Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

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

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

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice

Motion-resistant pulse oximetry

The measurement of vital signs ? such as peripheral capillary oxygen saturation (SpO2) and heart rate (HR) levels ? by a pulse oximeter is studied. The pulse oximeter is a non-invasive device that measures photoplethysmography (PPG) signals and extracts vital signs from them. However, the quality of the PPG signal measured by oximetry sensors is known to deteriorate in the presence of substantial human and sensor movements contributing to the measurement noise. Methods to suppress such noise from PPG signals measured by an oximeter and to calculate the associated vital signs with high accuracy even when the wearer is under substantial motion are presented in this study. The spectral components of the PPG waveform are known to appear at a fundamental frequency that corresponds to the participant\u27s HR and at its harmonics. To match this signal, a time-varying comb filter tuned to the participant\u27s HR is employed. The filter captures the HR components and eliminates most other artifacts. A significant improvement in the accuracy of SpO2 calculated from the comb-filtered PPG signals is observed, when tested on data collected from human participants while they are at rest and while they are exercising. In addition, an architecture that integrates SpO2 levels from multiple PPG channels mounted on different parts of the wearer\u27s arm is presented. The SpO2 levels are integrated using a Kalman filter that uses past measurements and modeling of the SpO2 dynamics to attenuate the effect of the motion artifacts. Again, data collected from human participants while they are at rest and while they are exercising are used. The integrated SpO2 levels are shown to be more accurate and reliable than those calculated from individual channels. Motion-resistant algorithms typically require an additional noise reference signal to produce high quality vital signs such as HR. A framework that employs PPG sensors only ? one in the green and one in the infrared spectrum ? to compute high quality HR levels is developed. Our framework is tested on experimental data collected from human participants while at rest and while running at various speeds. Our PPG-only framework generates HR levels with high accuracy and low computational complexity as compared to leading HR calculation methods in the literature that require the availability of a noise reference signal. The methods for SpO2 and HR calculation presented in this study are desirable since (1) they yield high accuracy in estimating vital signs under substantial level of motion artifacts and (2) they are computationally efficient, (and therefore are capable to be implemented in wearable devices)

Signal processing techniques for cardiovascular monitoring applications using conventional and video-based photoplethysmography

Photoplethysmography (PPG)-based monitoring devices will probably play a decisive role in healthcare environment of the future, which will be preventive, predictive, personalized and participatory. Indeed, this optical technology presents several practical advantages over gold standard methods based on electrocardiography, because PPG wearable devices can be comfortably used for long-term continuous monitoring during daily life activities. Contactless video-based PPG technique, also known as imaging photoplethysmography (iPPG), has also attracted much attention recently. In that case, the cardiac pulse is remotely measured from the subtle skin color changes resulting from the blood circulation, using a simple video camera. PPG/iPPG have a lot of potential for a wide range of cardiovascular applications. Hence, there is a substantial need for signal processing techniques to explore these applications and to improve the reliability of the PPG/iPPG-based parameters. \par A part of the thesis is dedicated to the development of robust processing schemes to estimate heart rate from the PPG/iPPG signals. The proposed approaches were built on adaptive frequency tracking algorithms that were previously developed in our group. These tools, based on adaptive band-pass filters, provide instantaneous frequency estimates of the input signal(s) with a very low time delay, making them suitable for real-time applications. In case of conventional PPG, a prior adaptive noise cancellation step involving the use of accelerometer signals was also necessary to reconstruct clean PPG signals during the regions corrupted by motion artifacts. Regarding iPPG, after comparing different regions of interest on the subject face, we hypothesized that the simultaneous use of different iPPG signal derivation methods (i.e. methods to derive the iPPG time series from the pixel values of the consecutive frames) could be advantageous. Methods to assess signal quality online and to incorporate it into instantaneous frequency estimation were also examined and successfully applied to improve system reliability. \par This thesis also explored different innovative applications involving PPG/iPPG signals. The detection of atrial fibrillation was studied. Novel features derived directly from the PPG waveforms, designed to reflect the morphological changes observed during arrhythmic episodes, were proposed and proven to be successful for atrial fibrillation detection. Arrhythmia detection and robust heart rate estimation approaches were combined in another study aimed at reducing the number of false arrhythmia alarms in the intensive care unit by exploiting signals from independent sources, including PPG. Evaluation on a hidden dataset demonstrated that the number of false alarms was drastically reduced while almost no true alarm was suppressed. Finally, other aspects of the iPPG technology were examined, such as the measurement of pulse rate variability indexes from the iPPG signals and the estimation of respiratory rate from the iPPG interbeat intervals

