Algoritmo para el análisis en conjunto de las señales del ECG y PPG

The main objective of this research is based on finding out some assertive and robust Photoplethysmogram’s PPG & Electrocardiogram’s ECG blood pressure-related parameters by the implementation of a novel method with innovations in signal processing and analysis. The biomedical ECG and PPG signals are recorded using a mobile monitor CardioQVark. To increase the cuffless blood pressure measurement accuracy, a technique that involves not only the ECG and PPG joint parameters extraction but also some individual PPG’s morphology features, is proposed in this work. Firstly, the biomedical ECG and PPG signals are time–frequency filtered.  Secondly, some novel parameters from the morphology of photoplethysmogram signal, which may be correlated with blood pressure, are considered in addition to the pulse transit time. Additionally, a neural network is built to determine the relationship between the estimated and reference blood pressure. Finally, the correlation coefficient and regression line are obtained to evaluate the feasibility.El objetivo de esta investigación consiste en identificar aquellos parámetros provenientes de las señales del electrocardiograma ECG y fotopletismograma PPG que permitan hacer una evaluación de la presión sanguínea utilizando un dispositivo móvil. El método propuesto incluye innovaciones en el procesamiento y análisis de las señales. Con el objetivo de aumentar la precisión de la medición de la presión sanguínea, en este trabajo, se propone la utilización de parámetros provenientes de la señal del PPG en conjunto con el PTT obtenido de las señales del ECG y PPG analizadas en conjunto. Adicionalmente, se propone el diseño e implementación de una red neural para determinar la relación existente entre la presión sanguínea estimada por el método y la de referencia, lo cual permite evaluar la viabilidad del método propuesto

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

Wireless Chest Wearable Vital Sign Monitoring Platform for Hypertension

Hypertension, a silent killer, is the biggest challenge of the 21 st century in public health agencies worldwide. World Health Organization (WHO) statistic shows that the mortality rate of hypertension is 9.4 million per year and causes 55.3% of total deaths in cardiovascular (CV) patients. Early detection and prevention of hypertension can significantly reduce the CV mortality. We are presenting a wireless chest wearable vital sign monitoring platform. It measures Electrocardiogram (ECG), Photoplethsmogram (PPG) and Ballistocardiogram (BCG) signals and sends data over Bluetooth low energy (BLE) to mobile phone-acts as a gateway. A custom android application relays the data to thingspeak server where MATLAB based offline analysis estimates the blood pressure. A server reacts on the health of subject to friends and family on the social media - twitter. The chest provides a natural position for the sensor to capture legitimate signals for hypertension condition. We have done a clinical technical evaluation of prototypes on 11 normotensive subjects, 9 males 2 females

Cuffless calibration and estimation of continuous arterial blood pressure.

