Robust CNN-based Respiration Rate Estimation for Smartwatch PPG and IMU

Respiratory rate (RR) serves as an indicator of various medical conditions, such as cardiovascular diseases and sleep disorders. These RR estimation methods were mostly designed for finger-based PPG collected from subjects in stationary situations (e.g., in hospitals). In contrast to finger-based PPG signals, wrist-based PPG are more susceptible to noise, particularly in their low frequency range, which includes respiratory information. Therefore, the existing methods struggle to accurately extract RR when PPG data are collected from wrist area under free-living conditions. The increasing popularity of smartwatches, equipped with various sensors including PPG, has prompted the need for a robust RR estimation method. In this paper, we propose a convolutional neural network-based approach to extract RR from PPG, accelerometer, and gyroscope signals captured via smartwatches. Our method, including a dilated residual inception module and 1D convolutions, extract the temporal information from the signals, enabling RR estimation. Our method is trained and tested using data collected from 36 subjects under free-living conditions for one day using Samsung Gear Sport watches. For evaluation, we compare the proposed method with four state-of-the-art RR estimation methods. The RR estimates are compared with RR references obtained from a chest-band device. The results show that our method outperforms the existing methods with the Mean-Absolute-Error and Root-Mean-Square-Error of 1.85 and 2.34, while the best results obtained by the other methods are 2.41 and 3.29, respectively. Moreover, compared to the other methods, the absolute error distribution of our method was narrow (with the lowest median), indicating a higher level of agreement between the estimated and reference RR values

Methods and systems for extracting venous pulsation and respiratory information from photoplethysmographs

A system and method for separating a venous component and an arterial component from a red signal and an infrared signal of a PPG sensor is provided. The method uses the second order statistics of venous and arterial signals to separate the venous andarterial signals. After reliable separation of the venous and thearterial component signals,the component signals can be used for different purposes. In a preferred embodiment, the respiratory signal, pattern, and rate are extracted from the separated venous component and a reliable ?ratio of ratios? is extracted for SpO, using only the arterial component of the PPG signals. The disclosed embodiments enable real-time continuous monitoring of respiration pattern/rate and site-independentarterial oxygen saturation.Board of Regents, University of Texas Syste

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

Due to the high demands of tiny, compact, lightweight, and low-cost photoplethysmogram (PPG) monitoring devices, these devices are resource-constrained including limited battery power. Consequently, it highly demands frequent charge or battery replacement in the case of continuous PPG sensing and transmission. Further, PPG signals are often severely corrupted under ambulatory and exercise recording conditions, leading to frequent false alarms. In this paper, we propose a unified quality-aware compression and pulse-respiration rates estimation framework for reducing energy consumption and false alarms of wearable and edge PPG monitoring devices by exploring predictive coding techniques for jointly performing signal quality assessment (SQA), data compression and pulse rate (PR) and respiration rate (RR) estimation without the use of different domains of signal processing techniques that can be achieved by using the features extracted from the smoothed prediction error signal. By using the five standard PPG databases, the performance of the proposed unified framework is evaluated in terms of compression ratio (CR), mean absolute error (MAE), false alarm reduction rate (FARR), processing time (PT) and energy saving (ES). The compression, PR, RR estimation, and SQA results are compared with the existing methods and results of uncompressed PPG signals with sampling rates of 125 Hz and 25 Hz. The proposed unified qualityaware framework achieves an average CR of 4%, SQA (Se of 92.00%, FARR of 84.87%), PR (MAE: 0.46 ±1.20) and RR (MAE: 1.75 (0.65-4.45), PT (sec) of 15.34 ±0.01) and ES of 70.28% which outperforms the results of uncompressed PPG signal with a sampling rate of 125 Hz. Arduino Due computing platformbased implementation demonstrates the real-time feasibility of the proposed unified quality-aware PRRR estimation and data compression and transmission framework on the limited computational resources. Thus, it has great potential in improving energy-efficiency and trustworthiness of wearable and edge PPG monitoring devices.publishedVersio

Rapid Extraction of Respiratory Waveforms from Photoplethysmography: A Deep Encoder Approach

Much of the information of breathing is contained within the photoplethysmography (PPG) signal, through changes in venous blood flow, heart rate and stroke volume. We aim to leverage this fact, by employing a novel deep learning framework which is a based on a repurposed convolutional autoencoder. Our model aims to encode all of the relevant respiratory information contained within photoplethysmography waveform, and decode it into a waveform that is similar to a gold standard respiratory reference. The model is employed on two photoplethysmography data sets, namely Capnobase and BIDMC. We show that the model is capable of producing respiratory waveforms that approach the gold standard, while in turn producing state of the art respiratory rate estimates. We also show that when it comes to capturing more advanced respiratory waveform characteristics such as duty cycle, our model is for the most part unsuccessful. A suggested reason for this, in light of a previous study on in-ear PPG, is that the respiratory variations in finger-PPG are far weaker compared with other recording locations. Importantly, our model can perform these waveform estimates in a fraction of a millisecond, giving it the capacity to produce over 6 hours of respiratory waveforms in a single second. Moreover, we attempt to interpret the behaviour of the kernel weights within the model, showing that in part our model intuitively selects different breathing frequencies. The model proposed in this work could help to improve the usefulness of consumer PPG-based wearables for medical applications, where detailed respiratory information is required

