Photoplethysmography for the Assessment of Haemorheology

COVID-19-Induced Changes in Photoplethysmography

INTRODUCTION

Photoplethysmography (PPG) is the science behind many commonly used medical devices such as the pulse oximeter. PPG changes, herein as "PPG dropouts," have been described in existing in vitro studies following artificially induced clot activation. Because COVID-19 causes increased arterial, venous, and microvascular clot formation, our hypothesis is that PPG dropouts identified in vitro can also be found in vivo in patients with COVID-19. The aim of this study is to evaluate PPG recordings and D-dimer levels for patients hospitalized with COVID-19 and compare them with the PPG tracings from non-COVID controls.

MATERIALS AND METHODS

PPG recordings were obtained for 197 ICU patients with COVID-19 and 300 non-COVID controls. PPG tracings were obtained using a TigerTech CovidPlus monitor, which received U.S. FDA emergency use authorization in March 2020 for monitoring the biometrics of patients with COVID-19 and featured unfiltered red and infrared spectrum PPG monitoring. D-dimer lab results were also recorded whenever available.

RESULTS

The results demonstrated significant differences in the prevalence rate of PPG dropout among patients with COVID-19 vs. non-COVID controls. The median PPG dropout rate was 0.58 for COVID-19 patients (median 0.58, IQR 0.42-0.72, P < .05) as opposed to a median 0.0 for non-COVID patients (median 0.0, IQR 0.0-0.0, P < .05). Furthermore, at least one incidence of PPG dropout was detected in 100% of COVID-19 patients, as opposed to 2.3% of non-COVID controls (P < .05). PPG dropout also correlated closely with the normalized serum D-dimer levels taken on the same day. The change in the normalized D-dimer levels was plotted against the change in PPG dropout, and a line of best fit was created. Linear regression resulted in R2 = 0.743 (P < .05), indicating that changes in the PPG dropout rate correlate with hemorheological changes in COVID-19 patients.

CONCLUSIONS

PPG dropout, like D-dimer, may not be specific for COVID-19. However, the inflammatory nature of the disease and the prevalence of prolonged ICU created a large sample size and allowed the authors to observe PPG changes in vivo in a statistically meaningful way. Further confirmatory studies are needed to confirm the potential application of PPG dropout as a measure of inflammation in other disease processes.

Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning

Hemorrhagic events are the main focus of attention during antithrombosis therapy in patients with coronary artery disease (CAD). This study aims to investigate the potential of using photoplethysmography (PPG) and machine learning techniques to assess hemorrhagic risk in patients with CAD. A total of 1638 patients with CAD were enrolled from January 2018 to October 2019, among which 114 patients were observed to have at least one positive event. Importantly, 102 patients with 9933 records were finally retained for analysis in this study. Participants were required to collect data using the portable PPG acquisition device and the specially designed Android APP. The data was collected and uploaded to a remote server. Based on collected PPG signals, we extracted features in a total of 30 dimensions from time-domain, frequency-domain, and wavelet packet decomposition. Logistic regression, support vector regression, random forest, and XGBoost regression models were established to achieve hemorrhagic risk evaluation, and then, their performances were compared. In total, 10 features extracted from PPG showed statistical significance (p < 0.01) between negative and positive groups. The newly established XGBoost model performed best in the hemorrhagic risk evaluation experiment, wherein the mean area under the curve (AUC) with tenfold cross-validation was 0.762 ± 0.024 and the sensitivity and specificity were 0.679 ± 0.051 and 0.714 ± 0.014, respectively. We established a data acquisition system for PPG signal collection, and demonstrated that a set of features extracted from PPG and the proposed machine learning model are promising in the evaluation of hemorrhagic risk among patients with CAD. In comparison with the traditional HAS-BLED score, the proposed method can obtain the quantitative risk prediction probability from a single PPG record, which has the advantages of dynamics and continuity, and can provide timely feedback for doctors' antithrombotic treatment, which is of great significance for doctors to quickly determine the effectiveness of the treatment and adjust the timely treatment plans accordingly.

