Current progress of photoplethysmography and SPO2 for health monitoring

Validation of Polar Elixir™ Pulse Oximeter against Arterial Blood Gases during Stepwise Steady-State Inspired Hypoxia

PURPOSE

The purpose of this study was to evaluate the accuracy of peripheral oxygen saturation (SpO 2 ) measurements from Polar Elixir™ pulse oximetry technology compared with arterial oxygen saturation (SaO 2 ) measurements during acute stepwise steady-state inspired hypoxia at rest. A post hoc objective was to determine if SpO 2 measurements could be improved by recalibrating the Polar Elixir™ algorithm with SaO 2 values from a random subset of participants.

METHODS

The International Organization for Standardization (ISO) protocol (ISO 80601-2-61:2017) for evaluating the SpO 2 accuracy of pulse oximeter equipment was followed whereby five plateaus of SaO 2 between 70% and 100% were achieved using stepwise reductions in inspired O 2 during supine rest. Blood samples drawn through a radial arterial catheter from 25 participants were first used to compare SaO 2 with SpO 2 measurements from Polar Elixir™. Then the Polar Elixir™ algorithm was recalibrated using SaO 2 data from 13 random participants, and SpO 2 estimates were recalculated for the other 12 participants. For SaO 2 values between 70% and 100%, root mean square error, intraclass correlation coefficients (ICC), Pearson correlations, and Bland-Altman plots were used to assess the accuracy, agreement, and strength of relationship between SaO 2 values and SpO 2 values from Polar Elixir™.

RESULTS

The initial root mean square error for Polar Elixir™ was 4.13%. After recalibrating the algorithm, the RMSE was improved to 2.67%. The ICC revealed excellent levels of agreement between SaO 2 and Polar Elixir™ SpO 2 values both before (ICC(1,3) = 0.837, df = 574, P < 0.001) and after (ICC(1,3) = 0.942, df = 287, P < 0.001) recalibration.

CONCLUSIONS

Relative to ISO standards, Polar Elixir™ yielded accurate SpO 2 measurements during stepwise inspired hypoxia at rest when compared with SaO 2 values, which were improved by recalibrating the algorithm using a subset of the SaO 2 data.

Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement

In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.

Mobile Diagnostic Clinics

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

Ninety years of pulse oximetry: history, current status, and outlook

Significance

This year, 2024, marks the 50th anniversary of the invention of pulse oximetry (PO), which was first presented by Takuo Aoyagi, an engineer from the Nihon Kohden Company, at the 13th Conference of the Japanese Society of Medical Electronics and Biological Engineering in Osaka in 1974. His discovery and the development of PO for the non-invasive measurement of peripheral arterial oxygenation represents one of the most significant chapters in the history of medical technology. It resulted from research and development efforts conducted by biochemists, engineers, physicists, physiologists, and physicians since the 1930s.

Aim

The objective of this work was to provide a narrative review of the history, current status, and future prospects of PO.

Approach

A comprehensive review of the literature on oximetry and PO was conducted.

Results and Conclusions

Our historical review examines the development of oximetry in general and PO in particular, tracing the key stages of a long and fascinating story that has unfolded from the first half of the twentieth century to the present day-an exciting journey in which serendipity has intersected with the hard work of key pioneers. This work has been made possible by the contributions of numerous key pioneers, including Kurt Kramer, Karl Matthes, Glenn Millikan, Evgenii M. Kreps, Earl H. Wood, Robert F. Show, Scott A. Wilber, William New, and, above all, Takuo Aoyagi. PO has become an integral part of modern medical care and has proven to be an important tool for physiological monitoring. The COVID-19 pandemic not only highlighted the clinical utility of PO but also revealed some of the problems with the technology. Current research in biomedical optics should address these issues to make the technology even more reliable and accurate. We discuss the necessary innovations in PO and present our thoughts on what the next generation of PO might look like.

Finger photopletysmography detects early acute blood loss in compensated blood donors: a pilot study

Objective.Diagnosis of incipient acute hypovolemia is challenging as vital signs are typically normal and patients remain asymptomatic at early stages. The early identification of this entity would affect patients' outcome if physicians were able to treat it precociously. Thus, the development of a noninvasive, continuous bedside monitoring tool to detect occult hypovolemia before patients become hemodynamically unstable is clinically relevant. We hypothesize that pulse oximeter's alternant (AC) and continuous (DC) components of the infrared light are sensitive to acute and small changes in patient's volemia. We aimed to test this hypothesis in a cohort of healthy blood donors as a model of slight hypovolemia.Approach.We planned to prospectively study blood donor volunteers removing 450 ml of blood in supine position. Noninvasive arterial blood pressure, heart rate, and finger pulse oximetry were recorded. Data was analyzed before donation, after donation and during blood auto-transfusion generated by the passive leg-rising (PLR) maneuver.Main results.Sixty-six volunteers (44% women) accomplished the protocol successfully. No clinical symptoms of hypovolemia, arterial hypotension (systolic pressure < 90 mmHg), brady-tachycardia (heart rate <60 and >100 beats-per-minute) or hypoxemia (SpO2< 90%) were observed during donation. The AC signal before donation (median 0.21 and interquartile range 0.17 a.u.) increased after donation [0.26(0.19) a.u;p< 0.001]. The DC signal before donation [94.05(3.63) a.u] increased after blood extraction [94.65(3.49) a.u;p< 0.001]. When the legs' blood was auto-transfused during the PLR, the AC [0.21(0.13) a.u.;p= 0.54] and the DC [94.25(3.94) a.u.;p= 0.19] returned to pre-donation levels.Significance.The AC and DC components of finger pulse oximetry changed during blood donation in asymptomatic volunteers. The continuous monitoring of these signals could be helpful in detecting occult acute hypovolemia. New pulse oximeters should be developed combining the AC/DC signals with a functional hemodynamic monitoring of fluid responsiveness to define which patient needs fluid administration.

