Photoplethysmogram Analysis and Applications: An Integrative Review

Implantable bioelectronics and wearable sensors for kidney health and disease

Established clinical practices for monitoring kidney health and disease - including biopsy and serum biomarker analysis - suffer from practical limitations in monitoring frequency and lack adequate sensitivity for early disease detection. Engineering advances in biosensors have led to the development of wearable and implantable systems for monitoring of kidney health. Non-invasive microfluidic systems have demonstrated utility in the detection of kidney-relevant biomarkers, such as creatinine, urea and electrolytes in peripheral body fluids such as sweat, interstitial fluid, tears and saliva. Implantable systems may aid the identification of early transplant rejection through analysis of organ temperature and perfusion, and enable real-time assessment of inflammation through the use of thermal sensors. These technologies enable continuous, real-time monitoring of kidney health, offering complementary information to standard clinical procedures to alert physicians of changes in kidney health for early intervention. In this Review, we explore devices for monitoring renal biomarkers in peripheral biofluids and discuss developments in implantable sensors for the direct measurement of the local, biophysical properties of kidney tissue. We also describe potential clinical applications, including monitoring of chronic kidney disease, acute kidney injury and allograft health.

Predicting blood pressure without a cuff using a unique multi-modal wearable device and machine learning algorithm

Blood pressure is a critical risk factor for cardiovascular diseases (CVDs), yet most adults do not monitor it frequently enough to prevent serious complications. This is in part because the traditional cuff-based method is inconvenient, uncomfortable, and does not allow for continuous monitoring. To address these constraints, we developed a unique multi-modal wearable device and used a random forest regression (RFR) algorithm that resulted in a model capable of accurate cuffless blood pressure prediction. This multi-modal device features two photoplethysmography (PPG) sensors and two bioimpedance (BioZ) sensors to measure pulse wave propagation along the radial artery on the wrist. The redundancy in the design enhances prediction accuracy. To validate the device, a novel human subject study protocol was also developed that allows an individual's blood pressure to rise safely and repeatably by more than 40 mmHg (systolic pressure) from baseline measurements. In this study, using multiple pulsatile waveforms from the PPG and BioZ sensors as inputs into the machine learning prediction algorithm, showed that the model had higher accuracy than models using a single sensor. Specifically, the training, validation, and leaving one subject out of data sets all showed mean absolute errors of less than 3.3 mmHg for both systolic and diastolic blood pressures (BPs). While results from this test were promising, a subject-wise evaluation showed variability depending on how well an individual's BP distribution matched the training set. These findings demonstrate the potential for a universal model for cuffless BP estimation, with further validation needed in more diverse populations. Thus, the accompaniment of the RFR model with the multi-modal wearable device offers the potential for robust and continuous blood pressure monitoring, providing a unique and practical solution for long-term cardiovascular health management.

A PPG Signal Dataset Collected in Semi-Naturalistic Settings Using Galaxy Watch

The widespread adoption of consumer-grade wearable devices, such as Galaxy Watch, has revolutionized personal health monitoring as they enable continuous and non-invasive measurement of key cardiovascular indicators through photoplethysmography (PPG) sensors. However, existing datasets primarily rely on research-grade devices, limiting the applicability of consumer-grade wearables in real-world conditions. To address this gap, this study presents GalaxyPPG, a dataset collected from 24 participants that includes wrist-worn PPG signals from a Galaxy Watch 5 and an Empatica E4, alongside chest-worn ECG data from a Polar H10. Data were captured during diverse activities in a semi-naturalistic setting, providing insights into the sensing performance of consumer-grade wearables under motion- or stress-inducing activities. This dataset is designed to advance applications of PPG signals, such as HR tracking with diverse physical activities and HRV monitoring for stress detection. Additionally, we offer an open-source toolkit for data collection and analysis using Samsung Galaxy Watch, fostering reproducibility and further research leveraging this toolkit.

Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion

The physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination involves measuring physiological responses such as oxygen uptake (VO2) while performing the work. The objective of this study is to develop a procedure for automatic oxygen uptake prediction using the worker's forearm muscle activity and motion data. The fused IMU and EMG data were analyzed to build a bidirectional long-short-term memory (BiLSTM) model to predict VO2. The results show a strong correlation between the IMU and EMG features and oxygen uptake (R = 0.90, RMSE = 1.257 mL/kg/min). Moreover, measured (9.18 ± 1.97 mL/kg/min) and predicted (9.22 ± 0.09 mL/kg/min) average oxygen consumption to build one scaffold unit are significantly the same. This study concludes that the fusion of IMU and EMG features resulted in high model performance compared to IMU and EMG alone. The results can facilitate the continuous monitoring of the physiological status of construction workers and early detection of any potential occupational risks.

Multi-wavelength imaging photoplethysmography for non-invasive and non-contact assessment of burn severity

We report a non-contact burn severity assessment system using the image-based photoplethysmography (IPPG) technique by fabricating a multi-wavelength imaging system. In this burn assessment system, four wavelengths (visible light wavelengths of 405 nm, 520 nm, 660 nm, and near-infrared wavelength of 940 nm) were used, and burn severity was identified based on the fact that each wavelength has different penetration depths. Each wavelength was set to irradiate with the same optical power (1 mW/cm²), and IPPG was acquired using images captured at 35 frames per second for wavelengths with different penetration depths. To measure burn severity, we created burn lesion models using hairless mice. For each degree of burn, we acquired images of the burn area at four different wavelengths, measured IPPG from the acquired images, and observed the signal change at each wavelength to evaluate burn severity. In addition, while monitoring the healing process, we observed that IPPG recovered as the blood flow in the tissue normalized. Through the results of this study, we expect that IPPG technology will be used not only as a non-contact technology to evaluate burn severity, but also as a new method to monitor the burn recovery process in real time.

Stretchable multifunctional wearable system for real-time and on-demand thermotherapy of arthritis

Thermotherapy is a conventional and effective physiotherapy for arthritis. However, the current thermotherapy devices are often bulky and lack real-time temperature feedback and self-adjustment functions. Here, we developed a multifunctional wearable system for real-time thermotherapy of arthritic joints based on a multilayered flexible electronic device consisting of homomorphic hollow thin-film sensors and heater. The kirigami-serpentine thin-film sensors provide stretchability and rapid response to changes in environmental temperature and humidity, and the homomorphic design offers easy de-coupling of dual-modal sensing signals. Based on a closed-loop control, the thin-film Joule heater exhibits rapid and stable temperature regulation capability, with thermal response time < 1 s and maximum deviation < 0.4 °C at 45 °C. Based on the multifunctional wearable system, we developed a series of user-friendly gears and demonstrated programmable on-demand thermotherapy, real-time personal thermal management, thermal dehumidification, and relief of the pain via increasing blood perfusion. Our innovation offers a promising solution for arthritis management and has the potential to benefit the well-being of thousands of patients.