Optical electrocardiogram monitor with a real-time analysis of an abnormal heart rhythm for home-based medical alerts

Sudden cardiac death (SCD) caused by cardiovascular disease is the greatest hidden danger to human life, accounting for about 25% of the total deaths in the world. Due to the early concealment of SCD and the heavy medical burden of long-term examination, telemedicine combined with home monitoring has become a potential medical alert method. Among all the existing human cardiac and electrophysiology monitoring methods, optics-based sensors attract the widest attention due to the advantages of low delay, real-time monitoring, and high signal-to-noise ratio. In this paper, we propose an optical sensor with the capabilities of long-term monitoring and real-time analysis. Combining an R-peak recognition algorithm, Lorenz plots (LP), and statistical analysis, we carried out the consistency analysis and result visualization of ECG sequences over 1 h. The results of 10 subjects show that the R-peak recognition accuracy of the optical ECG monitor is higher than 97.99%. The optical system can display abnormal heart rhythm in real-time through LP, and the readability is good, which makes the system suitable for self-monitoring at home. In addition, this paper provides a detailed long-term monitoring assessment method to effectively guide the practical clinical transformation of other optical wearable devices.

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual's quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

A Tactile-Pattern-Integrated Sensing Window for More Consistent Photoplethysmography (PPG) Measurements

We have demonstrated a tactile-pattern-integrated sensing window for more consistent photoplethysmogram (PPG) measurements. The pattern is composed of two tiny bumps that measure 500μm in diameter and 300μm in height and allow users to position their finger pulps more consistently on the sensing window over different measurement occasions, simply by following their tactile sensation. We experimentally compared the tactile pattern window to a flat window (without any bumps) for 5 test subjects and found that the sensing window with the tactile pattern significantly helped users obtain more consistent PPG signals than the flat window (p < 0.01).The use of PPG sensors in mobile phones and wearable watches have been limited to the measurements of heart rates and blood oxygen saturation in spite of widely-spread efforts to expand their applications. This is due to the fluctuations observed between measurements which largely originate from inconsistent placement of fingers on the sensing windows. The integrated tactile pattern could provide consistent and accurate measurements and lead to more successful commercialization of diverse PPG-based mobile healthcare services.

Investigating the origin of photoplethysmography using a multiwavelength Monte Carlo model

Photoplethysmography (PPG) is a photometric technique used for the measurement of volumetric changes in the blood. The recent interest in new applications of PPG has invigorated more fundamental research regarding the origin of the PPG waveform, which since its discovery in 1937, remains inconclusive. A handful of studies in the recent past have explored various hypotheses for the origin of PPG. These studies relate PPG to mechanical movement, red blood cell orientation or blood volume variations.

OBJECTIVE

Recognising the significance and need to corroborate a theory behind PPG formation, the present work rigorously investigates the origin of PPG based on a realistic model of light-tissue interactions.

APPROACH

A three-dimensional comprehensive Monte Carlo model of finger-PPG was developed and explored to quantify the optical entities pertinent to PPG (e.g. absorbance, reflectance, and penetration depth) as the functions of multiple wavelengths and source-detector separations. Complementary to the simulations, a pilot in vivo investigation was conducted on eight healthy volunteers. PPG signals were recorded using a custom-made multiwavelength sensor with an adjustable source-detector separation.

MAIN RESULTS

Simulated results illustrate the distribution of photon-tissue interactions in the reflectance PPG geometry. The depth-selective analysis quantifies the contributions of the dermal and subdermal tissue layers in the PPG wave formation. A strong negative correlation (r = -0.96) is found between the ratios of the simulated absorbances and measured PPG amplitudes.

SIGNIFICANCE

This work quantified for the first time the contributions of different tissue layers and sublayers in the formation of the PPG signal.

In silico and in vivo investigations using an endocavitary photoplethysmography sensor for tissue viability monitoring

SIGNIFICANCE

Colorectal cancer is one of the major causes of cancer-related deaths worldwide. Surgical removal of the cancerous growth is the primary treatment for this disease. A colorectal cancer surgery, however, is often unsuccessful due to the anastomotic failure that may occur following the surgical incision. Prevention of an anastomotic failure requires continuous monitoring of intestinal tissue viability during and after colorectal surgery. To date, no clinical technology exists for the dynamic and continuous monitoring of the intestinal perfusion.

AIM

A dual-wavelength indwelling bowel photoplethysmography (PPG) sensor for the continuous monitoring of intestinal viability was proposed and characterized through a set of in silico and in vivo investigations.

APPROACH

The in silico investigation was based on a Monte Carlo model that was executed to quantify the variables such as penetration depth and detected intensity with respect to the sensor-tissue separations and tissue perfusion. Utilizing the simulated information, an indwelling reflectance PPG sensor was designed and tested on 20 healthy volunteers. Two sets of in vivo studies were performed using the driving current intensities 20 and 40 mA for a comparative analysis, using buccal tissue as a proxy tissue-site.

RESULTS

Both simulated and experimental results showed the efficacy of the sensor to acquire good signals through the "contact" to a "noncontact" separation of 5 mm. A very slow wavelength-dependent variation was shown in the detected intensity at the normal and hypoxic states of the tissue, whereas a decay in the intensity was found with the increasing submucosal-blood volume. The simulated detected-to-incident-photon-ratio and the experimental signal-to-noise ratio exhibited strong positive correlations, with the Pearson product-moment correlation coefficient R ranging between 0.65 and 0.87.

CONCLUSIONS

The detailed feasibility analysis presented will lead to clinical trials utilizing the proposed sensor.

Monte Carlo Analysis of Optical Interactions in Reflectance and Transmittance Finger Photoplethysmography

Photoplethysmography (PPG) is a non-invasive photometric technique that measures the volume changes in arterial blood. Recent studies have reported limitations in developing and optimising PPG-based sensing technologies due to unavailability of the fundamental information such as PPG-pathlength and penetration depth in a certain region of interest (ROI) in the human body. In this paper, a robust computational model of a dual wavelength PPG system was developed using Monte Carlo technique. A three-dimensional heterogeneous volume of a specific ROI (i.e., human finger) was exposed at the red (660 nm) and infrared (940 nm) wavelengths in the reflectance and transmittance modalities of PPG. The optical interactions with the individual pulsatile and non-pulsatile tissue-components were demonstrated and the optical parameters (e.g., pathlength, penetration depth, absorbance, reflectance and transmittance) were investigated. Results optimised the source-detector separation for a reflectance finger-PPG sensor. The analysis with the recorded absorbance, reflectance and transmittance confirmed the maximum and minimum impact of the dermis and bone tissue-layers, respectively, in the formation of a PPG signal. The results presented in the paper provide the necessary information to develop PPG-based transcutaneous sensors and to understand the origin of the ac and dc components of the PPG signal.

Can Photoplethysmography Replace Arterial Blood Pressure in the Assessment of Blood Pressure?

Arterial Blood Pressure (ABP) and photoplethysmography (PPG) are both useful techniques to monitor cardiovascular status. Though ABP monitoring is more widely employed, this procedure of signal acquisition whether done invasively or non-invasively may cause inconvenience and discomfort to the patients. PPG, however, is simple, noninvasive, and can be used for continuous measurement. This paper focuses on analyzing the similarities in time and frequency domains between ABP and PPG signals for normotensive, prehypertensive and hypertensive subjects and the feasibility of the classification of subjects considering the results of the analysis performed. From a database with 120 records of ABP and PPG, each 120 s in length, the records where separated into epochs taking into account 10 heartbeats, and the following statistical measures were performed: Correlation (r), Coherence (COH), Partial Coherence (pCOH), Partial Directed Coherence (PDC), Directed Transfer Function (DTF), Full Frequency Directed Transfer Function (ffDTF) and Direct Directed Transfer Function (dDTF). The correlation coefficient was r > 0.9 on average for all groups, indicating a strong morphology similarity. For COH and pCOH, coherence (linear correlation in frequency domain) was found with significance (p < 0.01) in differentiating between normotensive and hypertensive subjects using PPG signals. For the dataset at hand, only two synchrony measures are able to convincingly distinguish hypertensive subjects from normotensive control subjects, i.e., ffDTF and dDTF. From PDC, DTF, ffDTF, and dDTF, a consistent, a strong significant causality from ABP→PPG was found. When all synchrony measures were combined, an 87.5 % accuracy was achieved to detect hypertension using a Neural Network classifier, suggesting that PPG holds most informative features that exist in ABP.

Investigating optical path and differential pathlength factor in reflectance photoplethysmography for the assessment of perfusion

Photoplethysmography (PPG) is an optical noninvasive technique with the potential for assessing tissue perfusion. The relative time-change in the concentration of oxyhemoglobin and deoxyhemoglobin in the blood can be derived from DC part of the PPG signal. However, the absolute concentration cannot be determined due to the inadequate data on PPG optical paths. The optical path and differential pathlength factor (DPF) for PPG at red (660 nm) and infrared (880 nm) wavelengths were investigated using a heterogeneous Monte Carlo model of the human forearm. Using the simulated DPFs, the absolute time-change in concentrations were determined from PPG signals recorded from the same tissue site. Results were compared with three conditions of approximated DPFs. Results showed the variation of the optical-path and DPF with different wavelengths and source-detector separations. Approximations resulted in significant errors, for example, using NIRS DPF in PPG led to "cross talk" of -0.4297 and 0.060 and an error of 15.16% to 25.18%. Results confirmed the feasibility of using the PPG (DC) for the assessment of tissue perfusion. The study also identified the inappropriateness of the assumption that DPF is independent of wavelength or source-detector separations and set the platform for further studies on investigating optical pathlengths and DPF in PPG.

An optimal filter for short photoplethysmogram signals

A photoplethysmogram (PPG) contains a wealth of cardiovascular system information, and with the development of wearable technology, it has become the basic technique for evaluating cardiovascular health and detecting diseases. However, due to the varying environments in which wearable devices are used and, consequently, their varying susceptibility to noise interference, effective processing of PPG signals is challenging. Thus, the aim of this study was to determine the optimal filter and filter order to be used for PPG signal processing to make the systolic and diastolic waves more salient in the filtered PPG signal using the skewness quality index. Nine types of filters with 10 different orders were used to filter 219 (2.1s) short PPG signals. The signals were divided into three categories by PPG experts according to their noise levels: excellent, acceptable, or unfit. Results show that the Chebyshev II filter can improve the PPG signal quality more effectively than other types of filters and that the optimal order for the Chebyshev II filter is the 4^th order.

A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China

Open clinical trial data provide a valuable opportunity for researchers worldwide to assess new hypotheses, validate published results, and collaborate for scientific advances in medical research. Here, we present a health dataset for the non-invasive detection of cardiovascular disease (CVD), containing 657 data segments from 219 subjects. The dataset covers an age range of 20-89 years and records of diseases including hypertension and diabetes. Data acquisition was carried out under the control of standard experimental conditions and specifications. This dataset can be used to carry out the study of photoplethysmograph (PPG) signal quality evaluation and to explore the intrinsic relationship between the PPG waveform and cardiovascular disease to discover and evaluate latent characteristic information contained in PPG signals. These data can also be used to study early and noninvasive screening of common CVD such as hypertension and other related CVD diseases such as diabetes.

In vitro validation of measurement of volume elastic modulus using photoplethysmography

Arterial stiffness (AS) is one of the earliest detectable symptoms of cardiovascular diseases and their progression. Current AS measurement methods provide an indirect and qualitative estimation of AS. The purpose of this study is to explore the utilisation of Photoplethysmography (PPG) as a measure of volumetric strain in providing a direct quantification of the Volume Elastic modulus (E_v). An in vitro experimental setup was designed using an arterial model to simulate the human circulation in health (Model 2) and disease (Model 1). Flow, pressure, and PPG signals were recorded continuously under varied conditions of flow dynamics. The obtained E_v values were validated with the gold standard mechanical testing techniques. Values obtained from both methods had no significant difference for both models with a percent error of 0.26% and 1.9% for Model 1 and Model 2, respectively. This study shows that PPG and pressure signals can provide a direct measure of AS in an in vitro setup. With emerging noninvasive pressure measurement methods, this research paves the way for the direct quantification of AS in vivo.