A Robust End-to-End IoT System for Supporting Workers in Mining Industries

The adoption of the Internet of Things (IoT) in the mining industry can dramatically enhance the safety of workers while simultaneously decreasing monitoring costs. By implementing an IoT solution consisting of a number of interconnected smart devices and sensors, mining industries can improve response times during emergencies and also reduce the number of accidents, resulting in an overall improvement of the social image of mines. Thus, in this paper, a robust end-to-end IoT system for supporting workers in harsh environments such as in mining industries is presented. The full IoT solution includes both edge devices worn by the workers in the field and a remote cloud IoT platform, which is responsible for storing and efficiently sharing the gathered data in accordance with regulations, ethics, and GDPR rules. Extended experiments conducted to validate the IoT components both in the laboratory and in the field proved the effectiveness of the proposed solution in monitoring the real-time status of workers in mines.

Hospital Environmental Factors That Influence Peripheral Oxygen Saturation Measurements: A Cross-Sectional Study

Pulse oximetry is a non-invasive, cost-effective, and generally reliable instrument measuring pulse rate and peripheral oxygen saturation (SpO2). However, these measurements can be affected by the patient's internal or external factors, including the type of pulse oximeter device (POD). (1) This study's objective was to identify potential environmental factors that may impact the measurements taken by three PODs. (2) Methods: A descriptive-analytical cross-sectional study was designed. The patients' SpO2 levels were measured using a standard monitor and two PODs owned by the professionals. The measurements were taken on the patients' fingers. Concurrently, we evaluated the surrounding environmental conditions, encompassing temperature, humidity, illuminance, and noise. (3) Results: This study involved 288 adult participants in the sample. For each 20-decibel increment in noise, there was a reduction in SpO2 by an average of 1%, whereas for every additional degree of ambient temperature, SpO2 decreased by an average of 2% (4) Conclusions: Significant correlations between SpO2 and age, as well as with noise and ambient temperature, were observed. No significant differences between oxygen saturation and lighting or humidity were observed. This study was prospectively registered with the Clinical Research Ethics Committee of Gran Canaria at the Dr. Negrín University Hospital, with protocol code 2019-247-1, and approved on 24 May 2019.

Evaluating Vascular Depth-Dependent Changes in Multi-Wavelength PPG Signals Due to Contact Force

Photoplethysmography (PPG) is a non-invasive method used for cardiovascular monitoring, with multi-wavelength PPG (MW-PPG) enhancing its efficacy by using multiple wavelengths for improved assessment. This study explores how contact force (CF) variations impact MW-PPG signals. Data from 11 healthy subjects are analyzed to investigate the still understudied specific effects of CF on PPG signals. The obtained dataset includes simultaneous recording of five PPG wavelengths (470, 525, 590, 631, and 940 nm), CF, skin temperature, and the tonometric measurement derived from CF. The evolution of raw signals and the PPG DC and AC components are analyzed in relation to the increasing and decreasing faces of the CF. Findings reveal individual variability in signal responses related to skin and vasculature properties and demonstrate hysteresis and wavelength-dependent responses to CF changes. Notably, all wavelengths except 631 nm showed that the DC component of PPG signals correlates with CF trends, suggesting the potential use of this component as an indirect CF indicator. However, further validation is needed for practical application. The study underscores the importance of biomechanical properties at the measurement site and inter-individual variability and proposes the arterial pressure wave as a key factor in PPG signal formation.

Introducing the Pi-CON Methodology to Overcome Usability Deficits during Remote Patient Monitoring

The adoption of telehealth has soared, and with that the acceptance of Remote Patient Monitoring (RPM) and virtual care. A review of the literature illustrates, however, that poor device usability can impact the generated data when using Patient-Generated Health Data (PGHD) devices, such as wearables or home use medical devices, when used outside a health facility. The Pi-CON methodology is introduced to overcome these challenges and guide the definition of user-friendly and intuitive devices in the future. Pi-CON stands for passive, continuous, and non-contact, and describes the ability to acquire health data, such as vital signs, continuously and passively with limited user interaction and without attaching any sensors to the patient. The paper highlights the advantages of Pi-CON by leveraging various sensors and techniques, such as radar, remote photoplethysmography, and infrared. It illustrates potential concerns and discusses future applications Pi-CON could be used for, including gait and fall monitoring by installing an omnipresent sensor based on the Pi-CON methodology. This would allow automatic data collection once a person is recognized, and could be extended with an integrated gateway so multiple cameras could be installed to enable data feeds to a cloud-based interface, allowing clinicians and family members to monitor patient health status remotely at any time.

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10-3 comparing the DigiPen's computed Δt (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals.

A Self-Calibrated Single Wavelength Biosensor for Measuring Oxygen Saturation

Traditional methods for measuring blood oxygen use multiple wavelengths, which produce an intrinsic error due to ratiometric measurements. These methods assume that the absorption changes with the wavelength, but in fact the scattering changes as well and cannot be neglected. We found that if one measures in a specific angle around a cylindrical tissue, called the iso-pathlength (IPL) point, the reemitted light intensity is unaffected by the tissue's scattering. Therefore, the absorption can be isolated from the scattering, which allows the extraction of the subject's oxygen saturation. In this work, we designed an optical biosensor for reading the light intensity reemitted from the tissue, using a single light source and multiple photodetectors (PDs), with one of them in the IPL point's location. Using this bio-device, we developed a methodology to extract the arterial oxygen saturation using a single wavelength light source. We proved this method is not dependent on the light source and is applicable to different measurement locations on the body, with an error of 0.5%. Moreover, we tested thirty-eight males and females with the biosensor under normal conditions. Finally, we show the results of measuring subjects in a hypoxic chamber that simulates extreme conditions with low oxygen.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

CL-SPO2Net: Contrastive Learning Spatiotemporal Attention Network for Non-Contact Video-Based SpO2 Estimation

Video-based peripheral oxygen saturation (SpO2) estimation, utilizing solely RGB cameras, offers a non-contact approach to measuring blood oxygen levels. Previous studies set a stable and unchanging environment as the premise for non-contact blood oxygen estimation. Additionally, they utilized a small amount of labeled data for system training and learning. However, it is challenging to train optimal model parameters with a small dataset. The accuracy of blood oxygen detection is easily affected by ambient light and subject movement. To address these issues, this paper proposes a contrastive learning spatiotemporal attention network (CL-SPO2Net), an innovative semi-supervised network for video-based SpO2 estimation. Spatiotemporal similarities in remote photoplethysmography (rPPG) signals were found in video segments containing facial or hand regions. Subsequently, integrating deep neural networks with machine learning expertise enabled the estimation of SpO2. The method had good feasibility in the case of small-scale labeled datasets, with the mean absolute error between the camera and the reference pulse oximeter of 0.85% in the stable environment, 1.13% with lighting fluctuations, and 1.20% in the facial rotation situation.