Development of machine learning schemes for use in non-invasive and continuous patient health monitoring

Stephanie Baker developed machine learning schemes for the non-invasive and continuous measurement of blood pressure and respiratory rate from heart activity waveforms. She also constructed machine learning models for mortality risk assessment from vital sign variations. This research contributes several tools that offer significant advancements in patient monitoring and wearable healthcare

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

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 /

Haemodynamic optimization of cardiac resynchronization therapy

Heart failure carries a very poor prognosis, unless treated with the appropriate pharmacological agents which, have been evaluated in large randomized clinical trials and have demonstrated improvements in morbidity and mortality of this cohort of patients. A significant proportion of these patients develop conduction abnormalities involving both the atrioventricular node and also the specialised conduction tissue (bundle of His and Purkinje fibers) of the ventricular myocardium which is most commonly evidenced by the presence of a wide QRS, typically left bundle branch block. The net effect of these conduction abnormalities is inefficient filling and contraction of the left ventricle. The presence of these conduction abnormalities is an additional strong marker of poor prognosis. Over the last 15 years pacing treatments have been developed aimed at mitigating the conduction disease. Large scale randomized multicentre trials have repeatedly demonstrated the effectiveness of cardiac pacing, officially recognized as cardiac resynchronization therapy (CRT). This mode of pacing therapy has undoubtedly had a positive impact on both the morbidity and mortality of these patients. Despite the large advancement in the management of heart failure patients by pacing therapies, a significant proportion of patients (30%) being offered CRT are classed as non-responders. Many explanations have been put forward for the lack of response. The presence of scar at the pacing site with failure to capture or delayed capture of myocardium, too much left ventricular scar therefore minimal contractile response, incorrect pacing site due to often limited anatomical options of lead placement and insufficient programming i.e optimization, of pacemaker settings such as the AV and VV delay are just some of the suggested areas perceived to be responsible for the lack of patients’ response to cardiac resynchronization therapy. The effect of optimization of pacemaker settings is a field that has been investigated extensively in the last decade. Disappointingly, current methods of assessing the effect of optimization of pacemaker settings on several haemodynamic parameters, such as cardiac output and blood pressure, are marred with very poor reproducibility, so measurement of any effect of optimization is close to being meaningless. Moreover, detailed understanding of the effects of CRT on coronary physiology and cardiac mechanoenergetics is equally, disappointingly, lacking. In this thesis, I investigated the acute effects of cardiac resynchronization therapy and AV optimization on coronary physiology and cardiac mechanoenergetics. This was accomplished using very detailed and demanding series of invasive catheterization studies. I used novel analytical mathematical techniques, such as wave intensity analysis, which have been developed locally and this provided a unique insight of the important physiological entities defining coronary physiology and cardiac mechanics. I explored in detail the application and reliability of photoplethysmography as a tool for non-invasive optimization of the AV delay. Photoplethysmography has the potential of miniaturization and therefore implantation alongside pacemaker devices. I compared current optimization techniques (Echocardiography and ECG) of VV delay against beat-to-beat blood pressure using the Finometer device and defined the criteria that a technique requires if such a technique can be used meaningfully for the optimization of pacemaker settings both in clinical practice and in clinical trials. Finally, I investigated the impact of atrial pacing and heart rate on the optimal AV delay and attempted to characterize the mechanisms underlying any changes of the optimal AV delay under these varying patient and pacing states. In this thesis I found that optimization of AV delay of cardiac resynchronization therapy not only improved cardiac contraction and external cardiac work, but also cardiac relaxation and coronary blood flow, when compared against LBBB. I found that most of the increase in coronary blood flow occurred during diastole and that the predominant drive for this was ventricular microcirculatory suction as evidenced by the increased intracoronary diastolic backward-travelling decompression wave. I showed that non-invasive haemodynamic optimization using the plethysmograph signal of an inexpensive pulse oximeter is as reliable as using the Finometer. Appropriate processing of the oximetric signal improved the reproducibility of the optimal AV delay. The advantage of this technology is that it might be miniaturized and implanted to provide automated optimization. In this thesis I found that other commonly used modalities of VV optimization such as echocardiography and ECG lack internal validity as opposed to non-invasive haemodynamic optimization using blood pressure. This finding will encourage avoidance of internally invalid modalities, which may cause more harm than good. In this thesis I found that the sensed and paced optimal AV delays have, on average, a bigger difference than the one assumed by the device manufacturers and clinicians. As a significant proportion of patients will be atrially paced, especially during exercise, optimization during this mode of pacing is equally crucial as it is during atrial sensing. Finally, I found that the optimal AV delay decreases with increasing heart rate, and the slope of this is within the range of existing pacemaker algorithms used for rate adaptation of AV delay, strengthening the argument for the rate adaptation to be programmed on.Imperial Users Onl