Conduit Artery Photoplethysmography and its Applications in the Assessment of Hemodynamic Condition

Elektroniskā versija nesatur pielikumusPromocijas darbā ir izstrādāta maģistrālo artēriju fotopletizmogrāfijas (APPG) metode hemodinamisko parametru novērtējumam. Pretstatot referentām metodēm, demonstrēta iespēja iegūt arteriālo elasticitāti raksturojošus parametrus, izmantojot APPG signāla formas analīzi (atvasinājuma un signāla formas aproksimācijas parametri) un ar APPG iegūtu pulsa izplatīšanās ātrumu unilaterālā gultnē. Izstrādāta APPG reģistrācijas standartizācija, mērījuma laikā nodrošinot optimālo sensora piespiedienu. Šis paņēmiens validēts ārējās ietekmes (sensora piespiediens) un hemodinamisko stāvokļu (perifērā vaskulārā pretestība) izmaiņās femorālā APPG signālā, identificējot būtiskākos faktorus APPG pielietojumos. Veikta APPG validācija asinsrites fizioloģijas un preklīniskā pētījumā demonstrējot APPG potenciālu pētniecībā un diagnostikā. Izstrādāts pulsa formas parametrizācijas paņēmiens, saistot fizioloģiskās un aproksimācijas modeļa komponentes. Atslēgas vārdi: maģistrālā artērija, fotopletizmogrāfija, arteriālā elasticitāte, metodes standartizācija, pulsa formas kvantifikācija, vazomocija, sepseThe doctoral thesis features the development of a conduit artery photoplethysmography technique (APPG) for the evaluation of hemodynamic parameters. Contrasting referent methods, the work demonstrates the possibility to receive parameters characterizing the arterial stiffness by means of APPG waveform analysis (derivation and waveform approximation parameters) and APPG obtained pulse wave velocity in a unilateral vascular bed. In this work APPG standardization technique was developed providing optimal probe contact pressure conditions. It was validated by altering the external factors (probe contact pressure) and hemodynamic conditions (peripheral vascular resistance) on the femoral APPG waveform identifying the key factors in APPG applications. The APPG validation in blood circulation physiology and a pre-clinical trial was performed demonstrating APPG potential in the extension of applications. An arterial waveform parameterization was developed relating the physiological wave to approximation model components. Keywords: conduit artery, photoplethysmography, arterial stiffness, method standardization, waveform parametrization, vasomotion, sepsi

Modelling arterial pressure waveforms using Gaussian functions and two-stage particle swarm optimizer

Changes of arterial pressure waveform characteristics have been accepted as risk indicators of cardiovascular diseases. Waveform modelling using Gaussian functions has been used to decompose arterial pressure pulses into different numbers of subwaves and hence quantify waveform characteristics. However, the fitting accuracy and computation efficiency of current modelling approaches need to be improved. This study aimed to develop a novel two-stage particle swarm optimizer (TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms (CAPW and RAPW) which were simultaneously recorded from twenty normal volunteers. The fitting accuracy and calculation efficiency of our TSPSO were compared with three published optimization methods: the Nelder-Mead, the modified PSO (MPSO), and the dynamic multiswarm particle swarm optimizer (DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error (MAE) of 1.1% for CAPW and 1.0% for RAPW, in comparison with 4.2% and 4.1% for Nelder-Mead, 2.0% and 1.9% for MPSO, and 1.2% and 1.1% for DMS-PSO. In addition, to achieve target MAE of 2.0%, the computation time of TSPSO was only 1.5 s, which was only 20% and 30% of that for MPSO and DMS-PSO, respectively

Mobile ECG and SPO2 Chest Pain Subjective Indicators of Patient with GPS Location in Smart Cities

Subjective indicators of chest pain in this article describe a system based on devices for measuring ECG (Electrocardiogram) and SPO2 (Saturation of peripheral Oxygen) signals with PPG (Photoplethysmograph). The development system used for ECG detection signals is created in the SMT technology technique. Preparing for ECG (Electrocardiogram) signal analysis is realized on the coordinator side of the WSN (Wireless Sensor Network) node and LabView application interface. Existing model RPC-50E, as SPO2 detector is used for a measurement device. SPO2 performance upgrade was realized by installing hardware module XBee PRO S2B in the function of router-end device working mode. Except for ZigBee wireless transmission technology, it leaves a possibility to expand with Bluetooth module. The technical description is strictly related to the location of the patients using the GPS signal when it comes to undesirable measuring sizes of each decentralized measuring device. Possibilities to measure beats per second (bps) is also included in the measurement device for saturation of peripheral oxygen. Smart city integration is part of upgraded hardware which operates on the level of hospital cloud. With existing smart city infrastructure, it is easier to connect mobile IoT (Internet of Things) logger of ECG and SPO2 measurements. This article describes only the main reasons for chest pain. Acute and chronic chest pain is defined with ECG signal waveforms in certain cases. Measuring graphs are based on 12 measurement points that lead to the electrocardiogram device

Characterization of the Autonomic Nervous System Response in Hyperbaric Environments.

