The use of photoplethysmography for assessing hypertension

Impact of macrohemodynamic manipulations during cardiopulmonary bypass on finger microcirculation assessed by photoplethysmography signal components

Objective.Continuous monitoring of the hemodynamic coherence between macro and microcirculation is difficult at the bedside. We tested the role of photoplethysmography (PPG) to real-time assessment of microcirculation during extreme manipulation of macrohemodynamics induced by the cardiopulmonary bypass (CPB).Approach.We analyzed the alternating (AC) and direct (DC) components of the finger PPG in 12 patients undergoing cardiac surgery with CPB at five moments: (1) before-CPB; (2) CPB-start, at the transition from pulsatile to non-pulsatile blood flow; (3) CPB-aortic clamping, at a sudden decrease in pump blood flow and volemia.; (4) CPB-weaning, during step-wise 20% decreases in pump blood flow and opposite proportional increases in native pulsatile blood flow; and (5) after-CPB.Main results.Nine Caucasian men and three women were included for analysis. Macrohemodynamic changes during CPB had an immediate impact on the PPG at all studied moments. Before-CPB the AC signal amplitude showed a median and IQR values of 0.0023(0.0013). The AC signal completely disappeared at CPB-start and at CPB-aortic clamping. During CPB weaning its amplitude progressively increased but remained lower than before CPB, at 80% [0.0008 (0.0005);p< 0.001], 60% [0.0010(0.0006);p< 0.001], and 40% [0.0013(0.0009);p= 0.011] of CPB flow. The AC amplitude returned close to Before-CPB values at 20% of CPB flow [0.0015(0.0008);p= 0.081], when CPB was completely stopped [0.0019 (0.0009);p= 0.348], and at after-CPB [0.0021(0.0009);p= 0.687]. The DC signal Before-CPB [0.95(0.02)] did not differ statistically from CPB-start, CPB-weaning and After-CPB. However, at CPB-aortic clamping, at no flow and a sudden drop in volemia, the DC signal decreased from [0.96(0.01)] to [0.94(0.02);p= 0.002].Significance.The macrohemodynamic alterations brought on by CPB were consistent with changes in the finger's microcirculation. PPG described local pulsatile blood flow (AC) as well as non-pulsatile blood flow and volemia (DC) in the finger. These findings provide plausibility to the use of PPG in ongoing hemodynamic coherence monitoring.

Digitalization of hypertension management: a paradigm shift

Hypertension, which stands as a leading global health challenge, demands a dynamic approach for its effective management. The traditional methods of managing hypertension, centered on periodic clinic visits for blood pressure measurement and pharmacological interventions, are increasingly being complemented and enhanced by digital technologies. The integration of wearable devices, mobile applications, personalized treatments, and telehealth solutions into healthcare system is reshaping traditional hypertension care. Digitalization of hypertension management extends to population health, in addition to individual patient benefits, aimed at preventing and controlling hypertension on a broader scale. However, this digital revolution in hypertension management brings forth challenges related to data security, data accuracy, equitable access, and standardization of devices by international regulatory agencies. Addressing these issues is equally important to ensure that the benefits of digital technologies are accessible to everyone, irrespective of socio-economic factors. This paper concludes with a forward-looking perspectives, emphasizing the potential of digitalization to modify the landscape of hypertension management.

State of the art of mobile health technologies use in clinical arrhythmia care

The rapid growth in consumer-facing mobile and sensor technologies has created tremendous opportunities for patient-driven personalized health management. The diagnosis and management of cardiac arrhythmias are particularly well suited to benefit from these easily accessible consumer health technologies. In particular, smartphone-based and wrist-worn wearable electrocardiogram (ECG) and photoplethysmography (PPG) technology can facilitate relatively inexpensive, long-term rhythm monitoring. Here we review the practical utility of the currently available and emerging mobile health technologies relevant to cardiac arrhythmia care. We discuss the applications of these tools, which vary with respect to diagnostic performance, target populations, and indications. We also highlight that requirements for successful integration into clinical practice require adaptations to regulatory approval, data management, electronic medical record integration, quality oversight, and efforts to minimize the additional burden to health care professionals.

Photoplethysmography Features Correlated with Blood Pressure Changes

Blood pressure measurement is a key indicator of vascular health and a routine part of medical examinations. Given the ability of photoplethysmography (PPG) signals to provide insights into the microvascular bed and their compatibility with wearable devices, significant research has focused on using PPG signals for blood pressure estimation. This study aimed to identify specific clinical PPG features that vary with different blood pressure levels. Through a literature review of 297 publications, we selected 16 relevant studies and identified key time-dependent PPG features associated with blood pressure prediction. Our analysis highlighted the second derivative of PPG signals, particularly the b/a and d/a ratios, as the most frequently reported and significant predictors of systolic blood pressure. Additionally, features from the velocity and acceleration photoplethysmograms were also notable. In total, 29 features were analyzed, revealing novel temporal domain features that show promise for further research and application in blood pressure estimation.

Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals

BACKGROUND/OBJECTIVES

Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD.

METHODS

This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI).

RESULTS

The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%.

CONCLUSIONS

This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease.

