Spatio-temporal analysis of blood perfusion by imaging photoplethysmography

Imaging photoplethysmography (iPPG) has attracted much attention over the last years. The vast majority of works focuses on methods to reliably extract the heart rate from videos. Only a few works addressed iPPGs ability to exploit spatio-temporal perfusion pattern to derive further diagnostic statements. This work directs at the spatio-temporal analysis of blood perfusion from videos. We present a novel algorithm that bases on the two-dimensional representation of the blood pulsation (perfusion map). The basic idea behind the proposed algorithm consists of a pairwise estimation of time delays between photoplethysmographic signals of spatially separated regions. The probabilistic approach yields a parameter denoted as perfusion speed. We compare the perfusion speed versus two parameters, which assess the strength of blood pulsation (perfusion strength and signal to noise ratio). Preliminary results using video data with different physiological stimuli (cold pressure test, cold face test) show that all measures are in fluenced by those stimuli (some of them with statistical certainty). The perfusion speed turned out to be more sensitive than the other measures in some cases. However, our results also show that the intraindividual stability and interindividual comparability of all used measures remain critical points. This work proves the general feasibility of employing the perfusion speed as novel iPPG quantity. Future studies will address open points like the handling of ballistocardiographic effects and will try to deepen the understanding of the predominant physiological mechanisms and their relation to the algorithmic performance

Motion limitations of non-contact photoplethysmography due to the optical and topological properties of skin

Non-contact photoplethysmography (PPG) provides multiple benefits over in-contact methods, but is not as tolerant to motion due to the lack of mechanical coupling between the subject and sensor. One limitation of non-contact photoplethysmography is discussed here, specifically looking at the topology and optical variations of the skin and how this impacts upon the ability to extract a photoplethysmogram when a subject moves horizontally across the field of view of the detector (a panning motion). When this occurs it is shown that whilst the general relationships between the speed of traversal, detection area and resultant signal quality can be found, the quality of signal in each individual case is determined by the properties of the area of skin chosen

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Performance Comparison for Ballistocardiogram Peak Detection Methods

Citation: Suliman, A., Carlson, C., Ade, C. J., Warren, S., & Thompson, D. E. (2019). Performance Comparison for Ballistocardiogram Peak Detection Methods. IEEE Access, 7, 53945–53955. https://doi.org/10.1109/ACCESS.2019.2912650A number of research groups have proposed methods for ballistocardiogram (BCG) peak detection toward the identification of individual cardiac cycles. However, objective comparisons of these proposed methods are lacking. This paper, therefore, conducts a systematic and objective performance evaluation and comparison of several of these approaches. Five peak-detection methods (three replicated from the literature and two adapted from code provided by the methods' authors) are compared using data from 30 volunteers. A basic cross-correlation approach was also included as a sixth method. Two high-performing methods were identified: the method proposed by Sadek et al. and the method proposed by Brüser et al. The first achieved the highest average peak-detection rate of 94%, the lowest average false alarm rate of 0.0552 false alarms per second, and a relatively small mean absolute error between the real and detected peaks: 0.0175 seconds. The second method achieved the lowest mean absolute error of 0.0088 seconds between the real and detected peaks, an average peak-detection success rate of 89%, and 0.0766 false alarms per second. All metrics are averaged across participants

Estimating Carotid Pulse and Breathing Rate from Near-infrared Video of the Neck

Objective: Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtrusively with remote devices. However, most of the approaches work only in bright environments in which subtle photoplethysmographic and ballistocardiographic signals can be easily analyzed and/or require expensive and custom hardware to perform the measurements. Approach: This work introduces a low-cost method to measure subtle motions associated with the carotid pulse and breathing movement from the neck using near-infrared (NIR) video imaging. A skin reflection model of the neck was established to provide a theoretical foundation for the method. In particular, the method relies on template matching for neck detection, Principal Component Analysis for feature extraction, and Hidden Markov Models for data smoothing. Main Results: We compared the estimated HR and BR measures with ones provided by an FDA-cleared device in a 12-participant laboratory study: the estimates achieved a mean absolute error of 0.36 beats per minute and 0.24 breaths per minute under both bright and dark lighting. Significance: This work advances the possibilities of non-contact physiological measurement in real-life conditions in which environmental illumination is limited and in which the face of the person is not readily available or needs to be protected. Due to the increasing availability of NIR imaging devices, the described methods are readily scalable.Comment: 21 pages, 15 figure

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

Robust Visual Heart Rate Estimation

Je představena nová metoda odhadu srdeční frekvence, HR-CNN - dvoustupňová konvoluční neuronová síť. Síť je trénována end-to-end alternující optimalizací a je robustní vůči změnám osvětlení a relativnímu pohybu snímaného objektu a kamery. Síť funguje dobře s nepřesně registrovaným obličejem z komerčního obličejového detektoru. Z rozsáhlého rozboru relevantních zdrojů vyplývají klíčové faktory omezující přesnost a reprodukovatelnost metod jako: (i) nedostatek veřejně dostupných datových sad a nedostatečně popsané experimenty v publikovaných článcích, (ii) použití nespolehlivého pulzního oximetru pro referenční ground-truth, (iii) chybějící standardní experimentální protokoly. Je představena nová veřejně dostupná datová sada ECG-Fitness, která obsahuje 205 minutových videí, v nichž 17 dobrovolníků cvičí na posilovacích strojích. Dobrovolníci provádí celkem 4 aktivity (rozhovor, veslování, cvičení na stepperu a na rotopedu). Každá aktivita je zachycena dvěma RGB kamerami, z nichž jedna je připevněna k právě používanému posilovacímu stroji, který výrazně vibruje, a druhá je uchycena na samostatně stojícím stativu. Aktivity "veslování" a "rozhovor" opakují dobrovolníci dvakrát. Při druhém opakování jsou osvětleni halogenovou lampou. 4 dobrovolníci jsou osvětleni LED světlem ve všech šesti videích. HR-CNN má o více jak polovinu lepší výsledky než dosud publikované metody. Každá aktivita v ECG-Fitness datasetu představuje jinou kombinaci realistických výzev. HR-CNN má nejlepší výsledky v případě aktivity "veslování" s průměrnou absolutní chybou 3.94 a nejhorší v případě aktivity "rozhovor" s průměrnou absolutní chybou 15.57.A novel heart rate estimator, HR-CNN - a two-step convolutional neural network, is presented. The network is trained end-to-end by alternating optimization to be robust to illumination changes and relative movement of the subject and the camera. The network works well with images of the face roughly aligned by an of-the-shelf commercial frontal face detector. An extensive review of the literature on visual heart rate estimation identifies key factors limiting the performance and reproducibility of the methods as: (i) a lack of publicly available datasets and incomplete description of published experiments, (ii) use of unreliable pulse oximeters for the ground-truth reference, (iii) missing standard experimental protocols. A new challenging publicly available ECG-Fitness dataset with 205 sixty-second videos of subjects performing physical exercises is introduced. The dataset includes 17 subjects performing 4 activities (talking, rowing, exercising on a stepper and a stationary bike) captured by two RGB cameras, one attached to the currently used fitness machine that significantly vibrates, the other one to a separately standing tripod. With each subject, "rowing" and "talking" activity is repeated with a halogen lamp lighting. In case of 4 subjects, the whole recording session is also lighted by an LED light. HR-CNN outperforms the published methods on the dataset reducing error by more than a half. Each ECG-Fitness activity contains a different combination of realistic challenges. The HR-CNN method performs the best in case of the "rowing" activity with the mean absolute error 3.94, and the worst in case of the "talking" activity with the mean absolute error 15.57