In-ear photoplethysmography for central pulse waveform analysis in non-invasive hemodynamic monitoring

In recent years, the analysis of the photoplethys-mographic (PPG) pulse waveforms has attracted much research focus. However, the considered signals are primarily recorded at the fingertips, which suffer from reduced peripheral perfusion in situations like hypovolemia or sepsis, rendering waveform analysis infeasible. The ear canal is not affected by cardiovascular centralization and could thus prove to be an ideal alternate measurement site for pulse waveform analysis. Therefore, we developed a novel system that allows for highly accurate photoplethysmographic measurements in the ear canal. We conducted a measurement study in order to assess the signal-to-noise ratio of our developed system Hereby, we achieved a mean SNR of 40.65 dB. Hence, we could show that our system allows for highly accurate PPG recordings in the ear canal facilitating sophisticated pulse waveform analysis. Furthermore, we demonstrated that the pulse decomposition analysis is also applicable to in-ear PPG recordings

Beat-to-beat blood pressure estimation by photoplethysmography and its interpretation

Blood pressure (BP) is among the most important vital signals. Estimation of absolute BP solely using photoplethysmography (PPG) has gained immense attention over the last years. Available works differ in terms of used features as well as classifiers and bear large differences in their results. This work aims to provide a machine learning method for absolute BP estimation, its interpretation using computational methods and its critical appraisal in face of the current literature. We used data from three different sources including 273 subjects and 259,986 single beats. We extracted multiple features from PPG signals and its derivatives. BP was estimated by xgboost regression. For interpretation we used Shapley additive values (SHAP). Absolute systolic BP estimation using a strict separation of subjects yielded a mean absolute error of 9.456mmHg and correlation of 0.730. The results markedly improve if data separation is changed (MAE: 6.366mmHg, r: 0.874). Interpretation by means of SHAP revealed four features from PPG, its derivation and its decomposition to be most relevant. The presented approach depicts a general way to interpret multivariate prediction algorithms and reveals certain features to be valuable for absolute BP estimation. Our work underlines the considerable impact of data selection and of training/testing separation, which must be considered in detail when algorithms are to be compared. In order to make our work traceable, we have made all methods available to the public

A ZigBee-based wireless biomedical sensor network as a precursor to an in-suit system for monitoring astronaut state of health

Master of ScienceDepartment of Electrical and Computer EngineeringSteven WarrenNetworks of low-power, in-suit, wired and wireless health sensors offer the potential to track and predict the health of astronauts engaged in extra-vehicular and in-station activities in zero- or reduced- gravity environments. Fundamental research questions exist regarding (a) types and form factors of biomedical sensors best suited for these applications, (b) optimal ways to render wired/wireless on-body networks with the objective to draw little-to-no power, and (c) means to address the wireless transmission challenges offered by a spacesuit constructed from layers of aluminized mylar. This thesis addresses elements of these research questions through the implementation of a collection of ZigBee-based wireless health monitoring devices that can potentially be integrated into a spacesuit, thereby providing continuous information regarding astronaut fatigue and state of health. Wearable biomedical devices investigated for this effort include electrocardiographs, electromyographs, pulse oximeters, inductive plethysmographs, and accelerometers/gyrometers. These ZigBee-enabled sensors will form the nodes of an in-suit ZigBee Pro network that will be used to (1) establish throughput requirements for a functional in-suit network and (2) serve as a performance baseline for future devices that employ ultra-low-power field-programmable gate arrays and micro-transceivers. Sensor devices will upload data to a ZigBee network coordinator that has the form of a pluggable USB connector. Data are currently visualized using MATLAB and LabVIEW

In vivo investigations of photoplethysmograms and arterial oxygen saturation from the auditory canal in conditions of compromised peripheral perfusion