Esta tesis se centra en el estudio de la respuesta del Sistema Nervioso Autónomo (ANS) en entornos hiperbáricos. Los entornos hiperbáricos son aquellos escenarios en los cuales la presión atmosférica aumenta y ese aumento en la presión produce cambios en el sistema cardio-respiratorio del sujeto para mantener la homeostasis.Estos cambios se ven reflejados en el ANS, cuya respuesta puede ser medida de manera no invasiva a través de la Variabilidad del Ritmo Cardiaco (HRV), extraída del electrocardiograma (ECG), o a través de la Variabilidad del Ritmo del Pulso (PRV), extraída de la señal de pulso pletismográfico (PPG). La descripción de los entornos hiperbáricos, de la actividad del ANS, de la relación entre ellos y de cómo la respuesta del ANS puede ser medida a través de las señales ECG y PPG, puede encontrarse en el Capítulo 1.En el Capítulo 2, para corroborar si la señal PPG proporciona la misma información en términos de respuesta del ANS que la señal ECG, ambas señales fueron registradas en sujetos en el interior de una cámara hiperbárica, con la presión atmosférica aumentando desde 1 atm a 3 y 5 atm y luego volviendo a 3 y 1 atm. La correlación y el análisis estadístico entre los parámetros en el dominio temporal y frecuencial extraídos de ambas señales demuestran que la PRV puede ser considerada una medida sustituta de la HRV para los sujetos en el interior de la cámara hiperbárica. Esto hace de la PPG una señal a ser considerada en los entornos hiperbáricos, dado que su sensor es más barato y fácil de colocar que los electrodos del ECG (especialmente debajo del agua), y además la PPG puede estimar otros parámetros, como la saturación de oxígeno, que no se pueden estimar con el ECG. También se ha realizado una caracterización de cómo el ANS reacciona ante los cambios de presión y ante el tiempo pasado en el entorno hiperbárico mediante los parámetros extraídos del ECG y la PPG, aumentando aquellos relacionados con el sistema parasimpático cuando la presión es alta y disminuyendo los parámetros relacionados con el sistema simpático conforme más tiempo se pasa dentro de la cámara.La respiración juega un papel importante en los entornos hiperbáricos por lo que se debe incluir la información respiratoria en el análisis del HRV/PRV, dado que se ha demostrado que los cambios en el patrón respiratorio pueden alterar la interpretación de la respuesta del ANS. Por lo tanto, una vez que se ha probado que la señal PPG debe ser tenida en cuenta en los entornos hiperbáricos, en el Capítulo 3 se ha realizado un estudio sobre la estimación de la frecuencia respiratoria colocando el sensor de la PPG en distintas localizaciones. Para hacer esto, se ha registrado la señal respiratoria junto con la señal PPG en el dedo y en la frente en 35 sujetos mientras respiraban espontáneamente y de forma controlada a un ritmo constante, desde 0,1 Hz a 0,6 Hz en pasos de 0,1 Hz. Cuatro señales respiratorias derivadas dela PPG (PDR) fueron extraídas de cada una de las señales PPG registradas. Éstas son: la variabilidad del ritmo del pulso (PRV), la variabilidad de la anchura del pulso (PWV), la variabilidad de la amplitud del pulso (PAV) y la variabilidad de la intensidad inducida de la respiración (RIIV). La frecuencia respiratoria fue estimada para cada una de las 4 señales PDR en ambas localizaciones del sensor PPG. Los resultados sugieren que: i) la estimación de la frecuencia respiratoria es mejor en frecuencias bajas (por debajo de 0,4 Hz); ii) las señales registradas en el dedo son mejores para la estimación que las registradas en la frente; iii) es mejor no incluir la señal RIIV para estimar la frecuencia respiratoria.Siguiendo con la señal PPG, no sólo la PRV contiene información sobre la respuesta del ANS. También la morfología de la PPG puede proporcionar una gran cantidad de información sobre el estado vascular o sobre la distensibilidad arterial, dado que la propagación de la presión del pulso en las arterias causa alteraciones en el volumen de la sangre y por lo tanto cambios en la forma de onda de la PPG.Esta es la razón por la que, en el Capítulo 4, se presenta un nuevo algoritmo para descomponer el pulso de la PPG en dos ondas relacionadas con los picos sistólico y diastólico. La primera onda es obtenida concatenando la pendiente de subida del pulso, desde el principio hasta el primer máximo, con ella misma girada horizontalmente. La segunda onda se modela como una curva lognormal, ajustando su máximo al pico diastólico. De estas dos ondas, se extraen la amplitud, el instante temporal, la anchura, el _área y algunos ratios. Este método se aplica en el conjunto de datos de la cámara hiperbárica para identificar alteraciones en la morfología del pulso PPG debido a la exposición de los sujetos a diferentes presiones atmosféricas.Los resultados del