Finger and forehead PPG signal comparison for respiratory rate estimation

Objective: an evaluation of the location of the photoplethysmogram (PPG) sensor for respiratory rate estimation is performed. Approach: finger-PPG, forehead-PPG, and respiratory signal were simultaneously 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. In addition, different combinations of PDR signals, power distribution of the respiratory frequency range and differences of the morphological parameters extracted from both PPG signals have been analysed. Main results: results show a better performance in terms of successful estimation and relative error when: i) PPG signal is recorded in the finger; ii) the respiratory rate is less than 0.4 Hz; iii) RIIV signal is not considered. Furthermore, lower spectral power around the respiratory rate in the PDR signals recorded from the forehead was observed. Significance: these results suggest that respiratory rate estimation is better at lower rates (0.4 Hz and below) and that finger is better than forehead to estimate respiratory rate

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 /

Estimación robusta de la diferencia del tiempo de tránsito del pulso sanguíneo a partir de señales fotopletismográficas

En el presente trabajo se va a estudiar la posibilidad de detectar estrés mental utilizando técnicas no invasivas basadas en la señal fotopletismográfica de pulso (PPG). Para ello se pretende detectar cambios en la velocidad de pulso arterial (PWV), utilizando señales de PPG tomadas en dos puntos distintos del árbol arterial con las que poder medir el tiempo de llegada de pulso arterial a la periferia (PAT) y la diferencia de ese tiempo de llegada entre dos puntos de la periferia distintos (PTTD). Tanto el PAT como el PTTD han sido propuestas en la bibliografía como medidas influenciados por el Tiempo de Tránsito de Pulso (PTT), este último capaz de medir cambios en la dinámica cardiovascular. Sin embargo, el PTTD, al contrario que el PAT, no necesita del electrocardiograma (ECG) para ser obtenido y no está influenciado por el periodo de pre-eyección (PEP) -un intervalo de tiempo en la sístole ventricular que cambia pulso a pulso- el cual genera que el PAT pierda la relación con el PTT, dos factores importantes que aventajan al PTTD frente al PAT. Primero, se estudia de fiabilidad de los puntos fiduciales para la detección de los pulsos de la señal PPG y con ésto comprobar cuál es el método con la mayor precisión. Se demuestra mediante diversos análisis que el mejor punto para detectar los pulsos corresponde al valor de la PPG en el instante de máxima pendiente (valor máximo en la primera derivada). Resulta necesario implementar un detector de artefactos ya que el método de adquisición de la PPG es muy sensible a ellos pudiendo llegar a haber segmentos en los que la señal registrada es absolutamente inutilizable. Posteriormente, se analizan 14 voluntarios sanos sometidos a un protocolo de estrés y se realiza un test estadístico para comprobar la validez del método propuesto. Los resultados muestran que la desviación estándar de la PTTD tiene la capacidad estadística suficiente como para discernir entre estados de estrés y de relajación, para cada uno de los sujetos por separado. Además, se puede ver una tendencia descendente generalizada del descenso de la PTTD en situación de estrés con respecto a relajación. %Sin embargo, resultará necesario repetir el análisis con una muestra de señales mayor ya que se dispone de pocos sujetos en la base de datos utilizada, ya que la calidad de la señal de PPG que se registró en la frente es muy mala y hay muy pocos sujetos con los que se puede computar la PTTD. A modo de conclusión, se ha visto que la PTTD contiene información fisiológica que puede ser interesante para la detección de estrés. A su vez, también es una técnica potencialmente interesante para otros tipos de aplicaciones clínicas tales como la estimación no invasiva de la presión arterial o la evaluación de la rigidez arterial, pero se necesita estudiar la adecuación de ésta en cada escenario en particular. Además, como la PTTD se puede medir a partir de únicamente dos señales PPG, la técnica es idónea para dispositivos wearable y smartphones

PHOTOPLETHYSMOGRAPHIC WAVEFORM ANALYSIS DURING LOWER BODY NEGATIVE PRESSURE SIMULATED HYPOVOLEMIA AS A TOOL TO DISTINGUISH REGIONAL DIFFERENCES IN MICROVASCULAR BLOOD FLOW REGULATION.