Real-time ancillary diagnostics for intraoperative assessment of intestinal viability in horses-looking for answers across species

Clinical intestinal viability assessment is associated with significant limitations, and there is an undisputable need for ancillary diagnostics during colic surgery. Human and companion animal surgeons struggle with similar intraoperative issues, yet there is little exchange between specialists. Therefore, this narrative review aimed to create an overview of real-time ancillary diagnostics with the potential for intraoperative intestinal viability assessment in horses. Most real-time ancillary diagnostics can be classified as either tissue perfusion or oxygenation assessments. Intestinal perfusion may be quantified using dark field microscopy, laser Doppler flowmetry, or fluorescence angiography (FA). In particular, indocyanine green FA has gained popularity in human medicine and is increasingly employed to predict intestinal injury. Intestinal oxygen saturation can be measured by pulse oximetry or mixed tissue oximetry. The latter can be conducted using visible light or near-infrared spectrophotometry, and these measurements correlate with clinical outcomes in various species. Other real-time diagnostics include thermography and techniques currently under development, such as laser speckle flowgraphy or photoacoustic imaging. The modalities discussed are minimally invasive and may be used for intraoperative assessments of the intestine. However, limitations include the occurrence of artifacts and the subjective nature of some modalities. Techniques such as indocyanine green FA and tissue oximetry are already available in veterinary practice and have the potential for use during colic surgery. However, blinded clinical trials are lacking in all species, and more research is needed to determine the accuracy and cutoff values in equine-specific intestinal lesions.

Structure function in photoplethysmographic signal dynamics for physiological assessment

Physiological systems are inherently complex, driven by non-linear interactions among various subsystems that govern their function across diverse spatiotemporal scales. Understanding this interconnectedness is crucial; in this sense, the structure function enables us to dissect the dynamic intricacies of biological responses. By examining amplitude fluctuations across different timescales, we can gain valuable insights into the variability and adaptability of these vital systems. A structure function serves as an essential tool for uncovering long-term correlations that highlight self-organizing behavior. Additionally, it effectively examines the fractal characteristics of short-term signals influenced by the measurement noise often present in biological data. This paper presents a novel investigation into how various parameters of the structure function of the PhotoPlethysmoGraphic (PPG) signal can serve as reliable physiological biomarkers indicative of an individual's cardiorespiratory activity level. Preliminary tests on 40 students from the Universidad Politécnica de Madrid (UPM), all young and healthy individuals aged between 19 and 30, yielded promising results. These findings enhance our understanding of PPG signal dynamics from a physiological standpoint and provide a procedural framework for real-time patient monitoring and health assessment in clinical environments.

Classification of anemic condition based on photoplethysmography signals and clinical dataset

OBJECTIVES

One of the worldwide public health issues mostly affecting children and expectant mothers is Anemia. Recently, non-invasive hemoglobin (Hb) measurements, such as machine learning (ML) algorithms, can diagnose Anemia more quickly and efficiently.

METHODS

To diagnose Anemia using photoplethysmography (PPG), two tracks are investigated in this paper, based on clinical data and PPG signals. We use state-of-the-art data for Hb levels, extracted from PPG signals. This first track's methodology is divided into three stages: the labelling of the data as normal and abnormal; the data pre-processing; and applying ML algorithms based on four given features. We extracted nineteen features for red and infrared measurements in the second track. The second track's methodology is broken down into five stages: labelling of the data; data processing; signal augmentation; feature extraction; and applying ML algorithms. A five-fold cross-validation technique was applied for both tracks.

RESULTS

We succeeded in classifying the anemic condition with 100 % classification accuracy. Our accurate detection of anemic status will promote preventive healthcare.

CONCLUSIONS

Ultimately, this proposed ML model in this paper validated the effectiveness of the ML algorithms as non-invasive techniques for identifying Anemia.

Clinical evaluation of a smart wristband for monitoring oxygen saturation, pulse rate, and respiratory rate

Recently, photoplethysmography-based vital parameter measurements have increased in popularity. However, clinical evaluation of these measurements is lacking. The objective of this study was to rigorously evaluate the clinical accuracy and reliability of a novel photoplethysmography-based wristband for measuring key vital parameters-oxygen saturation (SpO2), respiratory rate (RR), and pulse rate (PR)-during heart catheterisations. Vital parameters obtained during heart catheterisations by means of a photoplethysmography-based wristband (CardioWatch 287-2, Corsano Health) were compared to reference measurements performed by a Nellcor fingerclip (SpO2, PR) as well as a 5-lead ECG (RR) (QMAPP Haemodynamic Monitoring module, Fysicon B.V.) by means of correlation coefficients and root means squared error (RMSE). Effects of skin colour and arm hair density were additionally evaluated. In total, 945 samples from a total of 100 patients were included in the analysis. The correlation coefficients and RSME obtained for the difference between reference and photoplethysmography-based wristband measurements were r = 0.815 and 1.6% for SpO2, r = 0.976 and 0.9 brpm for RR, and r = 0.995 and 1.3 bpm for PR. Similar results were obtained across all skin colour and arm hair density subcategories. This study shows that photoplethysmography-based SpO2, RR, and PR measurements can be accurate during heart catheterisations. Future investigations are required to evaluate the wristband's performance under dynamic circumstances as well as over an extended time period. Trial registration: www.clinicaltrials.gov, NCT05566886.

Regional Variation in Pulse Transit Time in the Upper Limb Arteries During Hypotensive and Non-hypotensive Lower Body Negative Pressure

PURPOSE

Pulse transit time (PTT) is crucial in developing non-invasive cuffless blood pressure (BP) measurement devices. Sympathetic activation, due to its effect on PTT, can lead to erroneous estimation of BP. Sympathetic activation might affect the PTT differentially depending on the site where PTT is measured in the upper limb. This study aimed to decipher regional variation in PTT in response to sympathetic activation in three segments of the upper limb arteries. Exposure to graded lower body negative pressure (LBNP) at hypotensive (-30 mmHg and -40 mmHg) and non-hypotensive (-10 mmHg and -20 mmHg) levels has been used to produce sympathetic activation.

METHODS

This was a pilot study. Ten healthy subjects were recruited for the study, and recordings were done. Carotid, brachial, and radial pulse waveforms were recorded simultaneously by tonometry, and the finger pulse waveform was recorded by photoplethysmography (PPG). LBNP was applied at -10 mmHg, -20 mmHg, -30 mmHg, and -40 mmHg for two minutes. Carotid-brachial PTT (cbPTT), brachial-radial PTT (brPTT), and radial-finger PTT (rfPTT) were calculated.

RESULTS

cbPTT did not show any significant change, whereas both brPTT (0.02679±0.00635 sec at baseline vs. 0.02027±0.00662 sec at hypotensive LBNP; p=0.0386) and rfPTT (0.00908±0.00350 sec at baseline vs. 0.00585±0.00211 sec at hypotensive LBNP; p=0.003) showed a significant decrease in response to hypotensive LBNP. rfPTT (0.00908±0.00350 at baseline vs. 0.00534±0.00249s at non-hypotensive LBNP; p=0.0257) also showed a significant decline in response to non-hypotensive LBNP as well.