Accurate and 30-plus days reliable cuffless blood pressure measurements with 9-minutes personal photoplethysmograph data and mixed deduction learning

Cuffless blood pressure (BP) measurements have long been anticipated, and the PPG (Photoplethysmography)-only method is the most promising one since already embedded in many wearable devices. To further meet the clinical accuracy requirements, PPG-only BP predictions with personalized modeling for overcoming personal deviations have been widely studied, but all required tens to hundreds of minutes of personal PPG measurements for training. Moreover, their accurate test periods without calibration practice were not reported. In this work, we collected records of PPG data from our recruited subjects in real-life scenarios instead of relying on the openly available MIMIC dataset obtained from intensive care unit (ICU) patients. Since our objective is commercial application and a substantial reduction in training data, we tailored our model training to closely mimic real-world usage. To achieve this, we developed a training approach that only requires 9-minutes of personal PPG signal recordings and mixed with other PPG data from our recruited 364 subjects. The modeling is conducted with two-channel paired inputs to the convolutional neural network (CNN)-based model, which we called Mixed Deduction Learning (MDL). The test results of 88 samples from 15 subjects, under testing period up to 30-plus days without extra calibration, revealed that MDL meets most of the standards of AAMI, BHS, and IEEE 1708-2014 (for static test only) for BP measurement devices, which indicates MDL's long-term stability and consistency. Furthermore, we found that the model with two-channel inputs presents a trend of improving performance as the pool of mixed training data increased, while the conventional one-channel input revealed degraded performance. The outperformance of MDL is attributed to many significant features remained in the first CNN layer even when mixing personal 9-minutes data with the other 364 subjects. Consequently, PPG-only with MDL introduces a new avenue for overcoming challenges in training due to personal physiological variations. Given our consideration of real-life usage, this technology can be seamlessly translated to commercial applications.

Data-knowledge co-driven feature based prediction model via photoplethysmography for evaluating blood pressure

BACKGROUND

Knowledge feature (KF) with clear physiological significance of photoplethysmography are widely used in predicting blood pressure. However, KF primarily focus on local information of photoplethysmography, which may struggle to capture the overall characteristics.

METHODS

Firstly, functional data analysis (FDA) was introduced to extract two types of data feature (DF). Furthermore, data-knowledge co-driven feature (DKCF) was proposed by combining FDA and constraints of KF. Finally, random forest, ada boost, gradient boosting, support vector machine and deep neural network were adopted, to compare the abilities of KF, DFs and DKCF in predicting blood pressure with two datasets (A published dataset and a self-collected dataset).

RESULTS

Under the premise of extracting only 9 features, the average mean absolute errors (MAE) of systolic blood pressure (SBP) and diastolic blood pressure (DBP) obtained by DKCF are both the smallest in dataset 1. In dataset 2, DKCF acquires the smallest MAE in predicting SBP and obtains the second smallest MAE in predicting DBP.

CONCLUSIONS

The results demonstrate that low-dimensional DKCF of photoplethysmography is closely correlated with blood pressure, which may serve as an important indicator for health assessment.

Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases.

Combined Impact of Heart Rate Sensor Placements with Respiratory Rate and Minute Ventilation on Oxygen Uptake Prediction

Oxygen uptake (V˙O2) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate (HR) is a prominent physiological indicator correlated with V˙O2 and is often used for indirect V˙O2 prediction. This study investigates the impact of HR placement on V˙O2 prediction accuracy by analyzing HR data combined with the respiratory rate (RESP) and minute ventilation (V˙E) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at various intensities. Data on V˙O2, RESP, V˙E, and HR were collected and used to develop a neural network model for V˙O2 prediction. The influence of HR position on prediction accuracy was assessed via Bland-Altman plots, and model performance was evaluated by mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE). Our findings indicate that HR combined with RESP and V˙E (V˙O2HR+RESP+V˙E) produces the most accurate V˙O2 predictions (MAE: 165 mL/min, R2: 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of V˙O2 prediction decreases, particularly within high-intensity exercise. The substitution of HR with different anatomical sites significantly impacts V˙O2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering HR placement in V˙O2 prediction models, with RESP and V˙E serving as effective compensatory factors. These findings contribute to refining indirect V˙O2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements.

Examining the challenges of blood pressure estimation via photoplethysmogram

The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis.

Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24 GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14 mmHg and -0.20 ±5.50 mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

Recommendations for evaluating photoplethysmography-based algorithms for blood pressure assessment

Photoplethysmography (PPG) is a non-invasive optical technique that measures changes in blood volume in the microvascular tissue bed of the body. While it shows potential as a clinical tool for blood pressure (BP) assessment and hypertension management, several sources of error can affect its performance. One such source is the PPG-based algorithm, which can lead to measurement bias and inaccuracy. Here, we review seven widely used measures to assess PPG-based algorithm performance and recommend implementing standardized error evaluation steps in their development. This standardization can reduce bias and improve the reliability and accuracy of PPG-based BP estimation, leading to better health outcomes for patients managing hypertension.