Pulse oximeters rely on the technique of photoplethysmography (PPG) to estimate arterial oxygen saturation (SpO2). In conditions of poor peripheral perfusion such as hypotension, hypothermia, and vasoconstriction, the PPG signals detected are often small and noisy, or in some cases unobtainable. Hence, pulse oximeters produce erroneous SpO2 readings in these circumstances. The problem arises as most commercial pulse oximeter probes are designed to be attached to peripheral sites such as the finger or toes, which are easily affected by vasoconstriction. In order to overcome this problem, the ear canal was investigated as an alternative site for measuring reliable SpO2 on the hypothesis that blood flow to this central site is preferentially preserved. Novel miniature ear canal PPG sensors were developed along with a state of the art PPG processing unit and a data acquisition system to allow for PPG measurements from different depths and surfaces of the ear canal. A preliminary in vivo investigation on seven healthy volunteers has revealed that good quality PPG signals with high amplitude can be obtained from the posterior surface of the outer ear canal. Based on these observations, a second prototype probe suitable for acquisition of PPGs from the posterior surface of the outer ear canal was developed. A pilot study was then carried out on 15 healthy volunteers to validate the feasibility of measuring PPGs and SpO2 from the ear canal in conditions of induced local peripheral vasoconstriction (right hand immersion in ice water). The PPG signals acquired from the ear canal probe were compared with those obtained simultaneously from finger probes attached to the left and the right index fingers. Significant drop (p 45%) and right (> 50%) index fingers during the ice water immersion, while good quality PPG signals with relatively constant amplitude were obtained from the ear canal. Also, the SpO2 values showed that the ear canal pulse oximeter performed better than the two finger pulse oximeters (mean failure rate 30%). A second in vivo investigation was carried out in 15 healthy volunteers, where hypoperfusion was induced more naturally by exposing the volunteer to cold temperatures of 10C for 10min. Normalised Pulse Amplitude (NPA) and SpO2 was calculated from the PPG signals acquired from the ear canal, the finger and the earlobe. By the end of the cold exposure, a mean drop of > 80% was found in the NPA of finger PPGs. The % drop in the NPA of red and infrared earlobe PPG signals was 20% and 26% respectively. Contrarily to both these sites, the NPA of the ear canal PPGs had only dropped by 0.2% and 13% respectively. The SpO2 estimated from the finger sensor was below 90% in 5 volunteers (failure) by the end of the cold exposure. The earlobe pulse oximeter failed in 3 volunteers. The ear canal sensor on the other hand had only failed in 1 volunteer. These results strongly suggest that the ear canal may be used as a suitable alternative site for reliable monitoring of PPGs and SpO2 in cases of compromised peripheral perfusion

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

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Development of a Signal Processing Library for Extraction of SpO2, HR, HRV, and RR from Photoplethysmographic Waveforms

Non-invasive remote physiological monitoring of soldiers on the battlefield has the potential to provide fast, accurate status assessments that are key to improving the survivability of critical injuries. The development of WPI’s wearable wireless pulse oximeter, designed for field-based applications, has allowed for the optimization of important hardware features such as physical size and power management. However, software-based digital signal processing (DSP) methods are still required to perform physiological assessments. This research evaluated DSP methods that were capable of providing arterial oxygen saturation (SpO2), heart rate (HR), heart rate variability (HRV), and respiration rate (RR) measurements derived from data acquired using a single optical sensor. In vivo experiments were conducted to evaluate the accuracies of the processing methods across ranges of physiological conditions. Of the algorithms assessed, 13 SpO2 methods, 1 HR method, 6 HRV indices, and 4 RR methods were identified that provided clinically acceptable measurement accuracies and could potentially be employed in a wearable pulse oximeter

Heart Rate Estimation During Physical Exercise Using Wrist-Type Ppg Sensors

Accurate heart rate monitoring during intense physical exercise is a challenging problem due to the high levels of motion artifacts (MA) in photoplethysmography (PPG) sensors. PPG is a non-invasive optical sensor that is being used in wearable devices to measure blood flow changes using the property of light reflection and absorption, allowing the extraction of vital signals such as the heart rate (HR). However, the sensor is susceptible to MA which increases during physical activity. This occurs since the frequency range of movement and HR overlaps, difficulting correct HR estimation. For this reason, MA removal has remained an active topic under research. Several approaches have been developed in the recent past and among these, a Kalman filter (KF) based approach showed promising results for an accurate estimation and tracking using PPG sensors. However, this previous tracker was demonstrated for a particular dataset, with manually tuned parameters. Moreover, such trackers do not account for the correct method for fusing data. Such a custom approach might not perform accurately in practical scenarios, where the amount of MA and the heart rate variability (HRV) depend on numerous, unpredictable factors. Thus, an approach to automatically tune the KF based on the Expectation-Maximization (EM) algorithm, with a measurement fusion approach is developed. The applicability of such a method is demonstrated using an open-source PPG database, as well as a developed synthetic generation tool that models PPG and accelerometer (ACC) signals during predetermined physical activities