Arterial stiffness assessment using PPG feature extraction and significance testing in an in vitro cardiovascular system

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, therefore understanding arterial stiffness is essential to developing innovative technologies to detect, monitor and treat them. The ubiquitous spread of photoplethysmography (PPG), a completely non-invasive blood-volume sensing technology suitable for all ages, highlights immense potential for arterial stiffness assessment in the wider healthcare setting outside specialist clinics, for example during routine visits to a General Practitioner or even at home with the use of mobile and wearable health devices. This study employs a custom-manufactured in vitro cardiovascular system with vessels of varying stiffness to test the hypothesis that PPG signals may be used to detect and assess the level of arterial stiffness under controlled conditions. Analysis of various morphological features demonstrated significant (p < 0.05) correlations with vessel stiffness. Particularly, area related features were closely linked to stiffness in red PPG signals, while for infrared PPG signals the most correlated features were related to pulse-width. This study demonstrates the utility of custom vessels and in vitro investigations to work towards non-invasive cardiovascular assessment using PPG, a valuable tool with applications in clinical healthcare, wearable health devices and beyond.

The Development of a Measuring System for Intraoral SpO2

Blood oxygen saturation (SpO2) is an essential indicator of a patient's general condition. However, conventional measurement methods have some issues such as time delay and interference by ambient light. Improved measurement methods must be developed, and there are no reports on intraoral measurements of SpO2 using wearable devices. Therefore, we aimed to establish an intraoral SpO2 measurement method for the first time. Twelve healthy adults participated in this study. The following steps were taken: (1) to identify the optimal measurement location, mid-perfusion index (PI) values were measured at six places on the mucosa of the maxilla, (2) to validate the optimal measurement pressure, PI values were obtained at different pressures, and (3) using the proposed mouthpiece device, SpO2 values in the oral cavity and on the finger were analyzed during breath-holding. The highest PI values were observed in the palatal gingiva of the maxillary canine teeth, with high PI values at pressures ranging from 0.3 to 0.8 N. In addition, changes in SpO2 were detected approximately 7 s faster in the oral cavity than those on the finger, which is attributed to their proximity to the heart. This study demonstrates the advantage of the oral cavity for acquiring biological information using a novel device.

Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography

Photoplethysmography (PPG) is used for heart-rate monitoring in a variety of contexts and applications due to its versatility and simplicity. These applications, namely studies involving PPG data acquisition during day-to-day activities, require reliable and continuous measurements, which are often performed at the index finger or wrist. However, some PPG sensors are susceptible to saturation, motion artifacts, and discomfort upon their use. In this paper, an off-the-shelf PPG sensor was benchmarked and modified to improve signal saturation. Moreover, this paper explores the feasibility of using an optimized sensor in the lower limb as an alternative measurement site. Data were collected from 28 subjects with ages ranging from 18 to 59 years. To validate the sensors' performance, signal saturation and quality, wave morphology, performance of automatic systolic peak detection, and heart-rate estimation, were compared. For the upper and lower limb locations, the index finger and the first toe were used as reference locations, respectively. Lowering the amplification stage of the PPG sensor resulted in a significant reduction in signal saturation, from 18% to 0.5%. Systolic peak detection at rest using an automatic algorithm showed a sensitivity and precision of 0.99 each. The posterior wrist and upper arm showed pulse wave morphology correlations of 0.93 and 0.92, respectively. For these locations, peak detection sensitivity and precision were 0.95, 0.94 and 0.89, 0.89, respectively. Overall, the adjusted PPG sensors are a good alternative for obtaining high-quality signals at the fingertips, and for new measurement sites, the posterior pulse and the upper arm allow for high-quality signal extraction.

Diffuse transmittance visible spectroscopy using smartphone flashlight for photoplethysmography and vital signs measurements

Photoplethysmography (PPG), with its wide range of applications, has become one of the most promising modalities for healthcare monitoring technology. In this work, we present a new PPG measurement technique based on diffuse transmittance spectroscopy (DTS) with the help of a smartphone built-in flashlight as an alternative broadband light source. The blood Volume Pulse (BVP) signal was extracted from recorded transmittance spectra at 620 nm. The results were compared with the ground truth and conventional contact finger PPG sensors. A very high correlation was found between the diffuse transmittance signal and the reference PPG signals (r = 0.997, p < 0.0001). The accuracy and root mean square error (RMSE) were 99.23% and 0.8 bpm, respectively. In addition, a Bland-Altman analysis showed a good agreement between both techniques, with a very small bias between mean paired differences of heart rate observations. A simple forward model for diffuse transmittance spectra for different levels of blood oxygen saturation is developed and supported by experimental measurements. It was also found that blood oxygen saturation (SpO2) can be estimated with the aid of DTS based smartphone flash by tracking the wavelength corresponding to the oxygenation level in the visible range between orange and red regions of the visible spectrum particularly in the range between 610 and 635 nm for 26 healthy subjects. 624 nm on average seems to be the wavelength that corresponds with the normal blood oxygenation level. These findings show the potential of DTS PPG to reliably extract cardiac frequency and estimate SpO2 with adequate accuracy. The results also demonstrate the capability of smartphone flash as a miniature visible light source for recording multispectral PPG signals and quantifying vital signs in the transmission mode at the fingertip with acceptable signal quality over a wide range of wavelengths from 550 nm to 650 nm.