Plethysmography for retinal video processing

Zrak je jedním z pěti lidských smyslů a je velmi důležitým pro zkoumání blízkého i vzdáleného okolí. Tomu ovšem brání různé oční vady, které mohou souviset s cévním systémem sítnice a pulzacemi, které jsou na ní pozorovatelné. Tato práce se zabývá zkoumáním souvislostí mezi zmíněnými pulzacemi a onemocnění glaukomem. Pro hledání spojitostí mezi těmito dvěma aspekty jsou použité videozáznamy ze sítnice, s jejíž pulzací je dál pracováno ve formě 1D signálu, a to především ve spektrální oblasti.Sight is one of the five human senses and is very important for exploring near and far surroundings. However, this is prevented by various eye defects, which may be related to the vascular system of the retina and the pulsations that are observable on it. This work deals with the study of the relationship between the mentioned pulsations and glaucoma. To search for connections between these two aspects, video recordings from the retina are used, the pulsation of which is further worked in the form of a 1D signal, especially in the spectral region.

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Remote Assessment of the Cardiovascular Function Using Camera-Based Photoplethysmography

Camera-based photoplethysmography (cbPPG) is a novel measurement technique that allows the continuous monitoring of vital signs by using common video cameras. In the last decade, the technology has attracted a lot of attention as it is easy to set up, operates remotely, and offers new diagnostic opportunities. Despite the growing interest, cbPPG is not completely established yet and is still primarily the object of research. There are a variety of reasons for this lack of development including that reliable and autonomous hardware setups are missing, that robust processing algorithms are needed, that application fields are still limited, and that it is not completely understood which physiological factors impact the captured signal. In this thesis, these issues will be addressed. A new and innovative measuring system for cbPPG was developed. In the course of three large studies conducted in clinical and non-clinical environments, the system’s great flexibility, autonomy, user-friendliness, and integrability could be successfully proven. Furthermore, it was investigated what value optical polarization filtration adds to cbPPG. The results show that a perpendicular filter setting can significantly enhance the signal quality. In addition, the performed analyses were used to draw conclusions about the origin of cbPPG signals: Blood volume changes are most likely the defining element for the signal's modulation. Besides the hardware-related topics, the software topic was addressed. A new method for the selection of regions of interest (ROIs) in cbPPG videos was developed. Choosing valid ROIs is one of the most important steps in the processing chain of cbPPG software. The new method has the advantage of being fully automated, more independent, and universally applicable. Moreover, it suppresses ballistocardiographic artifacts by utilizing a level-set-based approach. The suitability of the ROI selection method was demonstrated on a large and challenging data set. In the last part of the work, a potentially new application field for cbPPG was explored. It was investigated how cbPPG can be used to assess autonomic reactions of the nervous system at the cutaneous vasculature. The results show that changes in the vasomotor tone, i.e. vasodilation and vasoconstriction, reflect in the pulsation strength of cbPPG signals. These characteristics also shed more light on the origin problem. Similar to the polarization analyses, they support the classic blood volume theory. In conclusion, this thesis tackles relevant issues regarding the application of cbPPG. The proposed solutions pave the way for cbPPG to become an established and widely accepted technology

The 2023 wearable photoplethysmography roadmap

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

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