instante temporal y la anchura de la onda relacionada con el pico sistólico apuntan a una vasoconstricción cuando aumenta la presión, probablemente debida a una activación del sistema simpático sobre los vasos sanguíneos. Los resultados del instante temporal y de la anchura de la onda relacionada con el pico diastólico reflejan esta vasoconstricción y también una dependencia con el intervalo entre los pulsos. Por lo tanto, esta metodología permite extraer una gran cantidad de parámetros relacionados con la morfología de la PPG que se ven afectados por los cambios de presión en los entornos hiperbáricos.En los Capítulos 2 y 4, la respuesta del ANS se ha estudiado dentro de una cámara hiperbárica, donde la presión varía. Sin embargo, hay muchas variables que pueden afectar la respuesta cardiovascular del cuerpo durante el buceo, como son la posición del cuerpo del buceador, la actividad física, la temperatura del agua, respirar por el regulador de presión, y algunas más. Por esta razón, en el Capítulo 5 se estudia la respuesta del ANS en tres entornos hiperbáricos distintos: dentro de la cámara hiperbárica, donde sólo la presión varió; durante una actividad de buceo controlado en el mar, donde la presión cambió, pero los efectos de otras variables se minimizaron lo máximo posible; y durante una actividad de buceo no controlado en un pantano, donde más factores cambiaron entre las etapas basal y de inmersión.Se realiza una comparación de los parámetros extraídos de la HRV entre dos etapas (basal e inmersión) en cada conjunto de datos para estudiar como estos factores relacionados con la actividad de buceo afectan a la respuesta del ANS. Para hacer esta comparación, en lugar de los parámetros frecuenciales clásicos, los métodos Principal Dynamic Mode (PDM) y Orthogonal Subspace Projection (OSP) se usan para tener en cuenta las interacciones lineales y no lineales y para tratar con la componente respiratoria que puede afectar a la respuesta del ANS, respectivamente.Los resultados del método OSP indican que la mayoría de la variación de la HRVno puede ser descrita por los cambios en la respiración, por lo que los cambios en la respuesta del ANS pueden aparecer por otros factores. Los parámetros temporales reflejan la activación vagal en la cámara hiperbárica y en el buceo controlado debido al efecto de la presión. En el buceo no controlado, sin embargo, la actividad simpática parece ser la dominante, debido a los efectos de otros factores como la actividad física, el entorno estimulante y el hecho de respirar a través del regulador durante la inmersión. Como resumen, se ha realizado una descripción detallada de los cambios en todos los posibles factores que pueden afectar a la respuesta del ANS entre las etapas basal y de inmersión en los distintos entornos hiperbáricos para una mejor explicación de los resultados.This dissertation focuses on the study of the Autonomic Nervous System (ANS) response in hyperbaric environments. Hyperbaric environments are those scenarios in which atmospheric pressure increases and this increase in pressure produces changes in the cardio-respiratory system of the subject to maintain the homeostasis. These changes are reflected in the ANS, whose response can be measured in a non-invasive way with the Heart Rate Variability (HRV), extracted from the electrocardiogram (ECG) or with the Pulse Rate Variability (PRV), extracted from the photoplethysmogram (PPG). The description of the hyperbaric environments, the ANS activity, the relationship between them and how the ANS response can be measured through ECG and PPG signals can be found in Chapter 1. In Chapter 2, to corroborate if PPG signal provides the same information in terms of ANS response than ECG signal, both signals were recorded for subjects inside a hyperbaric chamber when the atmospheric pressure varied from 1 atm to 3 atm and 5 atm and the coming back to 3 and 1 atm. The correlation and statistical analysis between time and frequency domain parameters extracted from both signals demonstrates that PRV can be considered as a surrogate measurement of HRV inside a hyperbaric chamber. This makes PPG a signal to be considered in hyperbaric environments, since its sensor is cheaper and easier to place than ECG electrodes (especially under the water), and PPG can estimate some parameters, as the oxygen saturation, than ECG cannot. Also a characterization of how the ANS reacts to pressure changes and the time spent in the hyperbaric environment is done with ECG and PPG parameters, increasing those related with the parasympathetic system when the pressure is high and decreasing the heart rate and the parameters related with the sympathetic system when more time is spent inside the chamber. Respiration plays an important role in hyperbaric environments, so it is