Prospective clinical validation of the Empatica EmbracePlus wristband as a reflective pulse oximeter

Introduction

Respiratory diseases such as chronic obstructive pulmonary disease, obstructive sleep apnea syndrome, and COVID-19 may cause a decrease in arterial oxygen saturation (SaO2). The continuous monitoring of oxygen levels may be beneficial for the early detection of hypoxemia and timely intervention. Wearable non-invasive pulse oximetry devices measuring peripheral oxygen saturation (SpO2) have been garnering increasing popularity. However, there is still a strong need for extended and robust clinical validation of such devices, especially to address topical concerns about disparities in performances across racial groups. This prospective clinical validation aimed to assess the accuracy of the reflective pulse oximeter function of the EmbracePlus wristband during a controlled hypoxia study in accordance with the ISO 80601-2-61:2017 standard and the Food & Drug Administration (FDA) guidance.

Methods

Healthy adult participants were recruited in a controlled desaturation protocol to reproduce mild, moderate, and severe hypoxic conditions with SaO2 ranging from 100% to 70% (ClinicalTrials.gov registration #NCT04964609). The SpO2 level was estimated with an EmbracePlus device placed on the participant's wrist and the reference SaO2 was obtained from blood samples analyzed with a multiwavelength co-oximeter.

Results

The controlled hypoxia study yielded 373 conclusive measurements on 15 subjects, including 30% of participants with dark skin pigmentation (V-VI on the Fitzpatrick scale). The accuracy root mean square (Arms) error was found to be 2.4%, within the 3.5% limit recommended by the FDA. A strong positive correlation between the wristband SpO2 and the reference SaO2 was observed (r = 0.96, P < 0.001), and a good concordance was found with Bland-Altman analysis (bias, 0.05%; standard deviation, 1.66; lower limit, -4.7%; and upper limit, 4.8%). Moreover, acceptable accuracy was observed when stratifying data points by skin pigmentation (Arms 2.2% in Fitzpatrick V-VI, 2.5% in Fitzpatrick I-IV), and sex (Arms 1.9% in females, and 2.9% in males).

Discussion

This study demonstrates that the EmbracePlus wristband could be used to assess SpO2 with clinically acceptable accuracy under no-motion and high perfusion conditions for individuals of different ethnicities across the claimed range. This study paves the way for further accuracy evaluations on unhealthy subjects and during prolonged use in ambulatory settings.

Applications of flexible electronics related to cardiocerebral vascular system

Ensuring accessible and high-quality healthcare worldwide requires field-deployable and affordable clinical diagnostic tools with high performance. In recent years, flexible electronics with wearable and implantable capabilities have garnered significant attention from researchers, which functioned as vital clinical diagnostic-assisted tools by real-time signal transmission from interested targets in vivo. As the most crucial and complex system of human body, cardiocerebral vascular system together with heart-brain network attracts researchers inputting profuse and indefatigable efforts on proper flexible electronics design and materials selection, trying to overcome the impassable gulf between vivid organisms and rigid inorganic units. This article reviews recent breakthroughs in flexible electronics specifically applied to cardiocerebral vascular system and heart-brain network. Relevant sensor types and working principles, electronics materials selection and treatment methods are expounded. Applications of flexible electronics related to these interested organs and systems are specially highlighted. Through precedent great working studies, we conclude their merits and point out some limitations in this emerging field, thus will help to pave the way for revolutionary flexible electronics and diagnosis assisted tools development.

Comparative Analysis of Perfusion Index and End-Tidal Carbon Dioxide in Cardiac Arrest Patients: Implications for Hemodynamic Monitoring and Resuscitation Outcomes

BACKGROUND

During cardiopulmonary resuscitation (CPR), some parameters (e.g., intraarterial pressure measurement and end-tidal carbon dioxide (EtCO2)) indicate the quality and outcome of resuscitation. These parameters are generally based on monitoring the hemodynamic status. Perfusion index (PI) is a calculation from the photoplethysmography (PPG) signal and displays the proportion of pulsatile to non-pulsatile light absorption or reflection in the PPG signal. It helps to evaluate cardiac output and tissue perfusion in the care of a critical patient. Its most important advantages are that it can be easily measured with a pulse oximeter probe attached to the finger (non-invasive), can be objectively repeated, can be applied quickly, and is inexpensive. Normal PI values range from 0.2% to 20%. Despite being recognized as a valuable indicator of hemodynamics, there is limited information regarding its relevance in patients experiencing cardiac arrest. Although the PI is known to be a valuable parameter to indicate hemodynamics, information about its value in cardiac arrest patients is limited. This study aims to evaluate the performance of PI and EtCO2 in predicting the return of spontaneous circulation (ROSC) among cardiac arrest patients.

METHODS

This was a single-center, prospective, observational clinical study including both out-of-hospital and in-hospital adult cardiac arrest patients. The study was conducted from November 1, 2018 to April 30, 2019 at the Emergency Department (ED) of the Hacettepe University Hospital, Ankara, Turkey. The EtCO2 values of the patients were recorded at the time they were intubated (t0) and every five minutes (t5, t10, t15...) during CPR. Along with these measurements, PI values were measured with the Masimo Signal Extraction Technology device (Masimo, California, United States). The study's primary outcome was PI's performance in predicting the ROSC among cardiac arrest patients. The secondary outcomes of the study were the performance of EtCO2 in predicting the ROSC among cardiac arrest patients and the association between PI and EtCO2 values.

RESULTS

We included a total of 100 cases. The mean age of patients was 70.4 ± 13.4 years, and 65% were male. The ROSC was achieved in 29 patients. There was no statistical difference in PI values between the ROSC (+) and ROSC (-) groups at any minute. However, in the ROSC (+) group, EtCO2 values were observed to be high starting from the fifth minute (t5, p=0.010; t10, p<0.001; t15, p=0.014; t20, p=0.033; t25, p=0.003, respectively). There was no correlation between the PI and EtCO2 values at 0, 5, 10, 15, 20, and 25 minutes (t0, p=0.436; t5, p=0.154; t10, p=0.557; t15, p=0.740; t20 p=0.241; t25 p=0.201, respectively).