Recent Advances in Materials, Devices and Algorithms Toward Wearable Continuous Blood Pressure Monitoring

Continuous blood pressure (BP) tracking provides valuable insights into the health condition and functionality of the heart, arteries, and overall circulatory system of humans. The rapid development in flexible and wearable electronics has significantly accelerated the advancement of wearable BP monitoring technologies. However, several persistent challenges, including limited sensing capabilities and stability of flexible sensors, poor interfacial stability between sensors and skin, and low accuracy in BP estimation, have hindered the progress in wearable BP monitoring. To address these challenges, comprehensive innovations in materials design, device development, system optimization, and modeling have been pursued to improve the overall performance of wearable BP monitoring systems. In this review, we highlight the latest advancements in flexible and wearable systems toward continuous noninvasive BP tracking with a primary focus on materials development, device design, system integration, and theoretical algorithms. Existing challenges, potential solutions, and further research directions are also discussed to provide theoretical and technical guidance for the development of future wearable systems in continuous ambulatory BP measurement with enhanced sensing capability, robustness, and long-term accuracy.

2D Hybrid Perovskite Sensors for Environmental and Healthcare Monitoring

Layered perovskites, a novel class of two-dimensional (2D) layered materials, exhibit versatile photophysical properties of great interest in photovoltaics and optoelectronics. However, their instability to environmental factors, particularly water, has limited their utility. In this study, we introduce an innovative solution to the problem by leveraging the unique properties of natural beeswax as a protective coating of 2D-fluorinated phenylethylammonium lead iodide perovskite. These photodetectors show outstanding figures of merit, such as a responsivity of >2200 A/W and a detectivity of 2.4 × 1018 Jones. The hydrophobic nature of beeswax endows the 2D perovskite sensors with an unprecedented resilience to prolonged immersion in contaminated water, and it increases the lifespan of devices to a period longer than one year. At the same time, the biocompatibility of the beeswax and its self-cleaning properties make it possible to use the very same turbidity sensors for healthcare in photoplethysmography and monitor the human heartbeat with clear systolic and diastolic signatures. Beeswax-enabled multipurpose optoelectronics paves the way to sustainable electronics by ultimately reducing the need for multiple components.

A machine learning-based approach for constructing remote photoplethysmogram signals from video cameras

BACKGROUND

Advancements in health monitoring technologies are increasingly relying on capturing heart signals from video, a method known as remote photoplethysmography (rPPG). This study aims to enhance the accuracy of rPPG signals using a novel computer technique.

METHODS

We developed a machine-learning model to improve the clarity and accuracy of rPPG signals by comparing them with traditional photoplethysmogram (PPG) signals from sensors. The model was evaluated across various datasets and under different conditions, such as rest and movement. Evaluation metrics, including dynamic time warping (to assess timing alignment between rPPG and PPG) and correlation coefficients (to measure the linear association between rPPG and PPG), provided a robust framework for validating the effectiveness of our model in capturing and replicating physiological signals from videos accurately.

RESULTS

Our method showed significant improvements in the accuracy of heart signals captured from video, as evidenced by dynamic time warping and correlation coefficients. The model performed exceptionally well, demonstrating its effectiveness in achieving accuracy comparable to direct-contact heart signal measurements.

CONCLUSIONS

This study introduces a novel and effective machine-learning approach for improving the detection of heart signals from video. The results demonstrate the flexibility of our method across various scenarios and its potential to enhance the accuracy of health monitoring applications, making it a promising tool for remote healthcare.

A flexible multimodal pulse sensor for wearable continuous blood pressure monitoring

Monitoring of arterial blood pressure via cuffless pulse waveform measurement at the wrist has an important clinical value for the early diagnosis and prevention of cardiovascular disease. However, accurate measurement of the radial pulse waveform is challenging owing to its subtle, wideband, and preload-dependent variation characteristics. Evidence shows that uncertainties or variations of wearing pressure and skin temperature can cause artifact signals in wrist pulse measurements, thus degrading blood pressure estimate accuracy and hindering precise clinical diagnosis. Herein, we report a flexible multisensory pulse sensor utilizing natural piezo-thermic transduction of human skin in conjunction with thin-film thermistors for the accurately measuring radial artery pulse waves with high fidelity and good anti-artifact performance. The flexible pulse sensor achieved a wide pressure measuring range (228.2 kPa), low detection limit (4 Pa), good linearity (R2 = 0.999), low hysteresis (2.45%), fast response (88 ms), and good durability and stability, thereby enabling accurate pulse measurement with high fidelity. The pulse sensor also monolithically integrated the simultaneous detections of skin temperature and wearing pressure for resisting artifact effects in pulse measurements. Through the fusion of multiple features extracted from the pulse waveform, wearing pressure, skin temperature and user's personal physical characteristics using an efficient multilayer perceptron, blood pressure is accurately estimated and good generalizability is achieved.

Application of artificial intelligence in hypertension

Hypertension is an important modifiable risk factor for morbidity and mortality associated with cardiovascular disease. The incidence of hypertension is increasing not only in Korea but also in many Western countries due to the aging of the population and the increase in unhealthy lifestyles. However, hypertension control rates remain low due to poor adherence to antihypertensive medications, low awareness of hypertension, and numerous factors that contribute to hypertension, including diet, environment, lifestyle, obesity, and genetics. Because artificial intelligence (AI) involves data-driven algorithms, AI is an asset to understanding chronic diseases that are influenced by multiple factors, such as hypertension. Although several hypertension studies using AI have been published recently, most are exploratory descriptive studies that are often difficult for clinicians to understand and have little clinical relevance. This review aims to provide a clinician-centered perspective on AI by showing recent studies on the relevance of AI for patients with hypertension. The review is organized into sections on blood pressure measurement and hypertension diagnosis, prognosis, and management.