important to include respiratory information in the HRV/PRV analysis, since it has been shown that changes in the respiratory pattern could alter the interpretation of the ANS response. Therefore, once that PPG signal has been proved as an interesting signal to consider in hyperbaric environments, in Chapter 3 a study about the respiratory rate estimation from different locations of the PPG sensor is performed. To do that, the respiratory signal together with finger and forehead PPG were recorded from 35 subjects while breathing spontaneously, and during controlled respiration experiments at a constant rate from 0.1 Hz to 0.6 Hz, in 0.1 Hz steps. Four PPG derived respiratory (PDR) signals were extracted from each one of the recorded PPG signals: pulse rate variability (PRV), pulse width variability (PWV), pulse amplitude variability (PAV) and the respiratory-induced intensity variability (RIIV). Respiratory rate was estimated from each one of the 4 PDR signals for both PPG sensor locations. Results suggest that: i) respiratory rate estimation is better at lower rates (0.4 Hz and below); ii) the signals recorded at the finger are better than those at the forehead to estimate respiratory rate; iii) it is better not to include RIIV signal to estimate the respiratory rate. Following with the PPG signal, not only PRV contains information about the ANS response. Also, PPG morphology can provide a great amount of information about vascular assessment or arterial compliance, since pulse pressure propagation in arteries causes alterations in blood volume and therefore changes in the PPG pulse shape. That is the reason why, in Chapter 4, a new algorithm to decompose the PPG pulse into two waves related with the systolic and the diastolic peaks is presented. The first wave is obtained concatenating the up-slope from the beginning to the first maximum with itself flipped horizontally. The second wave is modelled by a lognormal curve, adjusting its maximum to the diastolic peak. From these two waves, the amplitude, the time instant, the width, the area and some ratios are extracted. This method is applied in a hyperbaric chamber dataset to identify alterations in the morphology of the PPG pulse due to the exposure of the subjects to different pressures. Results of the time and width of the wave related with the systolic peak point out to a vasoconstriction when the pressure increases, probably due to an activation of the sympathetic system on the blood vessels. Results of the time and width of the wave related with the diastolic peak reflect the vasoconstriction but also a dependency with the pulse-to-pulse interval. Therefore this methodology allows to extract a great set of parameters related with the PPG morphology that are affected by the change of pressure in hyperbaric environments. In Chapters 2 and 4, the ANS response is studied inside a hyperbaric chamber, where the pressure varies. However, there are many variables that could affect the body's cardiovascular response during diving, such as diver body position, physical activity, water temperature, breathing with a scuba mouthpieces and more. This is the reason why in Chapter 5 the ANS response is studied in three different hyperbaric environments: inside a hyperbaric chamber, where only the pressure varied; during a controlled dive in the sea, where the pressure changed but the effects of other factors were minimized; and during an uncontrolled dive in a reservoir, where more factors differed from baseline to immersion stage. A comparison of the HRV features between the two stages (baseline and immersion) in each dataset is carried out to study how these factors related to scuba diving activity affect the ANS response. To do this comparison, instead of the classic frequency methods, the Principal Dynamic Mode (PDM) and the Orthogonal Subspace Projection (OSP) methods are used to account for linear and non-linear interactions and to deal with the respiratory component that could affect the ANS response, respectively. OSP results indicate that most of the variation in the heart rate variability cannot be described by changes in the respiration, so changes in ANS response can be assigned to other factors. Time domain parameters reflect vagal activation in the hyperbaric chamber and in the controlled dive because of the effect of pressure. In the uncontrolled dive, sympathetic activity seems to be dominant, due to the effects of other factors such as physical activity, the challenging environment, and the influence of breathing through the scuba mask during immersion. In summary, a careful description of the changes in all the possible factors that could affect the ANS response between baseline and immersion stages in hyperbaric environments is performed for better explanation of the results.<br /