CONCLUSION

The current study reveals that in upper limb arteries, PTT response to LBNP shows regional variation with an accentuation of response from proximal to distal segments.

Machine Learning-Based VO2 Estimation Using a Wearable Multiwavelength Photoplethysmography Device

The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO2) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO2 is a powerful prognostic predictor of survival in patients with heart failure (HF) because it provides an indirect assessment of a patient's ability to increase cardiac output (CO). In addition, VO2 measurements, particularly VO2 max, are significant because they provide a reliable indicator of your cardiovascular fitness and aerobic endurance. However, traditional VO2 assessment requires bulky, breath-by-breath gas analysis systems, limiting frequent and continuous monitoring to specialized settings. This study presents a novel wrist-worn multiwavelength photoplethysmography (PPG) device and machine learning algorithm designed to estimate VO2 continuously. Unlike conventional wearables that rely on static formulas for VO2 max estimation, our algorithm leverages the data from the PPG wearable and uses the Beer-Lambert Law with inputs from five wavelengths (670 nm, 770 nm, 810 nm, 850 nm, and 950 nm), incorporating the isosbestic point at 810 nm to differentiate oxy- and deoxy-hemoglobin. A validation study was conducted with eight subjects using a modified Bruce protocol, comparing the PPG-based estimates to the gold-standard Parvo Medics gas analysis system. The results demonstrated a mean absolute error of 1.66 mL/kg/min and an R2 of 0.94. By providing precise, individualized VO2 estimates using direct tissue oxygenation data, this wearable solution offers significant clinical and practical advantages over traditional methods, making continuous and accurate cardiovascular assessment readily available beyond clinical environments.

Feature Extraction Tool Using Temporal Landmarks in Arterial Blood Pressure and Photoplethysmography Waveforms

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiological information for the prevention and treatment of cardiovascular diseases. Extracted features from these waveforms have diverse clinical applications, including predicting hyper- and hypo-tension, estimating cardiac output from ABP, and monitoring blood pressure and nociception from PPG. However, the lack of standardized tools for feature extraction limits their exploration and clinical utilization. In this study, we propose an automatic feature extraction tool that first detects temporal location of landmarks within each cardiac cycle of ABP and PPG waveforms, including the systolic phase onset, systolic phase peak, dicrotic notch, and diastolic phase peak using the iterative envelope mean method. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tool's ability to detect landmarks was evaluated using ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. We analyzed 34,267 cardiac cycles of ABP waveforms and 33,792 cardiac cycles of PPG waveforms. Additionally, to assess the tool's real-time landmark detection capability, we retrospectively analyzed 3,000 cardiac cycles of both ABP and PPG waveforms, collected from a Philips IntelliVue MX800 patient monitor. The tool's detection performance was assessed against markings by an experienced researcher, achieving average F1-scores and error rates for ABP and PPG as follows: (1) On MLORD dataset: systolic phase onset (99.77 %, 0.35 % and 99.52 %, 0.75 %), systolic phase peak (99.80 %, 0.30 % and 99.56 %, 0.70 %), dicrotic notch (98.24 %, 2.63 % and 98.72 %, 1.96 %), and diastolic phase peak (98.59 %, 2.11 % and 98.88 %, 1.73 %); (2) On real time data: systolic phase onset (98.18 %, 3.03 % and 97.94 %, 3.43 %), systolic phase peak (98.22 %, 2.97 % and 97.74 %, 3.77 %), dicrotic notch (97.72 %, 3.80 % and 98.16 %, 3.07 %), and diastolic phase peak (98.04 %, 3.27 % and 98.08 %, 3.20 %). This tool has significant potential for supporting clinical utilization of ABP and PPG waveform features and for facilitating feature-based machine learning models for various clinical applications where features derived from these waveforms play a critical role.

A Sensor-Based Classification for Neuromotor Robot-Assisted Rehabilitation

Neurological diseases leading to motor deficits constitute significant challenges to healthcare systems. Despite technological advancements in data acquisition, sensor development, data processing, and virtual reality (VR), a suitable framework for patient-centered neuromotor robot-assisted rehabilitation using collective sensor information does not exist. An extensive literature review was achieved based on 124 scientific publications regarding different types of sensors and the usage of the bio-signals they measure for neuromotor robot-assisted rehabilitation. A comprehensive classification of sensors was proposed, distinguishing between specific and non-specific parameters. The classification criteria address essential factors such as the type of sensors, the data they measure, their usability, ergonomics, and their overall impact on personalized treatment. In addition, a framework designed to collect and utilize relevant data for the optimal rehabilitation process efficiently is proposed. The proposed classifications aim to identify a set of key variables that can be used as a building block for a dynamic framework tailored for personalized treatments, thereby enhancing the effectiveness of patient-centered procedures in rehabilitation.

The MSPTDfast photoplethysmography beat detection algorithm: design, benchmarking, and open-source distribution

Objective:photoplethysmography is widely used for physiological monitoring, whether in clinical devices such as pulse oximeters, or consumer devices such as smartwatches. A key step in the analysis of photoplethysmogram (PPG) signals is detecting heartbeats. The multi-scale peak & trough detection (MSPTD) algorithm has been found to be one of the most accurate PPG beat detection algorithms, but is less computationally efficient than other algorithms. Therefore, the aim of this study was to develop a more efficient, open-source implementation of theMSPTDalgorithm for PPG beat detection, namedMSPTDfast (v.2).Approach.five potential improvements toMSPTDwere identified and evaluated on four datasets.MSPTDfast (v.2)was designed by incorporating each improvement which on its own reduced execution time whilst maintaining a highF1-score. After internal validation,MSPTDfast (v.2)was benchmarked against state-of-the-art beat detection algorithms on four additional datasets.Main results.MSPTDfast (v.2)incorporated two key improvements: pre-processing PPG signals to reduce the sampling frequency to 20 Hz; and only calculating scalogram scales corresponding to heart rates >30 bpm. During internal validationMSPTDfast (v.2)was found to have an execution time of between approximately one-third and one-twentieth ofMSPTD, and a comparableF1-score. During benchmarkingMSPTDfast (v.2)was found to have the highestF1-score alongsideMSPTD, and amongst one of the lowest execution times with onlyMSPTDfast (v.1),qppgfastandMMPD (v.2)achieving shorter execution times.Significance.MSPTDfast (v.2)is an accurate and efficient PPG beat detection algorithm, available in an open-source Matlab toolbox.

Distress detection in VR environment using Empatica E4 wristband and Bittium Faros 360

Introduction

Distress detection in virtual reality systems offers a wealth of opportunities to improve user experiences and enhance therapeutic practices by catering to individual physiological and emotional states.

Methods

This study evaluates the performance of two wearable devices, the Empatica E4 wristband and the Faros 360, in detecting distress in a motion-controlled interactive virtual reality environment. Subjects were exposed to a baseline measurement and two VR scenes, one non-interactive and one interactive, involving problem-solving and distractors. Heart rate measurements from both devices, including mean heart rate, root mean square of successive differences, and subject-specific thresholds, were utilized to explore distress intensity and frequency.

Results

Both the Faros and E4 sensors adequately captured physiological signals, with Faros demonstrating a higher signal-to-noise ratio and consistency. While correlation coefficients were moderately positive between Faros and E4 data, indicating a linear relationship, small mean absolute error and root mean square error values suggested good agreement in measuring heart rate. Analysis of distress occurrence during the interactive scene revealed that both devices detect more high- and medium-level distress occurrences compared to the non-interactive scene.

Discussion

Device-specific factors in distress detection were emphasized due to differences in detected distress events between devices.

A multi-band spectral data fusion method for improving the accuracy of quantitative spectral analysis

The signal-to-noise ratio of the spectrum is a critical determinant of detection accuracy in compositional analysis utilizing spectroscopy. The spectral data acquired by the spectrometer, while intended to capture essential sample characteristics, is often interspersed with various noise interferences. This contamination can severely disrupt the integrity of measurement outcomes. Therefore, this paper proposes the "multi-band spectral data fusion" method. In order to verify the feasibility of this method, this paper takes blood detection based on dynamic spectroscopy as an example and develops two models for each of the various components in blood. The experimental results show that when compared to modeling the raw spectrum data of the samples directly, the prediction accuracy of the model constructed using the new spectra processed by the multi-band spectral data fusion method suggested in this paper is greater. The correlation coefficient of the hemoglobin prediction set has improved by 13.48 %, and the root mean square error has decreased by 21.00 %. The correlation coefficient of the blood glucose prediction set improved by 4.07 %, and the root mean square error decreased by 12.78 %. The result demonstrates that the proposed method effectively mitigates the impact of random errors without compromising the spectral information content. The approach is not limited to blood component analysis but has potential applications across diverse spectroscopic domains, providing new ideas and methods for improving the accuracy of quantitative spectroscopic analysis.

Multi-point sensing organic light-emitting diode display based mobile cardiovascular monitor

Cardiovascular diseases are the major cause of death globally and require ubiquitous monitoring due to their asymptomatic yet modifiable nature. Photoplethysmography is an effective optical sensing technique for non-invasive health monitoring. However, its reliance on the current relatively large and rigid inorganic semiconductor-based light-emitting diodes and silicon photodiodes hampers high-resolution integration thus restricts a sensing from single measurement point. So, it limits detectable biomarkers to monitor cardiovascular diseases in a ubiquitous manner. In order to facilitate, here we report a single smartphone type multi-functional cardiovascular health monitor based on the massive array of organic photodiodes integrated into the most user interactive display device. Therefore, we achieved: 1) multi-point concurrent photoplethysmography and high-resolution dynamic image sensing, and 2) user-interactive sensing within the large display area. These advancements enabled new functions, including high-accuracy screening for cardiovascular diseases, blood pressure monitoring from both fingers, monitoring of finger blood vessels and flow dynamics, and single-device-based biofeedback. Applied machine learning enhanced diagnostic accuracy, with pilot studies showing results comparable to medical-grade devices. As a result, we believe smartphones harnessing the sensor organic light-emitting diode display could evolve into mobile health monitors and digital therapeutics thus revolutionizing diagnostic and treatment.

WF-PPG: A Wrist-finger Dual-Channel Dataset for Studying the Impact of Contact Pressure on PPG Morphology

Photoplethysmography (PPG) is a simple optical technique widely used in wearable devices for continuous cardiac health monitoring. However, the quality of PPG signals, particularly their morphology, is influenced by the contact pressure between the skin and the sensor. This variability in signal quality complicates complex tasks that rely on high-quality signals, such as blood pressure and heart rate variability estimation, making them less reliable or even impossible. To address this issue, we present a novel dataset (termed WF-PPG) comprising PPG signals from the wrist measured under varying contact pressures, along with high-quality PPG signals from the fingertip captured simultaneously. Data collection was conducted using a custom device setup capable of precisely adjusting the contact pressure for wrist PPG signals while also recording additional metrics such as contact pressure, electrocardiogram (ECG), blood pressure, and oxygen saturation. WF-PPG is designed to facilitate the analysis of effects of contact pressure on PPG morphology and to support the development and evaluation of advanced data-driven techniques aimed at enhancing the reliability of PPG-based health monitoring.

A review of machine learning methods for non-invasive blood pressure estimation

Blood pressure is a very important clinical measurement, offering valuable insights into the hemodynamic status of patients. Regular monitoring is crucial for early detection, prevention, and treatment of conditions like hypotension and hypertension, both of which increasing morbidity for a wide variety of reasons. This monitoring can be done either invasively or non-invasively and intermittently vs. continuously. An invasive method is considered the gold standard and provides continuous measurement, but it carries higher risks of complications such as infection, bleeding, and thrombosis. Non-invasive techniques, in contrast, reduce these risks and can provide intermittent or continuous blood pressure readings. This review explores modern machine learning-based non-invasive methods for blood pressure estimation, discussing their advantages, limitations, and clinical relevance.

Photoplethysmography as a noninvasive surrogate for microneurography in measuring stress-induced sympathetic nervous activation - A machine learning approach

The sympathetic nervous system (SNS) is essential for the body's immediate response to stress, initiating physiological changes that can be measured through sympathetic nerve activity (SNA). While microneurography (MNG) is the gold standard for direct SNA measurement, its invasive nature limits its practical use in clinical settings. This study investigates the use of multi-wavelength photoplethysmography (PPG) as a non-invasive alternative for SNA measurement. Key features are extracted from the pulsatile components of red and green PPG signals to train a linear regression machine learning (ML) model to predict R-wave-triggered spike count (SPR), a biomarker derived from MNG. The study correlates PPG-derived features with ground truth SPR to develop a predictive model capable of detecting SNA during induced physical stress (isometric handgrip and cold pressor) and cognitive stress (mental arithmetic and Stroop test). Unlike previous research that relies on subjective stress indicators, our work utilizes MNG-derived SPR as an objective ground truth for validation. Our findings demonstrate strong agreement between PPG-predicted SPR values and those obtained via MNG, with red PPG showing a higher correlation. The green wavelength PPG exhibits greater sensitivity in detecting stress-induced SNA, particularly during stress onset, where it outperforms the MNG method in capturing immediate responses to stressors such as mental arithmetic and the cold pressor task. To the best of our knowledge, this is the first study to directly compare PPG-derived SNA estimates with MNG, offering a promising pathway for developing wearable, non-invasive tools for continuous stress monitoring and sympathetic arousal detection.

Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography

With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities. However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame rates and missing data, leading to inaccuracies in HR measurements. This study addresses these issues by introducing low-complexity timing correction methods, including linear, cubic, and filter interpolation, to improve HR estimation from rPPG signals under conditions of irregular sampling and data loss. Through a comparative analysis, this study offers insights into efficient timing correction techniques for enhancing HR estimation from rPPG, particularly suitable for edge-computing applications where low computational complexity is essential. Cubic interpolation can provide robust performance in reconstructing signals but requires higher computational resources, while linear and filter interpolation offer more efficient solutions. The proposed low-complexity timing correction methods improve the reliability of rPPG-based HR estimation, making it a more robust solution for real-world healthcare applications.

Key Fundamentals and Examples of Sensors for Human Health: Wearable, Non-Continuous, and Non-Contact Monitoring Devices

The increasing demand for personalized healthcare, particularly among individuals requiring continuous health monitoring, has driven significant advancements in sensor technology. Wearable, non-continuous monitoring, and non-contact sensors are leading this innovation, providing novel methods for monitoring vital signs and physiological data in both clinical and home settings. However, there is a lack of comprehensive comparative studies assessing the overall functionality of these technologies. This paper aims to address this gap by presenting a detailed comparative analysis of selected wearable, non-continuous monitoring, and non-contact sensors used for health monitoring. To achieve this, we conducted a comprehensive evaluation of various sensors available on the market, utilizing key indicators such as sensor performance, usability, associated platforms functionality, data management, battery efficiency, and cost-effectiveness. Our findings highlight the strengths and limitations of each sensor type, thus offering valuable insights for the selection of the most appropriate technology based on specific healthcare needs. This study has the potential to serve as a valuable resource for researchers, healthcare providers, and policymakers, contributing to a deeper understanding of existing user-centered health monitoring solutions.

Evaluation of photoplethysmography-based monitoring of pulse rate, interbeat-intervals, and oxygen saturation during high-intensity interval training

BACKGROUND

Heart disease patients necessitate precise monitoring to ensure the safety and efficacy of their physical activities when managing conditions such as hypertension or heart failure. This study, therefore, aimed to evaluate the accuracy of photoplethysmography (PPG)-based monitoring of pulse rate (PR), interbeat-intervals (IB-I) and oxygen saturation (SpO2) during high-intensity interval training (HIIT).

METHODS

Between January and March 2024, healthy volunteers were subjected to a cycling HIIT workout with bike resistance increments to evaluate performance within different heart rate ranges. To determine the accuracy of PPG-based measurements for PR, IB-I, and SpO2 using the CardioWatch 287-2 (Corsano Health, the Netherlands), measurements throughout these ranges were compared to paired reference values from the Covidien Nellcor pulse oximeter (PM10N) and Vivalink's wearable ECG patch monitor. Subgroups were defined for Fitzpatrick skin type and gender.

RESULTS

In total, 35 healthy individuals participated, resulting in 7183 paired measurements for PR, 22,713 for IB-I, and 41,817 for SpO2. The PR algorithm showed an average root mean square (Arms) of 2.51 beats per minute (bpm), bias at 0.05 bpm, and limits of agreement (LoA) from -4.87 to 4.97 bpm. The IB-I algorithm achieved an Arms of 23.00 ms, a bias of 1.00 ms, and LoA from -43.82 to 46.21 ms. Finally, the SpO2 algorithm showed an Arms of 1.28%, a bias of 0.13%, and LoA from -2.37% to 2.62%. The results were consistent across different demographic subgroups.

CONCLUSIONS

This study demonstrates that the PPG-based CardioWatch 287-2 can accurately monitor PR, IB-I, and SpO2 during HIIT. However, further research is recommended to evaluate the algorithm's performance in heart disease patients during demanding exercise.

TG Vascutrack: A User-Friendly and Open-Source Software for Automated Extraction of Arterial Diameter and Velocity Profile Data From Vascular Ultrasound Videos

Existing automated software for vascular ultrasound data extraction lacks free, open-source options suitable for professionals without coding experience. These programs typically include signal-cleaning algorithms, resulting in processed output without access to raw data. To address these needs, we developed TG Vascutrack, an open-source and user-friendly software tailored for non-coder professionals. It features a graphical interface, multiple functionalities, and provides access to raw data. Comparative analysis against validated software and manual extraction revealed minimal biases and standard deviations in diameter and velocity measurements. TG Vascutrack offers a free, promising solution for non-coders needing automated vascular ultrasound data extraction.

Accuracy enhancement of metabolic index-based blood glucose estimation with a screening process for low-quality data

Significance

Many researchers have proposed various non-invasive glucose monitoring (NIGM) approaches using wearable or portable devices. However, due to the limited capacity of detectors for such compact devices and the movement of the body during measurement, the precision of the acquired data frequently diminishes, which can cause problems during actual use in daily life. In addition, intensive smoothing is often used in post-processing to mitigate the effects of erroneous values. However, this requires a considerable amount of data and results in a delay in the response to the actual blood glucose level (BGL).

Aim

Instead of just applying data smoothing in the post-process of the data acquisition, we propose an active low-quality data screening method in the pre-process. In the proposal phase of the screening process, we employ an analytical approach to examine and formulate factors that might affect the BGL estimation accuracy.

Approach

A signal quality index inspired by the standard deviation concept is introduced to detect visually apparent noise on signals. Furthermore, the total estimation error in the metabolic index (MI) is calculated based on potential perturbations defined by the signal-to-noise ratio (SNR) and the uncertainty due to discrete sampling. Thereafter, the acquired data were screened by these quality indices.

Results

By applying the proposed data screening process to the data obtained from a commercially available smartwatch device in the pre-process, the estimation accuracy of the MI-based BGL was improved significantly.

Conclusions

Adopting the proposed screen process improves BGL estimation accuracy in the smartwatch-based prototype. Applying the proposed screen process will facilitate the integration of wearable and continuous BGL monitoring into size- and SNR-limited devices such as smartwatches and smart rings.

OVAR-BPnet: A General Pulse Wave Deep Learning Approach for Cuffless Blood Pressure Measurement

Pulse wave analysis, a non-invasive and cuff-less approach, holds promise for blood pressure (BP) measurement in precision medicine. In recent years, pulse wave learning for BP estimation has undergone extensive scrutiny. However, prevailing methods still encounter challenges in grasping comprehensive features from pulse waves and generalizing these insights for precise BP estimation. In this study, we propose a general pulse wave deep learning (PWDL) approach for BP estimation, introduc-ing the OVAR-BPnet model to powerfully capture intricate pulse wave features and showcasing its effectiveness on multiple types of pulse waves. The approach involves constructing population pulse waves and employing a model comprising an omni-scale convolution subnet, a Vision Transformer subnet, and a multilayer perceptron subnet. This design enables the learning of both single-period and multi-period waveform features from multiple subjects. Additionally, the approach employs a data augmentation strategy to enhance the morphological features of pulse waves and devise a label sequence regularization strategy to strengthen the intrinsic relationship of the subnets' output. Notably, this is the first study to validate the performance of the deep learning approach of BP estimation on three types of pulse waves: photoplethysmography, forehead imaging photoplethysmography, and radial artery pulse pressure waveform. Experiments show that the OVAR-BPnet model has achieved advanced levels in both evaluation indicators and international evaluation criteria, demonstrating its excellent competitiveness and generalizability. The PWDL approach has the potential for widespread application in convenient and continuous BP monitoring systems.

Photoplethysmography-Based Angiography of Skin Tumors in Arbitrary Areas of Human Body

Noninvasive, rapid, and robust diagnostic techniques for clinical screening of tumors located in arbitrary areas of the human body are in demand. To address this challenge, we analyzed the feasibility of photoplethysmography-based angiography for assessing vascular structures within malignant and benign tumors. The proposed hardware and software were approved in a clinical study involving 30 patients with tumors located in the legs, torso, arms, and head. High-contrast and detailed vessel maps within both benign and malignant tumors were obtained. We demonstrated that capillary maps are consistent and can be interpreted using well-established dermoscopic criteria for vascular morphology. Vessel mapping provides valuable details, which may not be available in dermoscopic images and can aid in determining whether a tumor is benign or malignant. We believe that the proposed approach may become a valuable tool in the preliminary cancer diagnosis and is suitable for large-scale screening.

Utility of photoplethysmography in detecting elevated blood glucose among non-diabetics

INTRODUCTION

This study aimed to evaluate a technique of using photoplethysmography (PPG) for detecting elevated blood glucose in individuals.

METHOD

This is a prospective, cross-sectional study in which 500 healthy volunteers were recruited at a tertiary hospital in Singapore from October 2021 to February 2023. Capillary glucose was measured concurrently with PPG signals acquired using the wrist-worn Actxa Tracker (Spark + Series 2) and the In-Ear Prototype model SVT, which were worn for a duration of 8 min. Participants with a capillary blood test reading ≤11.1 mmol/dL had to consume a standard glucose tolerance drink and return 1 h later for a second capillary blood test. Two hundred and forty-four features were subsequently extracted from the PPG signals.

RESULTS

Of the 500 volunteers, 17 were excluded because of incomplete records. This led to a total of 483 participants' records being included in the final analysis. For predicting elevated capillary blood glucose level, demographics alone achieved an area under the curve (AUC) of 0.75. When wearable features derived from PPG were combined with demographics, AUC improved significantly to 0.82 (P = 0.0001).

CONCLUSION

This study shows that a non-invasive method of assessing diabetes mellitus risk using PPG combined with demographics is a viable option to provide a cheaper and more accessible modality for population-wide diabetes mellitus risk assessment.

Age-Related Changes in Blood Volume Pulse Wave at Fingers and Ears

OBJECTIVE

The decline in vascular elasticity with aging can be manifested in the shape of pulse wave. The study investigated the pulse wave features that are sensitive to age and the pattern of these features change with increasing age were examined.

METHODS

Five features were proposed and extracted from the photoplethysmography (PPG)-based pulse wave or its first derivative wave. The correlation between these PPG features and ages was studied in 100 healthy subjects with a wide range of ages (20-71 years). Piecewise regression coefficients were calculated to examine the rates of change of the PPG features with age at different age stages.

RESULTS

The proposed PPG features obtained from the finger showed a strong and significant correlation with age (with r = 0.76 - 0.77, p < 0.01), indicating higher sensitivity to age changes compared to the PPG features reported in previous studies (with r = 0.66 - 0.75). The correlation remained significant even after correcting for other clinical variables. The rate of change of the PPG feature values was found to be significantly faster in subjects aged ≥40 years compared to those aged < 40 years in the healthy population. This rate of change was similar to the age-related progression of arterial stiffness evaluated by pulse wave velocity (PWV), which is considered a gold standard for evaluating vascular stiffness.

CONCLUSIONS

The proposed PPG features showed a high correlation with chronological age in healthy subjects and exhibited a similar age-related change trend as PWV.

SIGNIFICANCE

With the convenience of PPG measures, the proposed age-related features have the potential to be used as biomarkers for vascular aging and estimating the risk of cardiovascular disease.

Dynamic Peripheral Hemoperfusion Distribution Monitoring Based on Janus Flexible Sensor System

OBJECTIVE

Peripheral vascular disease is a worldwide leading health concern. Real-time peripheral hemoperfusion monitoring during treatment is essential to plan treatment strategies to improve circulatory enhancement effects.

METHODS

The present work establishes a Janus flexible perfusion (JFP) sensor system for dynamic peripheral hemoperfusion monitoring. We develop a Janus structure with different Young's modulus to improve the mechanical properties for motion artifacts suppression. Besides, we propose a peripheral perfusion index (PPI) based on an optical perfusion model that is experimentally verified using an in-vitro model. The effectiveness of the system is assessed in three experimental scenarios, including motion artifact-robust test, induced vascular occlusion, and peripheral hemoperfusion monitoring with the intermittent pneumatic compression treatment.

RESULTS

The noise level of the traditional rigid sensor is five times that of the JFP sensor within the effective signal frequency domain when there is movement. The PPI can effectively discriminate between different peripheral hemoperfusion states and has a correlation coefficient of 0.92 with the Laser Doppler flowmetry (LDF) mean values. The kappa statistic between the JFP sensor and LDF is 0.78, indicating substantial agreement to estimate the peripheral hemoperfusion improvements.

CONCLUSION

The sensor system we proposed can monitor peripheral hemoperfusion variation in real-time and is insensitive to motion artifacts.

SIGNIFICANCE

The proposed sensing system provides a functional module for real-time estimation of peripheral hemoperfusion during clinical interventions.

Understanding the physiological transmission mechanisms of photoplethysmography signals: a comprehensive review

Objective. The widespread adoption of Photoplethysmography (PPG) as a non-invasive method for detecting blood volume variations and deriving vital physiological parameters reflecting health status has surged, primarily due to its accessibility, cost-effectiveness, and non-intrusive nature. This has led to extensive research around this technique in both daily life and clinical applications. Interestingly, despite the existence of contradictory explanations of the underlying mechanism of PPG signals across various applications, a systematic investigation into this crucial matter has not been conducted thus far. This gap in understanding hinders the full exploitation of PPG technology and undermines its accuracy and reliability in numerous applications.Approach. Building upon a comprehensive review of the fundamental principles and technological advancements in PPG, this paper initially attributes the origin of PPG signals to a combination of physical and physiological transmission processes. Furthermore, three distinct models outlining the concerned physiological transmission processes are synthesized, with each model undergoing critical examination based on theoretical underpinnings, empirical evidence, and constraints.Significance. The ultimate objective is to form a fundamental framework for a better understanding of physiological transmission processes in PPG signal generation and to facilitate the development of more reliable technologies for detecting physiological signals.