Tissue mimicking materials and finger phantom design for pulse oximetry

Pulse oximetry represents a ubiquitous clinical application of optics in modern medicine. Recent studies have raised concerns regarding the potential impact of confounders, such as variable skin pigmentation and perfusion, on blood oxygen saturation measurement accuracy in pulse oximeters. Tissue-mimicking phantom testing offers a low-cost, well-controlled solution for characterizing device performance and studying potential error sources, which may thus reduce the need for costly in vivo trials. The purpose of this study was to develop realistic phantom-based test methods for pulse oximetry. Material optical and mechanical properties were reviewed, selected, and tuned for optimal biological relevance, e.g., oxygenated tissue absorption and scattering, strength, elasticity, hardness, and other parameters representing the human finger's geometry and composition, such as blood vessel size and distribution, and perfusion. Relevant anatomical and physiological properties are summarized and implemented toward the creation of a preliminary finger phantom. To create a preliminary finger phantom, we synthesized a high-compliance silicone matrix with scatterers for embedding flexible tubing and investigated the addition of these scatterers to novel 3D printing resins for optical property control without altering mechanical stability, streamlining the production of phantoms with biologically relevant characteristics. Phantom utility was demonstrated by applying dynamic, pressure waveforms to produce tube volume change and resultant photoplethysmography (PPG) signals. 3D printed phantoms achieved more biologically relevant conditions compared to molded phantoms. These preliminary results indicate that the phantoms show strong potential to be developed into tools for evaluating pulse oximetry performance. Gaps, recommendations, and strategies are presented for continued phantom development.

Preserving shape details of pulse signals for video-based blood pressure estimation

In recent years, imaging photoplethysmograph (iPPG) pulse signals have been widely used in the research of non-contact blood pressure (BP) estimation, in which BP estimation based on pulse features is the main research direction. Pulse features are directly related to the shape of pulse signals while iPPG pulse signals are easily disturbed during the extraction process. To mitigate the impact of pulse feature distortion on BP estimation, it is necessary to eliminate interference while retaining valuable shape details in the iPPG pulse signal. Contact photoplethysmograph (cPPG) pulse signals measured at rest can be considered as the undisturbed reference signal. Transforming the iPPG pulse signal to the corresponding cPPG pulse signal is a method to ensure the effectiveness of shape details. However, achieving the required shape accuracy through direct transformation from iPPG to the corresponding cPPG pulse signals is challenging. We propose a method to mitigate this challenge by replacing the reference signal with an average cardiac cycle (ACC) signal, which can approximately represent the shape information of all cardiac cycles in a short time. A neural network using multi-scale convolution and self-attention mechanisms is developed for this transformation. Our method demonstrates a significant improvement in the maximal information coefficient (MIC) between pulse features and BP values, indicating a stronger correlation. Moreover, pulse signals transformed by our method exhibit enhanced performance in BP estimation using different model types. Experiments are conducted on a real-world database with 491 subjects in the hospital, averaging 60 years of age.

Effect of transmural pressure on the estimation of arterial stiffness index from the photoplethysmographic waveform

The aim of this study was to find the effect of transmural pressure on the determination of the photoplethysmographic (PPG) waveform arterial stiffness index (PPGAI). The study was conducted on 51 subjects without diagnosis of cardiovascular disease, aged between 24 and 74 years. The relation between the transmural pressure, which is the difference between the arterial blood pressure and the PPG sensor contact pressure, and the PPGAI was determined. PPG, beat-to-beat blood pressure, and sensor contact pressure signals were recorded from the index, middle, and ring finger. The PPG sensor contact pressure of the index finger was increased from 20 to 120 mmHg. The aortic augmentation index (AIx@75) was estimated with a SphygmoCor device as a reference. High correlation coefficients r = 0.79 and r = 0.83 between PPGAI and AIx@75, and low PPGAI standard deviations were observed at the transmural pressures of 10 and 20 mmHg, respectively. Transmural pressure of 20 mmHg can be considered suitable for the PPG signal registration and PPGAI calculation for the assessment of arterial stiffness. In summary, the contact pressure of the sensor should be selected according to theblood pressure of the subject finger in order to achieve the transmural pressure suitable for the assessment of PPGAI and arterial stiffness.

Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis

Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis - which involves their joint analysis - can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled "signal quality indicators" to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. Conclusions: The majority of data within VitalDB is suitable for multimodal analysis.