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

Baroreflex sensitivity measured by pulse photoplethysmography

Novel methods for assessing baroreflex sensitivity (BRS) using only pulse photoplethysmography (PPG) signals are presented. Proposed methods were evaluated with a data set containing electrocardiogram (ECG), blood pressure (BP), and PPG signals from 17 healthy subjects during a tilt table test. The methods are based on a surrogate of a index, which is defined as the power ratio of RR interval variability (RRV) and that of systolic arterial pressure series variability (SAPV). The proposed a index surrogates use pulse-to-pulse interval series variability (PPV) as a surrogate of RRV, and different morphological features of the PPG pulse which have been hypothesized to be related to BP, as series surrogates of SAPV. A time-frequency technique was used to assess BRS, taking into account the non-stationarity of the protocol. This technique identifies two time-varying frequency bands where RRV and SAPV (or their surrogates) are expected to be coupled: the low frequency (LF, inside 0.04–0.15 Hz range), and the high frequency (HF, inside 0.15–0.4 Hz range) bands. Furthermore, time-frequency coherence is used to identify the time intervals when the RRV and SAPV (or their surrogates) are coupled. Conventional a index based on RRV and SAPV was used as Gold Standard. Spearman correlation coefficients between conventional a index and its PPG-based surrogates were computed and the paired Wilcoxon statistical test was applied in order to assess whether the indices can find significant differences (p < 0.05) between different stages of the protocol. The highest correlations with the conventional a index were obtained by the a-index-surrogate based on PPV and pulse up-slope (PUS), with 0.74 for LF band, and 0.81 for HF band. Furthermore, this index found significant differences between rest stages and tilt stage in both LF and HF bands according to the paired Wilcoxon test, as the conventional a index also did. These results suggest that BRS changes induced by the tilt test can be assessed with high correlation by only a PPG signal using PPV as RRV surrogate, and PPG morphological features as SAPV surrogates, being PUS the most convenient SAPV surrogate among the studied ones

Baroreflex Sensitivity Measured by Pulse Photoplethysmography

Novel methods for assessing baroreflex sensitivity (BRS) using only pulse photoplethysmography (PPG) signals are presented. Proposed methods were evaluated with a data set containing electrocardiogram (ECG), blood pressure (BP), and PPG signals from 17 healthy subjects during a tilt table test. The methods are based on a surrogate of α index, which is defined as the power ratio of RR interval variability (RRV) and that of systolic arterial pressure series variability (SAPV). The proposed α index surrogates use pulse-to-pulse interval series variability (PPV) as a surrogate of RRV, and different morphological features of the PPG pulse which have been hypothesized to be related to BP, as series surrogates of SAPV. A time-frequency technique was used to assess BRS, taking into account the non-stationarity of the protocol. This technique identifies two time-varying frequency bands where RRV and SAPV (or their surrogates) are expected to be coupled: the low frequency (LF, inside 0.04–0.15 Hz range), and the high frequency (HF, inside 0.15–0.4 Hz range) bands. Furthermore, time-frequency coherence is used to identify the time intervals when the RRV and SAPV (or their surrogates) are coupled. Conventional α index based on RRV and SAPV was used as Gold Standard. Spearman correlation coefficients between conventional α index and its PPG-based surrogates were computed and the paired Wilcoxon statistical test was applied in order to assess whether the indices can find significant differences (p &lt; 0.05) between different stages of the protocol. The highest correlations with the conventional α index were obtained by the α-index-surrogate based on PPV and pulse up-slope (PUS), with 0.74 for LF band, and 0.81 for HF band. Furthermore, this index found significant differences between rest stages and tilt stage in both LF and HF bands according to the paired Wilcoxon test, as the conventional α index also did. These results suggest that BRS changes induced by the tilt test can be assessed with high correlation by only a PPG signal using PPV as RRV surrogate, and PPG morphological features as SAPV surrogates, being PUS the most convenient SAPV surrogate among the studied ones

Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography

Objective Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content. Methods PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests. Results From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz. Conclusion The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application. Significance The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal