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 /

Enhancing safety in hyperbaric environments through analysis of autonomic nervous system responses: a comparison of dry and humid conditions

Diving can have significant cardiovascular effects on the human body and increase the risk of developing cardiac health issues. This study aimed to investigate the autonomic nervous system (ANS) responses of healthy individuals during simulated dives in hyperbaric chambers and explore the effects of the humid environment on these responses. Electrocardiographic- and heart-rate-variability (HRV)-derived indices were analyzed, and their statistical ranges were compared at different depths during simulated immersions under dry and humid conditions. The results showed that humidity significantly affected the ANS responses of the subjects, leading to reduced parasympathetic activity and increased sympathetic dominance. The power of the high-frequency band of the HRV after removing the influence of respiration, PHF⊥¯, and the number of pairs of successive normal-to-normal intervals that differ by more than 50 ms divided by the total number of normal-to-normal intervals, pNN50¯, indices were found to be the most informative in distinguishing the ANS responses of subjects between the two datasets. Additionally, the statistical ranges of the HRV indices were calculated, and the classification of subjects as “normal” or “abnormal” was determined based on these ranges. The results showed that the ranges were effective at identifying abnormal ANS responses, indicating the potential use of these ranges as a reference for monitoring the activity of divers and avoiding future immersions if many indices are out of the normal ranges. The bagging method was also used to include some variability in the datasets’ ranges, and the classification results showed that the ranges computed without proper bagging represent reality and its associated variability. Overall, this study provides valuable insights into the ANS responses of healthy individuals during simulated dives in hyperbaric chambers and the effects of humidity on these responses

Autonomic Nervous System characterization in hyperbaric environments considering respiratory component and non-linear analysis of Heart Rate Variability

Objectives: an evaluation of Principal Dynamic Mode (PDM) and Orthogonal Subspace Projection (OSP) methods to characterize the Autonomic Nervous System (ANS) response in three different hyperbaric environments was performed. Methods: ECG signals were recorded in two different stages (baseline and immersion) in three different hyperbaric environments: (a) inside a hyperbaric chamber, (b) in a controlled sea immersion, (c) in a real reservoir immersion. Time-domain parameters were extracted from the RR series of the ECG. From the Heart Rate Variability signal (HRV), classic Power Spectral Density (PSD), PDM (a non-linear analysis of HRV which is able to separate sympathetic and parasympathetic activities) and OSP (an analysis of HRV which is able to extract the respiratory component) methods were used to assess the ANS response. Results: PDM and OSP parameters follows the same trend when compared to the PSD ones for the hyperbaric chamber dataset. Comparing the three hyperbaric scenarios, significant differences were found: i) heart rate decreased and RMSSD increased in the hyperbaric chamber and the controlled dive, but they had the opposite behavior during the uncontrolled dive; ii) power in the OSP respiratory component was lower than power in the OSP residual component in cases a and c; iii) PDM and OSP methods showed a significant increase in sympathetic activity during both dives, but parasympathetic activity increased only during the uncontrolled dive. Conclusions: PDM and OSP methods could be used as an alternative measurement of ANS response instead of the PSD method. 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 sum, 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 needed for better interpretation of the results

Autonomic nervous system measurement in hyperbaric environments using ECG and PPG signals

The main aim of this work was to characterise the Autonomic Nervous System (ANS) response in hyper- baric environments using electrocardiogram (ECG) and pulse- photoplethysmogram (PPG) signals. To that end, 26 subjects were introduced into a hyperbaric chamber and five stages with different atmospheric pressures (1 atm; descent to 3 and 5 atm; ascent to 3 and 1 atm) were recorded. Respiratory information was extracted from the ECG and PPG signals and a combined respiratory rate was studied. This information was also used to analyse Heart Rate Variability (HRV) and Pulse Rate Variability (PRV). The database was cleaned by eliminating those cases where the respiratory rate dropped into the low frequency band (LF: 0.04-0.15 Hz) and those in which there was a discrepancy between the respiratory rates estimated using the ECG and PPG signals. Classical temporal and frequency indices were calculated in such cases. The ECG results showed a time-related depen- dency, with the heart rate and sympathetic markers (normalised power in LF and LF/HF ratio) decreasing as more time was spent inside the hyperbaric environment. A dependency between the atmospheric pressure and the parasympathetic response, as reflected in the high frequency band power (HF: 0.15-0.40 Hz), was also found, with power increasing with atmospheric pressure. The combined respiratory rate also reached a maximum in the deepest stage, thus highlighting a significant difference between this stage and the first one. The PPG data gave similar findings and also allowed the oxygen saturation to be computed, therefore we propose the use of this signal for future studies in hyperbaric environments

Human adaptations to multiday saturation on NASA NEEMO

Human adaptation to extreme environments has been explored for over a century to understand human psychology, integrated physiology, comparative pathologies, and exploratory potential. It has been demonstrated that these environments can provide multiple external stimuli and stressors, which are sufficient to disrupt internal homeostasis and induce adaptation processes. Multiday hyperbaric and/or saturated (HBS) environments represent the most understudied of environmental extremes due to inherent experimental, analytical, technical, temporal, and safety limitations. National Aeronautic Space Agency (NASA) Extreme Environment Mission Operation (NEEMO) is a space-flight analog mission conducted within Florida International University's Aquarius Undersea Research Laboratory (AURL), the only existing operational and habitable undersea saturated environment. To investigate human objective and subjective adaptations to multiday HBS, we evaluated aquanauts living at saturation for 9-10 days via NASA NEEMO 22 and 23, across psychologic, cardiac, respiratory, autonomic, thermic, hemodynamic, sleep, and body composition parameters. We found that aquanauts exposed to saturation over 9-10 days experienced intrapersonal physical and mental burden, sustained good mood and work satisfaction, decreased heart and respiratory rates, increased parasympathetic and reduced sympathetic modulation, lower cerebral blood flow velocity, intact cerebral autoregulation and maintenance of baroreflex functionality, as well as losses in systemic bodyweight and adipose tissue. Together, these findings illustrate novel insights into human adaptation across multiple body systems in response to multiday hyperbaric saturation

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