Estimation of phase distortions of the photoplethysmographic signal in digital IIR filtering

Pre-processing of the photoplethysmography (PPG) signal plays an important role in the analysis of the pulse wave signal. The task of pre-processing is to remove noise from the PPG signal, as well as to transmit the signal without any distortions for further analysis. The integrity of the pulse waveform is essential since many cardiovascular parameters are calculated from it using morphological analysis. Digital filters with infinite impulse response (IIR) are widely used in the processing of PPG signals. However, such filters tend to change the pulse waveform. The aim of this work is to quantify the PPG signal distortions that occur during IIR filtering in order to select a most suitable filter and its parameters. To do this, we collected raw finger PPG signals from 20 healthy volunteers and processed them by 5 main digital IIR filters (Butterworth, Bessel, Elliptic, Chebyshev type I and type II) with varying parameters. The upper cutoff frequency varied from 2 to 10 Hz and the filter order-from 2nd to 6th. To assess distortions of the pulse waveform, we used the following indices: skewness signal quality index (SSQI), reflection index (RI) and ejection time compensated (ETc). It was found that a decrease in the upper cutoff frequency leads to damping of the dicrotic notch and a phase shift of the pulse wave signal. The minimal distortions of a PPG signal are observed when using Butterworth, Bessel and Elliptic filters of the 2nd order. Therefore, we can recommend these filters for use in applications aimed at morphological analysis of finger PPG waveforms of healthy subjects.

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.

Feasibility and Effectiveness of a Ring-Type Blood Pressure Measurement Device Compared With 24-Hour Ambulatory Blood Pressure Monitoring Device

BACKGROUNDS AND OBJECTIVES

This study aimed to evaluate the applicability and precision of a ring-type cuffless blood pressure (BP) measurement device, CART-I Plus, compared to conventional 24-hour ambulatory BP monitoring (ABPM).

METHODS

Forty patients were recruited, and 33 participants were included in the final analysis. Each participant wore both CART-I Plus and ABPM devices on the same arm for approximately 24 hours. BP estimation from CART-I Plus, derived from photoplethysmography (PPG) signals, were compared with the corresponding ABPM measurements.

RESULTS

The CART-I Plus recorded systolic blood pressure (SBP)/diastolic blood pressure (DBP) values of 131.4±14.1/81.1±12.0, 132.7±13.9/81.9±11.9, and 128.7±14.6/79.3±12.2 mmHg for 24-hour, daytime, and nighttime periods respectively, compared to ABPM values of 129.7±11.7/84.4±11.2, 131.9±11.6/86.3±11.1, and 124.5±13.6/80.0±12.2 mmHg. Mean differences in SBP/DBP between the two devices were 1.74±6.69/-3.24±6.51 mmHg, 0.75±7.44/-4.41±7.42 mmHg, and 4.15±6.15/-0.67±5.23 mmHg for 24-hour, daytime, and nighttime periods respectively. Strong correlations were also observed between the devices, with r=0.725 and r=0.750 for transitions in SBP and DBP from daytime to nighttime, respectively (both p<0.001).

CONCLUSIONS

The CART-I Plus device, with its unique ring-type design, shows promising accuracy in BP estimation and offers a potential avenue for continuous BP monitoring in clinical practice.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT06084065.

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.

Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning

Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart's blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland-Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications.

First-in-Human Study for Evaluating the Accuracy of Smart Ring Based Cuffless Blood Pressure Measurement

BACKGROUND

Recently, a ring-type cuffless blood pressure (BP) measuring device has been developed. This study was a prospective, single arm, first-in-human pivotal trial to evaluate accuracy of BP measurement by the new device.

METHODS

The ring-type smart wearable monitoring device measures photoplethysmography signals from the proximal phalanx and transmits the data wirelessly to a connected smartphone. For the BP comparison, a cuff was worn on the arm to check the reference BP by auscultatory method, while the test device was worn on the finger of the opposite arm to measure BP simultaneously. Measurements were repeated for up to three sets each on the left and right arms. The primary outcome measure was mean difference and standard deviation of BP differences between the test device and the reference readings.

RESULTS

We obtained 526 sets of systolic BP (SBP) and 513 sets of diastolic BP (DBP) from 89 subjects, with ranges of 80 to 175 mmHg and 43 to 122 mmHg for SBP and DBP, respectively. In sample-wise comparison, the mean difference between the test device and the reference was 0.16 ± 5.90 mmHg (95% limits of agreement [LOA], -11.41, 11.72) in SBP and -0.07 ± 4.68 (95% LOA, -9.26, 9.10) in DBP. The test device showed a strong correlation with the reference for SBP (r = 0.94, P < 0.001) and DBP (r = 0.95, P < 0.001). There were consistent results in subject-wise comparison.

CONCLUSION

The new ring-type BP measuring device showed a good correlation for SBP and DBP with minimal bias compared with an auscultatory method.

Wearable Sensors as a Preoperative Assessment Tool: A Review

Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.

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.

Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG

Remote photoplethysmography (rPPG) provides a non-contact method for measuring blood volume changes. In this study, we compared rPPG signals obtained from video cameras with traditional contact-based photoplethysmography (cPPG) to assess the effectiveness of different RGB channels in cardiac signal extraction. Our objective was to determine the most effective RGB channel for detecting blood volume changes and estimating heart rate. We employed dynamic time warping, Pearson's correlation coefficient, root-mean-square error, and Beats-per-minute Difference to evaluate the performance of each RGB channel relative to cPPG. The results revealed that the green channel was superior, outperforming the blue and red channels in detecting volumetric changes and accurately estimating heart rate across various activities. We also observed that the reliability of RGB signals varied based on recording conditions and subject activity. This finding underscores the importance of understanding the performance nuances of RGB inputs, crucial for constructing rPPG signals in algorithms. Our study is significant in advancing rPPG research, offering insights that could benefit clinical applications by improving non-contact methods for blood volume assessment.