CONCLUSION

Measuring PI values during resuscitation in intubated cardiac arrest patients does not help clinicians predict the outcome. In addition, no correlation was found with EtCO2 values. However, EtCO2 values remained high in patients with the ROSC from the fifth minute onward. Further larger-scale studies are needed regarding the optimal use of PI in cardiac arrest patients.

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.

Evaluating blood oxygen saturation measurements by popular fitness trackers in postoperative patients: A prospective clinical trial

Blood oxygen saturation is an important clinical parameter, especially in postoperative hospitalized patients, monitored in clinical practice by arterial blood gas (ABG) and/or pulse oximetry that both are not suitable for a long-term continuous monitoring of patients during the entire hospital stay, or beyond. Technological advances developed recently for consumer-grade fitness trackers could-at least in theory-help to fill in this gap, but benchmarks on the applicability and accuracy of these technologies in hospitalized patients are currently lacking. We therefore conducted at the postanaesthesia care unit under controlled settings a prospective clinical trial with 201 patients, comparing in total >1,000 oxygen blood saturation measurements by fitness trackers of three brands with the ABG gold standard and with pulse oximetry. Our results suggest that, despite of an overall still tolerable measuring accuracy, comparatively high dropout rates severely limit the possibilities of employing fitness trackers, particularly during the immediate postoperative period of hospitalized patients.

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

In the post-covid19 era, every new wave of the pandemic causes an increased concern/interest among the masses to learn more about their state of well-being. Therefore, it is the need of the hour to come up with ubiquitous, low-cost, non-invasive tools for rapid and continuous monitoring of body vitals that reflect the status of one's overall health. In this backdrop, this work proposes a deep learning approach to turn a smartphone-the popular hand-held personal gadget-into a diagnostic tool to measure/monitor the three most important body vitals, i.e., pulse rate (PR), blood oxygen saturation level (aka SpO2), and respiratory rate (RR). Furthermore, we propose another method that could extract a single-lead electrocardiograph (ECG) of the subject. The proposed methods include the following core steps: subject records a small video of his/her fingertip by placing his/her finger on the rear camera of the smartphone, and the recorded video is pre-processed to extract the filtered and/or detrended video-photoplethysmography (vPPG) signal, which is then fed to custom-built convolutional neural networks (CNN), which eventually spit-out the vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be precise, the contribution of this paper is twofold: (1) estimation of the three body vitals (PR, SpO2, RR) from the vPPG data using custom-built CNNs, vision transformer, and most importantly by CLIP model (a popular image-caption-generator model); (2) a novel discrete cosine transform+feedforward neural network-based method that translates the recorded video-PPG signal to a single-lead ECG signal. The significance of this work is twofold: (i) it allows rapid self-testing of body vitals (e.g., self-monitoring for covid19 symptoms), (ii) it enables rapid self-acquisition of a single-lead ECG, and thus allows early detection of atrial fibrillation (abormal heart beat or arrhythmia), which in turn could enable early intervention in response to a range of cardiovascular diseases, and could help save many precious lives. Our work could help reduce the burden on healthcare facilities and could lead to reduction in health insurance costs.

Recent Progress in Flexible and Wearable All Organic Photoplethysmography Sensors for SpO2 Monitoring

Flexible and wearable biosensors are the next-generation healthcare devices that can efficiently monitor human health conditions in day-to-day life. Moreover, the rapid growth and technological advancements in wearable optoelectronics have promoted the development of flexible organic photoplethysmography (PPG) biosensor systems that can be implanted directly onto the human body without any additional interface for efficient bio-signal monitoring. As an example, the pulse oximeter utilizes PPG signals to monitor the oxygen saturation (SpO2 ) in the blood volume using two distinct wavelengths with organic light emitting diode (OLED) as light source and an organic photodiode (OPD) as light sensor. Utilizing the flexible and soft properties of organic semiconductors, pulse oximeter can be both flexible and conformal when fabricated on thin polymeric substrates. It can also provide highly efficient human-machine interface systems that can allow for long-time biological integration and flawless measurement of signal data. In this work, a clear and systematic overview of the latest progress and updates in flexible and wearable all-organic pulse oximetry sensors for SpO2 monitoring, including design and geometry, processing techniques and materials, encapsulation and various factors affecting the device performance, and limitations are provided. Finally, some of the research challenges and future opportunities in the field are mentioned.

Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction From Faces

Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurably. We identify and evaluate the possible choices in the critical steps of unsupervised rPPG methodologies. We assess a state-of-the-art processing pipeline in six different datasets, incorporating important corrections in the methodology that ensure reproducible and fair comparisons. In addition, we extend the pipeline by proposing three novel ideas; 1) a new method to stabilize the detected face based on a rigid mesh normalization; 2) a new method to dynamically select the different regions in the face that provide the best raw signals, and 3) a new RGB to rPPG transformation method, called Orthogonal Matrix Image Transformation (OMIT) based on QR decomposition, that increases robustness against compression artifacts. We show that all three changes introduce noticeable improvements in retrieving rPPG signals from faces, obtaining state-of-the-art results compared with unsupervised, non-learning-based methodologies and, in some databases, very close to supervised, learning-based methods. We perform a comparative study to quantify the contribution of each proposed idea. In addition, we depict a series of observations that could help in future implementations.

Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study

BACKGROUND

Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea.

OBJECTIVE

The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard.

METHODS

We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device's efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant's data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods.

RESULTS

The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89.

CONCLUSIONS

Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals.

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.

Implementing Wearable Sensors for Clinical Application at a Surgical Ward: Points to Consider before Starting

Incorporating technology into healthcare processes is necessary to ensure the availability of high-quality care in the future. Wearable sensors are an example of such technology that could decrease workload, enable early detection of patient deterioration, and support clinical decision making by healthcare professionals. These sensors unlock continuous monitoring of vital signs, such as heart rate, respiration rate, blood oxygen saturation, temperature, and physical activity. However, broad and successful application of wearable sensors on the surgical ward is currently lacking. This may be related to the complexity, especially when it comes to replacing manual measurements by healthcare professionals. This report provides practical guidance to support peers before starting with the clinical application of wearable sensors in the surgical ward. For this purpose, the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework of technology adoption and innovations in healthcare organizations is used, combining existing literature and our own experience in this field over the past years. Specifically, the relevant topics are discussed per domain, and key lessons are subsequently summarized.

Non-invasive arterial blood pressure measurement and SpO2 estimation using PPG signal: a deep learning framework

BACKGROUND

Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection.

METHOD

Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation.

RESULTS

The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard.

CONCLUSIONS

The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.

Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites

Continuous, real-time monitoring of occupational health and safety in high-risk workplaces such as construction sites can substantially improve the safety of workers. However, introducing such systems in practice is associated with a number of challenges, such as scaling up the solution while keeping its cost low. In this context, this work investigates the use of an off-the-shelf, low-cost smartwatch to detect health issues based on heart rate monitoring in a privacy-preserving manner. To improve the smartwatch's low measurement quality, a novel, frugal machine learning method is proposed that corrects measurement errors, along with a new dataset for this task. This method's integration with the smartwatch and the remaining parts of the health and safety monitoring system (built on the ASSIST-IoT reference architecture) are presented. This method was evaluated in a laboratory environment in terms of its accuracy, computational requirements, and frugality. With an experimentally established mean absolute error of 8.19 BPM, only 880 bytes of required memory, and a negligible impact on the performance of the device, this method meets all relevant requirements and is expected to be field-tested in the coming months. To support reproducibility and to encourage alternative approaches, the dataset, the trained model, and its implementation on the smartwatch were published under free licenses.

Impact of Different Skin Penetration Depths of Red and Green Wavelengths on Camera-based SpO2 Measurement

Remote camera-based estimation of blood oxygen saturation (SpO2) using visible lights has been studied recently, typically for red (660 nm) and green (550 nm) wavelengths. This paper investigates the impact of different skin penetration depths of red and green wavelengths on the SpO2 estimation based on mathematical modeling and experiments, where the SpO2-calibritability between two illumination setups, narrow-band red/green and narrow-band red/infrared, are statistically compared using the "ratio-of-ratios" method. The results show that the performance of the setup using red/green is less consistent among 17 volunteers than the setup using red/infrared, and larger SpO2 disparity between different skin regions (by SpO2 imaging) have been found for individuals in the red/green wavelengths setup. The use of visible light (red and green) may impose a risk of SpO2 calibration due to the different skin penetration depths of these two wavelengths.

Fingertip Strain Plethysmography: Representation of Pulse Information based on Vascular Vibration

This study presents fingertip strain plethysmography (SPG) as a visual trace of cardiac cycles in peripheral vessels. The setup includes a small, sensitive MEMS strain sensor attached to the fingertip to capture the pulsatile vibrations corresponding to cardiac cycles. SPG is evaluated on 10 healthy subjects for the estimation of heart rate (HR) and heart rate variability (HRV), as well as heartbeat-derived respiratory rate (RR) which is an HRV parameter. The estimated parameters are compared with a simultaneously-recorded electrocardiogram (ECG) for HR and HRV, and an inertial sensor placed on the chest wall for RR. Bland-Altman analyses suggest small estimation biases of 0.03 beats-per-minute (BPM) and 0.38 ms for HR and HRV respectively, demonstrating excellent agreement between fingertip SPG and ECG. The average estimation accuracies of 99.88% (± 0.04), 96.43% (± 1.44), and 92.64% (± 2.30) for HR, HRV, and RR respectively, prove the reliability of SPG for hemodynamic monitoring.Clinical Relevance- Conventional plethysmography sensors are either cumbersome or susceptible to skin color. This effort is a fundamental step towards the augmentation of conventional methods, thus ensuring stable, clinical-grade hemodynamic monitoring.

A Secure Vitals Monitoring Point-of-Care Device

Point-of-care (POC) devices continuously monitor vital signs and provide health suggestions to users. However, the devices are not affordable to everyone due to their cost. Here, we design a POC device that can continuously estimate vital signs using fewer sensors and lower costs. We do so by measuring photoplethysmogram signals and temperature and then estimating the heart rate, blood oxygen saturation, respiration rate, and blood pressure. For keeping the vital data secure, an auto-encoder and a convolutional neural network were also used for encryption and abnormality detection, respectively. Tests on the hardware showed the design accurately obtained users' vitals. The proposed design is expected to be generalized to obtain other vitals and fabricated at a low cost, making it affordable to all people.

Perfusion index for early identification of regional anesthesia effectiveness: a narrative review

Regional anesthesia (RA) is a common and irreplaceable technique in clinical, which can be used in different surgery sites and control of acute and chronic pain, especially for outpatients, pediatrics and the elderly. RA demands are increasing during COVID-19 pandemic because many surgeries could be performed under RA to reduce the risk of cross-infection between patients and health care workers. Early and accurate identification of the effects of RA can help physicians make timely decisions about whether to supplement analgesics or switch to general anesthesia, which will save time and improve patient satisfaction in a busy operating room. Perfusion index (PI) is a parameter derived from photoplethysmography (PPG) and represents the ratio of pulsatile and non-pulsatile blood flow at monitoring sites. It reflects local perfusion and is mainly affected by stroke volume and vascular tone. With characteristics of non-invasive, rapid, simple, and objective, PI is widely used in clinical practice, such as fluid responsiveness prediction, nociceptive assessment, etc. Recently, many studies have assessed the accuracy of PI in early prediction of RA success, including brachial plexus block, sciatic nerve block, neuraxial anesthesia, paravertebral block, caudal block and stellate ganglion block. Successful RA often parallels increased PI. In this narrative review, we describe the principles and influencing factors of PI, and introduce the effects of PI on early identification of RA effectiveness.

On the Reliability of Wearable Technology: A Tutorial on Measuring Heart Rate and Heart Rate Variability in the Wild

Wearable sensors are quickly making their way into psychophysiological research, as they allow collecting data outside of a laboratory and for an extended period of time. The present tutorial considers fidelity of physiological measurement with wearable sensors, focusing on reliability. We elaborate on why ensuring reliability for wearables is important and offer statistical tools for assessing wearable reliability for between participants and within-participant designs. The framework offered here is illustrated using several brands of commercially available heart rate sensors. Measurement reliability varied across sensors and, more importantly, across the situations tested, and was highest during sleep. Our hope is that by systematically quantifying measurement reliability, researchers will be able to make informed choices about specific wearable devices and measurement procedures that meet their research goals.

Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies

Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.

Carbon-Based Textile Sensors for Physiological-Signal Monitoring

As the focus on physical health increases, the market demand for flexible wearable sensors increases. Textiles combined with sensitive materials and electronic circuits can form flexible, breathable high-performance sensors for physiological-signal monitoring. Carbon-based materials such as graphene, carbon nanotubes (CNTs), and carbon black (CB) have been widely utilized in the development of flexible wearable sensors due to their high electrical conductivity, low toxicity, low mass density, and easy functionalization. This review provides an overview of recent advancements in carbon-based flexible textile sensors, highlighting the development, properties, and applications of graphene, CNTs, and CB for flexible textile sensors. The physiological signals that can be monitored by carbon-based textile sensors include electrocardiogram (ECG), human body movement, pulse and respiration, body temperature, and tactile perception. We categorize and describe carbon-based textile sensors based on the physiological signals they monitor. Finally, we discuss the current challenges associated with carbon-based textile sensors and explore the future direction of textile sensors for monitoring physiological signals.

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.

Using Contactless Facial Image Recognition Technology to Detect Blood Oxygen Saturation

Since the outbreak of COVID-19, as of January 2023, there have been over 670 million cases and more than 6.8 million deaths worldwide. Infections can cause inflammation in the lungs and decrease blood oxygen levels, which can lead to breathing difficulties and endanger life. As the situation continues to escalate, non-contact machines are used to assist patients at home to monitor their blood oxygen levels without encountering others. This paper uses a general network camera to capture the forehead area of a person's face, using the RPPG (remote photoplethysmography) principle. Then, image signal processing of red and blue light waves is carried out. By utilizing the principle of light reflection, the standard deviation and mean are calculated, and the blood oxygen saturation is computed. Finally, the effect of illuminance on the experimental values is discussed. The experimental results of this paper were compared with a blood oxygen meter certified by the Ministry of Health and Welfare in Taiwan, and the experimental results had only a maximum error of 2%, which is better than the 3% to 5% error rates in other studies The measurement time was only 30 s, which is better than the one minute reported using similar equipment in other studies. Therefore, this paper not only saves equipment expenses but also provides convenience and safety for those who need to monitor their blood oxygen levels at home. Future applications can combine the SpO2 detection software with camera-equipped devices such as smartphones and laptops. The public can detect SpO2 on their own mobile devices, providing a convenient and effective tool for personal health management.

Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

Non-contact oxygen saturation monitoring for wound healing process using dual-wavelength simultaneous acquisition imaging system

Here we report the fabrication of a noncontact pulse oximeter system based on a dual-wavelength imaging system and its oxygen saturation monitoring performance during wound healing. The dual-wavelength imaging system consists of 660 nm and 940 nm light-emitting diodes and a multi-spectral camera that simultaneously accepts visible and near-infrared images. Using the proposed system, images were acquired at 30 fps at both wavelengths, and photoplethysmography signals were extracted by specifying a specific region in the images. We removed the signals caused by small movements and smoothed them using the discrete wavelet transform and moving average filter. To confirm the feasibility of the proposed noncontact oxygen saturation system, a wound model was created using a hairless mouse and oxygen saturation was measured during wound healing. The measured values were compared and analyzed using a reflective animal pulse oximeter. Through a comparative analysis of these two devices, the error of the proposed system was evaluated and the possibility of its clinical application and wound healing monitoring through oxygen saturation measurement confirmed.

Pulse Oximetry Imaging System Using Spatially Uniform Dual Wavelength Illumination

Pulse oximetry is a non-invasive method for measuring blood oxygen saturation. However, its detection scheme heavily relies on single-point measurements. If the oxygen saturation is measured at a single location, the measurements are influenced by the profile of illumination, spatial variations in blood flow, and skin pigment. To overcome these issues, imaging systems that measure the distribution of oxygen saturation have been demonstrated. However, previous imaging systems have relied on red and near-infrared illuminations with different profiles, resulting in inconsistent ratios between transmitted red and near-infrared light over space. Such inconsistent ratios can introduce fundamental errors when calculating the spatial distribution of oxygen saturation. In this study, we developed a novel illumination system specifically designed for a pulse oximetry imaging system. For the illumination system, we customized the integrating sphere by coating a mixture of barium sulfate and white paint inside it and by coupling eight red and eight near-infrared LEDs. The illumination system created identical patterns of red and near-infrared illuminations that were spatially uniform. This allowed the ratio between transmitted red and near-infrared light to be consistent over space, enabling the calculation of the spatial distribution of oxygen saturation. We believe our developed pulse oximetry imaging system can be used to obtain spatial information on blood oxygen saturation that provides insight into the oxygenation of the blood contained within the peripheral region of the tissue.

Multiwavelength tissue-mimicking phantoms with tunable vessel pulsation

Significance

For the development and routine characterization of optical devices used in medicine, tissue-equivalent phantoms mimicking a broad spectrum of human skin properties are indispensable.

Aim

Our work aims to develop a tissue-equivalent phantom suitable for photoplethysmography applications. The phantom includes the optical and mechanical properties of the three uppermost human skin layers (dermis, epidermis, and hypodermis, each containing different types of blood vessels) plus the ability to mimic pulsation.

Approach

While the mechanical properties of the polydimethylsiloxane base material are adjusted by different mixing ratios of a base and curing agent, the optical properties are tuned by adding titanium dioxide particles, India ink, and synthetic melanin in different concentrations. The layered structure of the phantom is realized using a doctor blade technique, and blood vessels are fabricated using molding wires of different diameters. The tissue-mimicking phantom is then integrated into an artificial circulatory system employing piezo-actuated double diaphragm pumps for testing.

Results

The optical and mechanical properties of human skin were successfully replicated. The diameter of the artificial blood vessels is linearly dependent on pump actuation, and the time-dependent expansion profile of real pulse forms were mimicked.

Conclusions

A tissue equivalent phantom suitable for the ex-vivo testing of opto-medical devices was demonstrated.

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers' physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.

Bioelectronic devices for light-based diagnostics and therapies

Light has broad applications in medicine as a tool for diagnosis and therapy. Recent advances in optical technology and bioelectronics have opened opportunities for wearable, ingestible, and implantable devices that use light to continuously monitor health and precisely treat diseases. In this review, we discuss recent progress in the development and application of light-based bioelectronic devices. We summarize the key features of the technologies underlying these devices, including light sources, light detectors, energy storage and harvesting, and wireless power and communications. We investigate the current state of bioelectronic devices for the continuous measurement of health and on-demand delivery of therapy. Finally, we highlight major challenges and opportunities associated with light-based bioelectronic devices and discuss their promise for enabling digital forms of health care.

Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography

Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a "gold standard" test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring.

Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

Reliability of smartphone measurements of peripheral oxygen saturation and heart rate in hypotensive patients measurement of vital signs with smartphones

Objective

With the increasing use of wearable technologies (smartphones and smartwatches), it has become possible to measure vital signs outside healthcare institutions without the need for an additional medical device. With the advancement in technologies, the accuracy of vital signs measured by smartphones and smartwatches has also increased. In this study, the accuracy of smart devices in the measurement of heart rate and saturation, which are two vital signs that are difficult to detect in conditions such as hypotension were investigated.

Materials and methods

The study was prospectively conducted in a tertiary healthcare center. In hypotensive patients who presented to the emergency department (ED) and required an arterial blood gas evaluation, oxygen saturation and heart rate values measured by a smartphone, those measured with a vital signs monitor (VSM) at the time of admission to the ED and oxygen saturation values measured by a blood gas analyzer (BGA) were compared.

Results

A total of 200 patients, 117 women and 83 men, were included in the study. It was determined that the correlation coefficients of the heart rate values measured by the vital signs monitor and smartphone were in a high statistical agreement. When the saturation values measured by the vital signs monitor, smartphone, and blood gas analyzer were compared, it was found that the intra-class correlation coefficients of the saturation values measured by the smartphone with reference to the blood gas analyzer and vital signs monitor were 0.957 and 0.949, respectively, indicating an excellent agreement.

Conclusion

Smartphones have as high efficiency as reference devices in measuring heart rate and saturation in hypotensive patients. In this way, hypotensive patients who need medical help can also have the opportunity to measure their vital parameters with their smartphones, without the need for any other medical device, before applying to the hospital or emergency health system. This may contribute to the improvement of the quality of life of the patients and the early and accurate information of the health care providers about the patient's health parameters.

Stable and Dynamic Multiparameter Monitoring on Chests Using Flexible Skin Patches with Self-Adhesive Electrodes and a Synchronous Correlation Peak Extraction Algorithm

Advances in wearable bioelectronics interfacing directly with skin offer important tools for non-invasive measurements of physiological parameters. However, wearable monitoring devices majorly conduct static sensing to avoid signal disturbance and unreliable contact with the skin. Dynamic multiparameter sensing is challenging even with the advanced flexible skin patches. This epidermal electronics system with self-adhesive conductive electrodes to supply stable skin contact and a unique synchronous correlation peak extraction (SCPE) algorithm to minimize motion artifacts in the photoplethysmogram (PPG) signals. The skin patch system can simultaneously and precisely monitor electrocardiogram (ECG), PPG, body temperature, and acceleration on chests undergoing daily activities. The low latency between the ECG and the PPG signals enables the SCPE algorithm that leads to reduced errors in deduced heart rates and improved performance in oxygen level determination than conventional adaptive filtering and wavelet transformation approaches. Dynamic multiparameter recording over 24 h by the system can reflect the circadian patterns of the wearers with low disturbance from motion artifacts. This demonstrated system may be applied for health monitoring in large populations to alleviate pressure on medical systems and assist management of public health crisis.

Measuring Arterial Oxygen Saturation Using Wearable Devices Under Varying Conditions

INTRODUCTION: Recently developed wearable monitoring devices can provide arterial oxygen saturation (S_po₂) measurements, offering potential for use in aerospace operations. Pilots and passengers are already using these technologies, but their performance has not yet been established under conditions experienced in the flight environment such as environmental hypoxia and concurrent body motion.METHODS: An initial evaluation was conducted in 10 healthy subjects who were studied in a normobaric chamber during normoxia and at a simulated altitude of 15,000 ft (4572 m; 11.8% oxygen). S_po₂ was measured simultaneously using a standard pulse oximeter and four wearable devices: Apple Watch Series 6; Garmin Fēnix 6 watch; Cosinuss^o Two in-ear sensor; and Oxitone 1000M wrist-worn pulse oximeter. Measurements were made while stationary at rest, during very slight body motion (induced by very low intensity cycling at 30 W on an ergometer), and during moderate body motion (induced by moderate intensity cycling at 150 W).RESULTS: Missed readings, defined as failure to record an S_po₂ value within 1 min, occurred commonly with all wearables. Even with only very slight body motion, most devices missed most readings (range of 12-82% missed readings) and the rate was higher with greater body motion (range 18-92%). One device tended to under-report S_po₂, while the other devices tended to over-report S_po₂. Performance decreased across the devices when oxygenation was reduced.DISCUSSION: In this preliminary evaluation, the wearable devices studied did not perform to the same standard as a traditional pulse oximeter. These limitations may restrict their utility in flight and require further investigation.Hearn EL, Byford J, Wolfe C, Agyei C, Hodkinson PD, Pollock RD, Smith TG. Measuring arterial oxygen saturation using wearable devices under varying conditions. Aerosp Med Hum Perform. 2023; 94(1):42-47.