Open-source synthetic photoplethysmographic signal generator with analog output

The photoplethysmographic (PPG) signal of the finger is being used to create embedded devices that estimate physiological variables. This project outlines an innovative method for developing a synthetic PPG generator that produces both actual reference digital signals and their equivalent analog signals using open-source technology. A series of PPG profiles is synthesized using three variant Gaussian functions. A low-frequency trend induced by respiratory frequency and background noise are then added. To generate a diverse range of continuously variable PPG profiles within specified boundaries and customizable levels of interference, all parameters undergo random fluctuations on a cycle-by-cycle basis, as per user-defined constraints. The generated signal is then converted into its equivalent analog form through the use of an RC filter that low-frequency filters a Pulse-Width Modulation square wave that is modulated directly by the generated signal. The software returns different PPG profiles and allows the signal comparison before vs after the addition of different-intensity modulated respiratory trends and background noise. The digital signal is faithfully converted into an equivalent analog voltage signal capable of reproducing not only the waveform profile but also the respiratory trend and various levels of noise.

Assessment of activity and heart rate as indicators for acute stress in Atlantic salmon

The aim of this study was to assess whether activity and heart rate sensor implants can be used to measure stress and thus estimate one important welfare indicator for fish in aquaculture pens, and if such measurements correlate to physiological factors measured through blood sampling. The experiment consisted of two parts: i) a bio-logger study where implanted sensors were used to monitor activity and heart rates for fish undergoing stress (crowding); and ii) an analysis of blood constituents (cortisol, glucose, lactate, and chloride) of a second group of fish undergoing the same treatment. We found that activity measurements can be used to track high-impact stress events but may not be suitable to discern possibly nuanced reactions to stress impacts of lower magnitude. While heart rate was measured reliably, e.g., in showing clear circadian rhythms, it was no credible proxy for predicting stress in this study. Our results thus underline challenges observed in previous work around the use of heart rate as stress indicator, and imply that the translation of its meaning into a proxy for stress needs further work. Although tag-based monitoring of stress is not without its difficulties, studies such as this provide a wealth of information on salmon behaviour and physiology, and the links between these.

A water-resistant, ultrathin, conformable organic photodetector for vital sign monitoring

Ultrathin flexible photodetectors can be conformably integrated with the human body, offering promising advancements for emerging skin-interfaced sensors. However, the susceptibility to degradation in ambient and particularly in aqueous environments hinders their practical application. Here, we report a 3.2-micrometer-thick water-resistant organic photodetector capable of reliably monitoring vital sign while submerged underwater. Embedding the organic photoactive layer in an adhesive elastomer matrix induces multidimensional hybrid phase separation, enabling high adhesiveness of the photoactive layer on both the top and bottom surfaces with maintained charge transport. This improves the water-immersion stability of the photoactive layer and ensures the robust sealing of interfaces within the device, notably suppressing fluid ingression in aqueous environments. Consequently, our fabricated ultrathin organic photodetector demonstrates stability in deionized water or cell nutrient media over extended periods, high detectivity, and resilience to cyclic mechanical deformation. We also showcase its potential for vital sign monitoring while submerged underwater.

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

Frequency-Dependent Variability of Pulse Wave Transit Time: Pilot Study

The dynamics of the pulse wave (PW) associated with the PW transit time variability (PWTTV) determines the peripheral pulse rate variability, which is used as a surrogate for heart rate variability (HRV). The aim of the work is to analyze the frequency-dependent dynamics of PWTTV and to identify the possible frequency-phase modulation of PW velocity oscillations on the transit from the heart to the soft tissues of the distal parts of the upper extremities. RR-interval recordings and synchronous records of photoplethysmograms of 12 conditionally healthy subjects from the PhysioNet open database were used in this work. Using the Hilbert-Huang transform 3 spectral components of PWTTV and HRV were identified. It was shown that the amplitudes of PWTTV oscillations were many times (up to 8.4 times) smaller than the amplitudes of HRV, and the peaks of PWTTV spectral components were shifted towards higher frequencies than those of HRV. Functional relations between PWTTV and HRV, which can determine the phase modulation of periodic changes in the PW propagation velocity, were revealed.

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

Skin-interfacing wearable biosensors for smart health monitoring of infants and neonates

Health monitoring of infant patients in intensive care can be especially strenuous for both the patient and their caregiver, as testing setups involve a tangle of electrodes, probes, and catheters that keep the patient bedridden. This has typically involved expensive and imposing machines, to track physiological metrics such as heart rate, respiration rate, temperature, blood oxygen saturation, blood pressure, and ion concentrations. However, in the past couple of decades, research advancements have propelled a world of soft, wearable, and non-invasive systems to supersede current practices. This paper summarizes the latest advancements in neonatal wearable systems and the different approaches to each branch of physiological monitoring, with an emphasis on smart skin-interfaced wearables. Weaknesses and shortfalls are also addressed, with some guidelines provided to help drive the further research needed.

Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation

This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network-Long Short-Term Memory-Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system's promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance.

Camera-Based Seismocardiogram for Heart Rate Variability Monitoring

Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.

Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning

Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants' emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera.

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.

P. Comparison between perfusion index, pleth variability index, and pulse pressure variability for prediction of hypotension during major abdominal surgery under general anaesthesia: A prospective observational study

Background and Aims

Short-term hypotension after general anaesthesia can negatively impact surgical outcomes. This study compared the predictive potential of the pleth variability index (PVI), pulse pressure variability (PPV), and perfusion index (PI) for anaesthesia-induced hypotension. This study's primary objective was to evaluate the predictive potential of PI, PVI, and PPV for hypotension.

Methods

This observational study included 140 adult patients undergoing major abdominal surgery under general anaesthesia. Mean arterial pressure, heart rate, PVI, PPV, and PI were collected at 1-min intervals up to 20 min post anaesthesia induction. Hypotension was assessed at 5-min and 15-min intervals. Receiver operating characteristic (ROC) curves were plotted to determine the diagnostic performance and best cut-off for continuous variables in predicting a dichotomous outcome. Statistical significance was kept at P < 0.05.

Results

Hypotension prevalence within 5 and 15 min of anaesthesia induction was 36.4% and 45%, respectively. A PI cut-off of <3.5 had an area under the ROC curve (AUROC) of 0.647 (P = 0.004) for a 5-min hypotension prediction. The PVI's AUROC was 0.717 (P = 0.001) at cut-off >11.5, while PPV's AUROC was 0.742 (P = 0.001) at cut-off >12.5. At 15 min, PVI's AUROC was 0.615 (95% confidence interval 0.521-0.708, P = 0.020), with 54.9% positive predictive value and 65.2% negative predictive value.

Conclusion

PVI, PPV, and PI predicted hypotension within 5 min after general anaesthesia induction. PVI had comparatively higher accuracy, sensitivity, specificity, and positive predictive value than PI and PPV when predicting hypotension at 15 min.