Photoplethysmography-based cuffless blood pressure estimation: an image encoding and fusion approach

Objective.Photoplethysmography (PPG) is a promising wearable technology that detects volumetric changes in microcirculation using a light source and a sensor on the skin's surface. PPG has been shown to be useful for non-invasive blood pressure (BP) measurement. Deep learning-based BP measurements are now gaining popularity. However, almost all methods focus on 1D PPG. We aimed to design an end-to-end approach for estimating BP using image encodings from a 2D perspective.Approach.In this paper, we present a BP estimation approach based on an image encoding and fusion (BP-IEF) technique. We convert the PPG into five image encodings and use them as input. The proposed BP-IEF consists of two parts: an encoder and a decoder. In addition, three kinds of well-known neural networks are taken as the fundamental architecture of the encoder. The decoder is a hybrid architecture that consists of convolutional and fully connected layers, which are used to fuse features from the encoder.Main results.The performance of the proposed BP-IEF is evaluated on the UCI database in both non-mixed and mixed manners. On the non-mixed dataset, the root mean square error and mean absolute error for systolic BP (SBP) are 13.031 mmHg and 9.187 mmHg respectively, while for diastolic BP (DBP) they are 5.049 mmHg and 3.810 mmHg. On the mixed dataset, the corresponding values for SBP are 4.623 mmHg and 3.058 mmHg, while for DBP the values are 2.350 mmHg and 1.608 mmHg. In addition, both SBP and DBP estimation on the mixed dataset achieved grade A compared to the British Hypertension Society standard. The DBP estimation on the non-mixed dataset also achieved grade A.Significance.The results indicate that the proposed approach has the potential to improve on the current mobile healthcare for cuffless BP measurement.

Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring?

Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.

Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.

Practical Application of a New Cuffless Blood Pressure Measurement Method

It would be useful to develop a reliable method for the cuffless measurement of blood pressure (BP), as such a method could be made available anytime and anywhere for the effective screening and monitoring of arterial hypertension. The purpose of this study is to evaluate blood pressure measurements through a CardioQVARK device in clinical practice in different patient groups.

METHODS

This study involved 167 patients aged 31 to 88 years (mean 64.2 ± 7.8 years) with normal blood pressure, high blood pressure, and compensated high blood pressure. During each session, three routine blood pressure measurements with intervals of 30 s were taken using a sphygmomanometer with an appropriate cuff size, and the mean value was selected for comparison. The measurements were carried out by two observers trained at the same time with a reference sphygmomanometer using a Y-shaped connector. In the minute following the last cuff-based measurements, an electrocardiogram (ECG) with an I-lead and a photoplethysmocardiogram were recorded simultaneously for 3 min with the CardioQVARK device. We compared the systolic and diastolic BP obtained from a cuff-based mercury sphygmomanometer and smartphone-case-based BP device: the CardioQVARK monitor. A statistical analysis plan was developed using the IEEE Standard for Wearable Cuffless Blood Pressure Devices. Bland-Altman plots were used to estimate the precision of cuffless measurements.

RESULTS

The mean difference between the values defined by CardioQVARK and the cuff-based sphygmomanometer for systolic blood pressure (SBP) was 0.31 ± 3.61, while that for diastolic blood pressure (DBP) was 0.44 ± 3.76. The mean absolute difference (MAD) for SBP was 3.44 ± 2.5 mm Hg, and that for DBP was 3.21 ± 2.82 mm Hg. In the subgroups, the smallest error (less than 3 mm Hg) was observed in the prehypertension group, with a slightly larger error (up to 4 mm Hg) found among patients with a normal blood pressure and stage 1 hypertension. The largest error was found in the stage 2 hypertension group (4-5.5 mm Hg). The largest error was 4.2 mm Hg in the high blood pressure group. We, therefore, did not record an error in excess of 7 mmHg, the upper boundary considered acceptable in the IEEE recommendations. We also did not reach a mean error of 5 mmHg, the upper boundary considered acceptable according to the very recent ESH recommendations. At the same time, in all groups of patients, the systolic blood pressure was determined with an error of less than 5 mm Hg in more than 80% of patients. While this study shows that the CardioQVARK device meets the standards of IEEE, the Bland-Altman analysis indicates that the cuffless measurement of diastolic blood pressure has significant bias. The difference was very small and unlikely to be of clinical relevance for the individual patient, but it may well have epidemiological relevance on a population level. Therefore, the CardioQVARK device, while being worthwhile for monitoring patients over time, may not be suitable for screening purposes. Cuffless blood pressure measurement devices are emerging as a convenient and tolerable alternative to cuff-based devices. However, there are several limitations to cuffless blood pressure measurement devices that should be considered. For instance, this study showed a high proportion of measurements with a measurement error of <5 mmHg, while detecting a small, although statistically significant, bias in the measurement of diastolic blood pressure. This suggests that this device may not be suitable for screening purposes. However, its value for monitoring BP over time is confirmed. Furthermore, and most importantly, the easy measurement method and the device portability (integrated in a smartphone) may increase the self-awareness of hypertensive patients and, potentially, lead to an improved adherence to their treatment.

CONCLUSION

The cuffless blood pressure technology developed in this study was tested in accordance with the IEEE protocol and showed great precision in patient groups with different blood pressure ranges. This approach, therefore, has the potential to be applied in clinical practice.

Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters

Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios.

Heart rate variability and haemodynamic function in individuals with hypertrophic cardiomyopathy

OBJECTIVES

Heart rate variability (HRV) is a measure of cardiac autonomic function. This study: (1) evaluated the differences in HRV and haemodynamic function between individuals with hypertrophic cardiomyopathy (HCM) and healthy controls, and (2) determined the relationship between HRV and haemodynamic variables in individuals with HCM.

METHODS

Twenty-eight individuals with HCM (n = 7, females; age 54 ± 15 years; body mass index: 29 ± 5 kg/m2 ) and 28 matched healthy individuals (n = 7 females; age 54 ± 16 years; body mass index: 29 ± 5 kg/m2 ) completed 5-min HRV and haemodynamic measurements under resting (supine) conditions using bioimpedance technology. Frequency domain HRV measures (absolute and normalized low-frequency power (LF), high-frequency power (HF) and LF/HF ratio) and RR interval were recorded.

RESULTS

Individuals with HCM demonstrated higher vagal activity (i.e., absolute unit of HF power (7.40 ± 2.50 vs. 6.03 ± 1.35 ms2 , p = 0.01) but lower RR interval (914 ± 178 vs. 1014 ± 168 ms, p = 0.03) compared to controls. Stroke volume (SV) index and cardiac index were lower in HCM compared with healthy individuals (SV, 33 ± 9 vs. 43 ± 7 ml‎/beat‎/m², p < 0.01; cardiac index,2.33 ± 0.42 vs. 3.57 ± 0.82 L/min/m2 , p < 0.01), but total peripheral resistance (TPR) was higher in HCM (3468 ± 1027 vs. 2953 ± 1050 dyn·s·m2 cm-5 , p = 0.03). HF power was significantly related to SV (r = -0.46, p < 0.01) and TPR (r = 0.28, p < 0.05) in HCM.

CONCLUSIONS

Short-term frequency domain indices of HRV provide a feasible approach to assess autonomic function in individuals with HCM. Vagal activity, represented by HF power, is increased, and associated with peripheral resistance in individuals with HCM.

Assessing the Validity of Several Heart Rate Monitors in Wearable Technology While Mountain Biking

Purpose

This study sought to assess the validity of several heart rate (HR) monitors in wearable technology during mountain biking (MTB), compared to the Polar H7® HR monitor, used as the criterion device.

Methods

A total of 20 participants completed two MTB trials while wearing six HR monitors (5 test devices, 1 criterion). HR was recorded on a second-by-second basis for all devices analyzed. After data processing, validity measures were calculated, including 1. error analysis: mean absolute percentage errors (MAPE), mean absolute error (MAE), and mean error (ME), and 2. Correlation analysis: Lin's concordance correlation coefficient (CCC) and Pearson's correlation coefficient (r). Thresholds for validity were set at MAPE < 10% and CCC > 0.7.

Results

The only device that was found to be valid during mountain biking was the Suunto Spartan Sport watch with accompanying HR monitor, with a MAPE of 0.66% and a CCC of 0.99 for the overall, combined data.

Conclusion

If a person would like to track their HR during mountain biking, for pacing, training, or other reasons, the devices best able to produce valid results are chest-based, wireless electrocardiogram (ECG) monitors, secured by elastic straps to minimize the movement of the device, such as the Suunto chest-based HR monitor.

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.

Insights into vascular physiology from sleep photoplethysmography

STUDY OBJECTIVES

Photoplethysmography (PPG) in consumer sleep trackers is now widely available and used to assess heart rate variability (HRV) for sleep staging. However, PPG waveform changes during sleep can also inform about vascular elasticity in healthy persons who constitute a majority of users. To assess its potential value, we traced the evolution of PPG pulse waveform during sleep alongside measurements of HRV and blood pressure (BP).

METHODS

Seventy-eight healthy adults (50% male, median [IQR range] age: 29.5 [23.0, 43.8]) underwent overnight polysomnography (PSG) with fingertip PPG, ambulatory blood pressure monitoring, and electrocardiography (ECG). Selected PPG features that reflect arterial stiffness: systolic to diastolic distance (∆T_norm), normalized rising slope (Rslope) and normalized reflection index (RI) were derived using a custom-built algorithm. Pulse arrival time (PAT) was calculated using ECG and PPG signals. The effect of sleep stage on these measures of arterial elasticity and how this pattern of sleep stage evolution differed with participant age were investigated.

RESULTS

BP, heart rate (HR) and PAT were reduced with deeper non-REM sleep but these changes were unaffected by the age range tested. After adjusting for lowered HR, ∆T_norm, Rslope, and RI showed significant effects of sleep stage, whereby deeper sleep was associated with lower arterial stiffness. Age was significantly correlated with the amount of sleep-related change in ∆T_norm, Rslope, and RI, and remained a significant predictor of RI after adjustment for sex, body mass index, office BP, and sleep efficiency.