Gu, Wenbo.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references.Abstract also in Chinese.Acknowledgment --- p.iAbstract --- p.ii摘要 --- p.iiiList of Figures --- p.viList of Tables --- p.viiList of Abbreviations --- p.viiiContents --- p.ixChapter 1. --- Introduction --- p.1Chapter 1.1. --- Arterial blood pressure and its importance --- p.1Chapter 1.2. --- Current methods for non-invasive blood pressure measurement --- p.4Chapter 1.2.1. --- The auscultatory method (mercury sphygmomanometer) --- p.4Chapter 1.2.2. --- The oscillometric method --- p.5Chapter 1.2.3. --- The tonometric method --- p.7Chapter 1.2.4. --- The volume-clamp method --- p.7Chapter 1.3. --- Blood pressure estimation based on pulse arrival time --- p.8Chapter 1.4. --- Objectives and structures of this thesis --- p.10Chapter 2. --- Hemodynamic models: relationship between PAT and BP --- p.14Chapter 2.1. --- The generation of arterial pulsation --- p.14Chapter 2.2. --- Pulse wave velocity along the arterial wall --- p.15Chapter 2.2.1. --- Moens-Korteweg equation --- p.15Chapter 2.2.2. --- Bergel wave velocity --- p.18Chapter 2.3. --- Relationship between PWV and BP --- p.19Chapter 2.3.1. --- Bramwell-Hill´ةs model --- p.20Chapter 2.3.2. --- Volume-pressure relationship --- p.20Chapter 2.3.3. --- Hughes' model --- p.22Chapter 2.4. --- The theoretical expression of PAT-BP relationship --- p.23Chapter 3. --- Estimation and calibration of arterial BP based on PAT --- p.25Chapter 3.1. --- PAT measurement --- p.25Chapter 3.1.1. --- Principle of ECG measurement --- p.25Chapter 3.1.2. --- Principle of PPG measurement --- p.26Chapter 3.1.3. --- Calculation of PAT --- p.28Chapter 3.2. --- Calibration methods for PAT-BP estimation --- p.29Chapter 3.2.1. --- Calibration based on cuff BP readings --- p.30Chapter 3.2.2. --- Calibration by hydrostatic pressure changes --- p.31Chapter 3.2.3. --- Calibration by multiple regression --- p.33Chapter 3.3. --- Model-based calibration with PPG waveform parameters --- p.34Chapter 3.3.1. --- Model-based equation with parameters from PPG waveform --- p.34Chapter 3.3.2. --- Selection of parameters from PPG waveform --- p.36Chapter 4. --- Cuffless calibration approach using PPG waveform parameter for PAT-BP estimation --- p.43Chapter 4.1. --- Introduction --- p.43Chapter 4.2. --- Experiment I: young group in sitting position including rest and after exercise states --- p.43Chapter 4.2.1. --- Experiment protocol --- p.43Chapter 4.2.2. --- Data Analysis --- p.44Chapter 4.2.3. --- Experiment results --- p.46Chapter 4.3. --- Experiment II: over-month observation using wearable device in sitting position --- p.48Chapter 4.3.1. --- Body sensor network for blood pressure estimation --- p.49Chapter 4.3.2. --- Experiment protocol and data collection --- p.50Chapter 4.3.3. --- Experiment results --- p.50Chapter 4.4. --- Experiment III: contactless monitoring in supine position --- p.51Chapter 4.4.1. --- The design of the contactless system --- p.52Chapter 4.4.2. --- Experiment protocol and data collection --- p.53Chapter 4.4.3. --- Experiment results --- p.53Chapter 4.5. --- Discussion --- p.55Chapter 4.5.1. --- Discussion of Experiments I and II --- p.55Chapter 4.5.2. --- Discussion of Experiments II and III --- p.57Chapter 4.5.3. --- Conclusion --- p.58Chapter 5. --- Cuff-based calibration approach for BP estimation in supine position --- p.61Chapter 5.1. --- Introduction --- p.61Chapter 5.2. --- Experiment protocol --- p.61Chapter 5.2.1. --- Experiment IV: exercise experiment in supine position in lab --- p.61Chapter 5.2.2. --- Experiment V: exercise experiment in supine position in PWH --- p.63Chapter 5.3. --- Data analysis --- p.65Chapter 5.3.1. --- Partition of signal trials and selection of datasets --- p.65Chapter 5.3.2. --- PPG waveform processing --- p.66Chapter 5.4. --- Experiment results --- p.68Chapter 5.4.1. --- Range and variation of reference SBP --- p.68Chapter 5.4.2. --- PAT-BP individual best regression --- p.69Chapter 5.4.3. --- Multiple regression using ZX and arm length --- p.72Chapter 5.4.4. --- One-cuff calibration improved by PPG waveform parameter --- p.72Chapter 5.5. --- Discussion --- p.74Chapter 6. --- Conclusion --- p.7

Wearable technology and the cardiovascular system: the future of patient assessment

The past decade has seen a dramatic rise in consumer technologies able to monitor a variety of cardiovascular parameters. Such devices initially recorded markers of exercise, but now include physiological and health-care focused measurements. The public are keen to adopt these devices in the belief that they are useful to identify and monitor cardiovascular disease. Clinicians are therefore often presented with health app data accompanied by a diverse range of concerns and queries. Herein, we assess whether these devices are accurate, their outputs validated, and whether they are suitable for professionals to make management decisions. We review underpinning methods and technologies and explore the evidence supporting the use of these devices as diagnostic and monitoring tools in hypertension, arrhythmia, heart failure, coronary artery disease, pulmonary hypertension, and valvular heart disease. Used correctly, they might improve health care and support research

A pervasive system for real-time blood pressure monitoring

Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

pyPPG: A Python toolbox for comprehensive photoplethysmography signal analysis

Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and increasingly used for in a variety of research and clinical application to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time series analysis recorded using a standard finger-based transmission pulse oximeter. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2,054 adult polysomnography recordings totaling over 91 million reference beats. This algorithm outperformed the open-source original Matlab implementation by ~5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3,000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Based on these fiducial points, pyPPG engineers a set of 74 PPG biomarkers. Studying the PPG time series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on physiozoo.orgComment: The manuscript was submitted to "Physiological Measurement" on September 5, 202