Effect of recurrent task-induced acute stress on task performance, vagally mediated heart rate variability, and task-evoked pupil response

Advances in wearable sensor technologies can be leveraged to investigate behavioral and physiological responses in task-induced stress environments. Reliable and valid multidimensional assessments are required to detect stress given its multidimensional nature. This study investigated the effect of recurrent task-induced acute stress on task performance, vagally mediated heart variability measures (vmHRV) and task-evoked pupillary response (TEPR). Task performance, vmHRV measures, and TEPR were collected from 32 study participants while they performed a computer-based task in a recurrent task-induced acute stress environment. Mixed-effects modeling was used to assess the sensitivity of each outcome variable to experimental conditions. Repeated measures correlation tests were used to examine associations between outcome variables. Task performance degraded under stress. vmHRV measures were lower in the stress conditions relative to the no stress conditions. TEPR was found to be higher in the stress conditions compared to the no stress conditions. Task performance was negatively associated with the vmHRV measures, and degraded task performance was linked to increased TEPR in the stress conditions. There were positive associations between vmHRV measures. TEPR was negatively associated with vmHRV measures. Although task-induced stress degrades task performance, recurrent exposure to that stress could alter this effect via habituation. Further, our findings suggest that vmHRV measures and TEPR are sensitive enough to quantify psychophysiological responses to recurrent task-induced stress.

Estimation of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning

Introduction

Aortic stiffness plays a critical role in the evolution of cardiovascular diseases, but the assessment requires specialized equipment. Photoplethysmography (PPG) and single-lead electrocardiogram (ECG) are readily available in healthcare and wearable devices. We studied whether a brief PPG registration, alone or in combination with single-lead ECG, could be used to reliably estimate aortic stiffness.

Methods

A proof-of-concept study with simultaneous high-resolution index finger recordings of infrared PPG, single-lead ECG, and finger blood pressure (Finapres) was performed in 33 participants [median age 44 (range 21-66) years, 19 men] and repeated within 2 weeks. Carotid-femoral pulse wave velocity (cfPWV; two-site tonometry with SphygmoCor) was used as a reference. A brachial single-cuff oscillometric device assessed aortic pulse wave velocity (aoPWV; Arteriograph) for further comparisons. We extracted 136 established PPG waveform features and engineered 13 new with improved coupling to the finger blood pressure curve. Height-normalized pulse arrival time (NPAT) was derived using ECG. Machine learning methods were used to develop prediction models.

Results

The best PPG-based models predicted cfPWV and aoPWV well (root-mean-square errors of 0.70 and 0.52 m/s, respectively), with minor improvements by adding NPAT. Repeatability and agreement were on par with the reference equipment. A new PPG feature, an amplitude ratio from the early phase of the waveform, was most important in modelling, showing strong correlations with cfPWV and aoPWV (r = -0.81 and -0.75, respectively, both P < 0.001).

Conclusion

Using new features and machine learning methods, a brief finger PPG registration can estimate aortic stiffness without requiring additional information on age, anthropometry, or blood pressure. Repeatability and agreement were comparable to those obtained using non-invasive reference equipment. Provided further validation, this readily available simple method could improve cardiovascular risk evaluation, treatment, and prognosis.

Arch watch: current approaches and opportunities for improvement

Coarctation of the aorta (CoA) is a ductus arteriosus (DA)-dependent form of congenital heart disease (CHD) characterized by narrowing in the region of the aortic isthmus. CoA is a challenging diagnosis to make prenatally and is the critical cardiac lesion most likely to go undetected on the pulse oximetry-based newborn critical CHD screen. When undetected CoA causes obstruction to blood flow, life-threatening cardiovascular collapse may result, with a high burden of morbidity and mortality. Hemodynamic monitoring practices during DA closure (known as an "arch watch") vary across institutions and existing tools are often insensitive to developing arch obstruction. Novel measures of tissue oxygenation and oxygen deprivation may improve sensitivity and specificity for identifying evolving hemodynamic compromise in the newborn with CoA. We explore the benefits and limitations of existing and new tools to monitor the physiological changes of the aorta as the DA closes in infants at risk of CoA.

Validity of resting heart rate derived from contact-based smartphone photoplethysmography compared with electrocardiography: a scoping review and checklist for optimal acquisition and reporting

Background

With the rise of smartphone ownership and increasing evidence to support the suitability of smartphone usage in healthcare, the light source and smartphone camera could be utilized to perform photoplethysmography (PPG) for the assessment of vital signs, such as heart rate (HR). However, until rigorous validity assessment has been conducted, PPG will have limited use in clinical settings.

Objective

We aimed to conduct a scoping review assessing the validity of resting heart rate (RHR) acquisition from PPG utilizing contact-based smartphone devices. Our four specific objectives of this scoping review were to (1) conduct a systematic search of the published literature concerning contact-based smartphone device-derived PPG, (2) map study characteristics and methodologies, (3) identify if methodological and technological advancements have been made, and (4) provide recommendations for the advancement of the investigative area.

Methods

ScienceDirect, PubMed and SPORTDiscus were searched for relevant studies between January 1st, 2007, and November 6th, 2022. Filters were applied to ensure only literature written in English were included. Reference lists of included studies were manually searched for additional eligible studies.

Results

In total 10 articles were included. Articles varied in terms of methodology including study characteristics, index measurement characteristics, criterion measurement characteristics, and experimental procedure. Additionally, there were variations in reporting details including primary outcome measure and measure of validity. However, all studies reached the same conclusion, with agreement ranging between good to very strong and correlations ranging from r = .98 to 1.

Conclusions

Smartphone applications measuring RHR derived from contact-based smartphone PPG appear to agree with gold standard electrocardiography (ECG) in healthy subjects. However, agreement was established under highly controlled conditions. Future research could investigate their validity and consider effective approaches that transfer these methods from laboratory conditions into the "real-world", in both healthy and clinical populations.

Improving heart rate monitoring in the obese with time-of-flight photoplethysmography (TOF-PPG): a quantitative analysis of source-detector-distance effect

Commercial photoplethysmography (PPG) sensors rely on the measurement of continuous-wave diffuse reflection signals (CW-DRS) to monitor heart rate. Using Monte Carlo modeling of light propagation in skin, we quantitatively evaluate the dependence of continuous-wave photoplethysmography (CW-PPG) in commercial wearables on source-detector distance (SDD). Specifically, when SDD increases from 0.5 mm to 3.3 mm, CW-PPG signal increases by roughly 846% for non-obese (NOB) skin and roughly 683% for morbidly obese (MOB) skin. Ultimately, we introduce the concept of time-of-flight PPG (TOF-PPG) which can significantly improve heart rate signals. Our model shows that the optimized TOF-PPG improves heart rate monitoring experiences by roughly 47.9% in NOB and 93.2% in MOB when SDD = 3.3 mm is at green light. Moving forward, these results will provide a valuable source for hypothesis generation in the scientific community to improve heart rate monitoring.

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.