cardiac output), as well as potentially providing arterial stiffness. The second objective of this thesis is to validate cuffless BP estimation during activities of daily living, an uncontrolled environment, but also in more challenging physiological conditions such as during exercise. Such validation is important to increase confidence in cuffless BP monitoring, it also helps understand the limitation of the method and how they would affect clinical outcomes. Finally, in an effort to improve confidence in the cuffless BP estimation framework (third objective), a prediction interval (PI) estimation method is introduced. For potential clinical uses, it is imperative to assess the uncertainty of the BP estimate for acute outcome evaluation and it is even more so if cuffless BP is to be employed outside of the clinic. In this thesis, user-specific nonlinear autoregressive models with exogenous inputs (NARX) are implemented using an artificial neural network (ANN) to predict the BP waveforms using ECG and/or PPG signals as inputs. To validate the NARX-based BP estimation framework during activities of daily living, data were collected during six-hours testing phase wherein the participants go about their normal daily living activities. Data are further collected at four-month and six-month time points to validate long-term performance. To broaden the range of BP in the training data, subjects followed a short procedure consisting of sitting, standing, walking, Valsalva maneuvers, and static handgrip exercises. To evaluate the uncertainty of the BP estimates, one-class support vector machines (OCSVM) models are trained to cluster data in terms of the percentage of outliers. New BP estimates are then assigned to a cluster using the OCSVMs hyperplanes, and the PIs are estimated using the BP error standard deviation associated with different training data clusters. The OCSVM is used to estimate the PI for three BP model architectures: NARX models, feedforward ANN models, and pulse arrival time (PAT models). The three BP estimations from the models are fused using the covariance intersection fusion algorithm, which improves BP and PI estimates in comparison with individual model performance. The proposed method models the BP as a dynamical system leading to better accuracy in the estimation of SBP, DBP and MAP when compared to the PAT model. Moreover, the NARX model, with its ability to provide the BP waveform, yields more insight into patient health. The NARX model demonstrates superior accuracy and correlation with “ground truth” SBP and DBP measures compared to the PAT models and a clear advantage in estimating the large range of BP. Preliminary results show that the NARX models can accurately estimate BP even months apart from the training. Preliminary testing suggests that it is robust against variabilities due to sensor placement. The employed model fusion architecture establishes a method for cuffless BP estimation and its PI during activities of daily living that can be used for continuous monitoring and acute hypotension and hypertension detection. The NARX model, with its capacity to estimate a large range of BP, is next tested during moderate and heavy intensity exercise. Participants performed three cycling exercises: a ramp-incremental exercise test to exhaustion, a moderate and a heavy pseudorandom binary sequence exercise tests on an electronically braked cycle ergometer. Subject-specific and population-based NARX models are compared with feedforward ANN models and PAT (and heart rate) models. Population-based NARX models, when trained on 11 participants’ three cycling tests (tested on the participant left out of training), perform better than the other models and show good capability at estimating large changes in MAP. A limitation of the approach is the incapability of the models to track consistent decreases in BP during the exercise caused by a decrease in peripheral resistance since this information is apparently not encoded in either the forehead PPG or ECG signals. Nevertheless, the NARX model shows good precision during the whole 21 minutes testing window, a precision that is increased when using a shorter evaluation time window, and that can potentially be even further increased if trained on more data. The validation protocols and the use of a confidence metric developed in this thesis is of great value for such health monitoring application. Through such methodology, it is hoped that cuffless BP estimation becomes, one day, a well-established BP measurement method

Monte Carlo investigation of light-tissue interaction in photoplethysmography

Photoplethysmography (PPG) is a non-invasive photometric technique which measures changes in the volume of blood in the biological tissue. PPG is well-known for its application in pulse oximetry used for the continuous monitoring of arterial blood oxygen saturation (SpO2). Over the past decade, there has been a plethora of research in the field of PPG, with potential applications beyond pulse oximetry and heart rate monitoring. Such applications explore the utilisation of PPG for the assessment of various bio-markers relating to vascular mechanics, haemodynamics and many others. With the growing research interest in the field of PPG, a comprehensive understanding of the light-tissue interaction-based working principle underlying the technique is essential. This thesis is focussed on the investigation of the fundamental light-tissue interactions in PPG using the Monte Carlo method. Tissue models have been developed in this thesis which were characterised by the optical properties (e.g., wavelength- dependent coefficients of scattering and absorption etc.), the anatomical features (e.g., stratification and dimension of tissue layers and sublayers etc.), and the physiological parameters (water and blood content in tissue layers etc.). The Monte Carlo strategy was verified, and was initially implemented to model the light propagation through a monolayer perfused dermal tissue volume in a reflective mode PPG at the red and near-infrared wavelengths, usually used in pulse oximetry. Results illustrated the distribution of the scattering-absorption interaction events, and quantified the optical pathlength, penetration depth and detected reflectance with the variable sensor geometry (i.e., source-detector separation) and physiological states (i.e., the volume of blood and oxygen saturation) of the tissue. The monolayer model was also employed to produce the plot resembling the ‘calibration curve’ used in pulse oximetry. With the knowledge gained from the monolayer-model study, a similar investigation was performed on a heterogeneous tissue structure of a human finger which was executed in both reflective and transmissive geometrical settings. The calibration curves produced from the detected reflectance and transmittance exhibited a high correlation. The absorbances of red and near-infrared light by individual layers of the finger were quantified at systole and diastole. To the relative absorbance, the contributions of dermis and bone were the maximum and the minimum, respectively. The dependence of the optical pathlength on the source-detector separation and the operating wavelength was quantified by the Differential Pathlength Factor (DPF), which was assessed for the reflective mode PPG by simulating light propagation through a human forearm tissue volume. The DPF values were used in experimentally obtained PPG signal in order to determine the time-change in the concentration of oxyhaemoglobin and deoxyhaemoglobin. Cross-talk and absolute errors were calculated between the simulated and approximated DPFs. The results presented in the thesis contribute greatly to the understanding on PPG light-tissue interaction. Such knowledge could also greatly contribute to the development of the new generation PPG sensors for various applications