The purpose of this investigation was to explore modulation of the photoplethsymographic (PPG) waveform in the setting of simulated hypovolemia as a tool to distinguish regional differences in regulation of the microvasculature. The primary goal was to glean useful physiological and clinical information as it pertains to these regional differences in regulation of microvascular blood flow. This entailed examining the cardiovascular, autonomic nervous, and respiratory systems interplay in the functional hemodynamics of regulation of microvascular blood flow to both central (ear, forehead) and peripheral (finger) sites. We monitored ten healthy volunteers (both men and women age 24-37 ) non-invasively with central and peripheral photoplethysmographs and laser Doppler flowmeters during Lower Body Negative Pressure (LBNP). Waveform amplitude, width, and oscillatory changes were characterized using waveform analysis software (Chart, ADInstruments). Data were analyzed with the Wilcoxon Signed Ranks Test, paired t-tests, and linear regression. Finger PPG amplitude decreased by 34.6 ± 17.6% (p = 0.009) between baseline and the highest tolerated LBNP. In contrast, forehead amplitude changed by only 2.4 ± 16.0% (p=NS). Forehead and finger PPG width decreased by 48.4% and 32.7%, respectively. Linear regression analysis of the forehead and finger PPG waveform widths as functions of time generated slopes of -1.113 (R = -0.727) and -0.591 (R = -0.666), respectively. A 150% increase in amplitude density of the ear PPG waveform was noted within the range encompassing the respiratory frequency (0.19-0.3Hz) (p=0.021) attributable to changes in stroke volume. We also noted autonomic modulation of the ear PPG signal in a different frequency band (0.12 0.18 Hz). The data indicate that during a hypovolemic challenge, healthy volunteers had a relative sparing of central cutaneous blood flow when compared to a peripheral site as indicated by observable and quantifiable changes in the PPG waveform. These results are the first documentation of a local vasodilatation at the level of the terminal arterioles of the forehead that may be attributable to recently documented cholinergic mechanisms on the microvasculature

Reliability of pulse photoplethysmography sensors: Coverage using different setups and body locations

Pulse photoplethysmography (PPG) is a simple and economical technique for obtaining cardiovascular information. In fact, PPG has become a very popular technology among wearable devices. However, the PPG signal is well-known to be very vulnerable to artifacts, and a good quality signal cannot be expected for most of the time in daily life. The percentage of time that a given measurement can be estimated (e.g., pulse rate) is denoted coverage (C), and it is highly dependent on the subject activity and on the configuration of the sensor, location, and stability of contact. This work aims to quantify the coverage of PPG sensors, using the simultaneously recorded electrocardiogram as a reference, with the PPG recorded at different places in the body and under different stress conditions. While many previous works analyzed the feasibility of PPG as a surrogate for heart rate variability analysis, there exists no previous work studying coverage to derive other cardiovascular indices. We report the coverage not only for estimating pulse rate (PR) but also for estimating pulse arrival time (PAT) and pulse amplitude variability (PAV). Three different datasets are analyzed for this purpose, consisting of a tilt-table test, an acute emotional stress test, and a heat stress test. The datasets include 19, 120, and 51 subjects, respectively, with PPG at the finger and at the forehead for the first two datasets and at the earlobe, in addition, for the latter. C ranges from 70% to 90% for estimating PR. Regarding the estimation of PAT, C ranges from 50% to 90%, and this is very dependent on the PPG sensor location, PPG quality, and the fiducial point (FP) chosen for the delineation of PPG. In fact, the delineation of the FP is critical in time for estimating derived series such as PAT due to the small dynamic range of these series. For the estimation of PAV, the C rates are between 70% and 90%. In general, lower C rates have been obtained for the PPG at the forehead. No difference in C has been observed between using PPG at the finger or at the earlobe. Then, the benefits of using either will depend on the application. However, different C rates are obtained using the same PPG signal, depending on the FP chosen for delineation. Lower C is reported when using the apex point of the PPG instead of the maximum flow velocity or the basal point, with a difference from 1% to even 10%. For further studies, each setup should first be analyzed and validated, taking the results and guidelines presented in this work into account, to study the feasibility of its recording devices with respect to each specific application

Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring

Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring

Wearable in-ear pulse oximetry: theory and applications

Wearable health technology, most commonly in the form of the smart watch, is employed by millions of users worldwide. These devices generally exploit photoplethysmography (PPG), the non-invasive use of light to measure blood volume, in order to track physiological metrics such as pulse and respiration. Moreover, PPG is commonly used in hospitals in the form of pulse oximetry, which measures light absorbance by the blood at different wavelengths of light to estimate blood oxygen levels (SpO2). This thesis aims to demonstrate that despite its widespread usage over many decades, this sensor still possesses a wealth of untapped value. Through a combination of advanced signal processing and harnessing the ear as a location for wearable sensing, this thesis introduces several novel high impact applications of in-ear pulse oximetry and photoplethysmography. The aims of this thesis are accomplished through a three pronged approach: rapid detection of hypoxia, tracking of cognitive workload and fatigue, and detection of respiratory disease. By means of the simultaneous recording of in-ear and finger pulse oximetry at rest and during breath hold tests, it was found that in-ear SpO2 responds on average 12.4 seconds faster than the finger SpO2. This is likely due in part to the ear being in close proximity to the brain, making it a priority for oxygenation and thus making wearable in-ear SpO2 a good proxy for core blood oxygen. Next, the low latency of in-ear SpO2 was further exploited in the novel application of classifying cognitive workload. It was found that in-ear pulse oximetry was able to robustly detect tiny decreases in blood oxygen during increased cognitive workload, likely caused by increased brain metabolism. This thesis demonstrates that in-ear SpO2 can be used to accurately distinguish between different levels of an N-back memory task, representing different levels of mental effort. This concept was further validated through its application to gaming and then extended to the detection of driver related fatigue. It was found that features derived from SpO2 and PPG were predictive of absolute steering wheel angle, which acts as a proxy for fatigue. The strength of in-ear PPG for the monitoring of respiration was investigated with respect to the finger, with the conclusion that in-ear PPG exhibits far stronger respiration induced intensity variations and pulse amplitude variations than the finger. All three respiratory modes were harnessed through multivariate empirical mode decomposition (MEMD) to produce spirometry-like respiratory waveforms from PPG. It was discovered that these PPG derived respiratory waveforms can be used to detect obstruction to breathing, both through a novel apparatus for the simulation of breathing disorders and through the classification of chronic obstructive pulmonary disease (COPD) in the real world. This thesis establishes in-ear pulse oximetry as a wearable technology with the potential for immense societal impact, with applications from the classification of cognitive workload and the prediction of driver fatigue, through to the detection of chronic obstructive pulmonary disease. The experiments and analysis in this thesis conclusively demonstrate that widely used pulse oximetry and photoplethysmography possess a wealth of untapped value, in essence teaching the old PPG sensor new tricks.Open Acces

Systems and methods for physiological signal enhancement and biometric extraction using non-invasive optical sensors

A system and method for signal processing to remove unwanted noise components including: (i) wavelength-independent motion artifacts such as tissue, bone and skin effects, and (ii) wavelength-dependent motion artifact/noise components such as venous blood pulsation and movement due to various sources including muscle pump, respiratory pump and physical perturbation. Disclosed are methods, analytics, and their uses for reliable perfusion monitoring, arterial oxygen saturation monitoring, heart rate monitoring during daily activities and in hospital settings and for extraction of physiological parameters such as respiration information, hemodynamic parameters, venous capacity, and fluid responsiveness. The system and methods disclosed are extendable to include monitoring platforms for perfusion, hypoxia, arrhythmia detection, airway obstruction detection and sleep disorders including apnea.Board of Regents, University of Texas Syste