CONCLUSIONS

The current findings indicate that the magnitude of sleep-related change in PPG waveform can provide useful information about vascular elasticity and age effects on this in healthy adults.

Non-Invasive Blood Pressure Sensing via Machine Learning

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.

A novel CS-NET architecture based on the unification of CNN, SVM and super-resolution spectrogram to monitor and classify blood pressure using photoplethysmography

CONTEXT

Continuous blood pressure (BP) monitoring plays an important role while treating various cardiovascular diseases and hypertension. A high correlation between arterial blood pressure (ABP) and Photoplethysmogram (PPG) signal enables using a PPG signal to monitor and classify BP continuously. Control of BP in realtime is the basis for the prevention of hypertension.

PROPOSED APPROACH

This work proposes a CS-NET architecture by unifying CNN and SVM approaches to classify BP using PPG signals. The main objective of the CS-NET method is to establish an accurate and reliable algorithm for the ABP classification.

METHODOLOGY

ABP signals are labeled normal and abnormal using the hypertension criteria the American College of Cardiology (ACC)/American Heart Association (AHA) laid down. The proposed CS-NET model incorporates three critical steps in three successive stages. The first stage includes converting a preprocessed PPG signal into a time-frequency (TF) representation called a super-resolution spectrogram by superlet transform. The second stage uses a convolutional neural network (CNN) model with several hidden layers to extract morphological features from every PPG super-resolution spectrogram. The third stage uses a support vector machine (SVM) classifier to classify the PPG signal.

RESULTS

PPG signals are used to train and test the proposed model. The performance of the proposed CS-NET method is tested using MIMIC-II, MIMIC-III, and PPG-BP-figshare database in terms of accuracy and F1 score. Moreover, the CS-NET method achieves better results with high accuracy when compared with other benchmark approaches that require an electrocardiogram signal for reference.

CONCLUSIONS

The proposed model achieved an aggregate classification accuracy of 98.21% across a five-fold cross-validation technique, making it a reliable approach for BP classification in clinical settings and realtime monitoring.

Evaluation of the effect of photoplethysmograms on workers' exposure to methyl bromide using second derivative

Methyl bromide (MB) is worldwide the only effective fumigant heavily used for quarantine pre-shipment treatment and has a critical use exemption for soil fumigations due to its excellent permeability and insecticidal effect. However, MB should be replaced as it is an an ozone-depleting substance and also highly toxic to humans. Recently, MB has been shown to be hazardous even for asymptomatic workers, affecting their central and autonomic nervous systems. However, the effects of MB exposure on vascular health have not been explored. This study aimed to determine whether MB affects the arterial system of asymptomatic workers. We measured the second derivative of the photoplethysmogram (SDPTG) indices, which are indicators of vascular load and aging, and urinary bromide ion (Br-) concentrations in 44 fumigators (study group) and 20 inspectors (control group) before and after fumigation. In fumigators, the mean values of post-work SDPTG indices (b/a, c/a, d/a, e/a, and SDPTG aging index) and Br- levels were significantly changed compared to their pre-work values (p < 0.05), indicating a negative effect on their cardiovascular health. In contrast, SDPTG indices and Br- levels in inspectors did not show any differences before and after work. All SDPTG indices except c/a showed significant correlations with Br- levels in all individuals (p < 0.05). In conclusion, the Br- levels and SDPTG indices of fumigators varied after MB work, and they experienced negative effects on their health despite being asymptomatic.

Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning

BACKGROUND

Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults.

METHODS

Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP).

RESULTS

Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness.

CONCLUSION

Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.

Blood Pressure Measurement Based on the Camera and Inertial Measurement Unit of a Smartphone: Instrument Validation Study

Background

Even though several mobile apps that can measure blood pressure have been developed, the data about the accuracy of these apps are limited.

Objective

We assessed the accuracy of AlwaysBP (test) in blood pressure measurement compared with the standard, cuff-based, manual method of brachial blood pressure measurement (reference).

Methods

AlwaysBP is a smartphone software that estimates systolic blood pressure (SBP) and diastolic blood pressure (DBP) based on pulse transit time (PTT). PTT was calculated with a finger photoplethysmogram and seismocardiogram using, respectively, the camera and inertial measurement unit sensor of a commercially available smartphone. After calculating PTT, SBP and DBP were estimated via the Bramwell-Hill and Moens-Korteweg equations. A calibration process was carried out 3 times for each participant to determine the input parameters of the equations. This study was conducted from March to August 2021 at Chungnam National University Sejong Hospital with 87 participants aged between 19 and 70 years who met specific conditions. The primary analysis aimed to evaluate the accuracy of the test method compared with the reference method for the entire study population. The secondary analysis was performed to confirm the stability of the test method for up to 4 weeks in 15 participants. At enrollment, gender, arm circumference, and blood pressure distribution were considered according to current guidelines.

Results

Among the 87 study participants, 45 (52%) individuals were male, and the average age was 35.6 (SD 10.4) years. Hypertension was diagnosed in 14 (16%) participants before this study. The mean test and reference SBPs were 120.0 (SD 18.8) and 118.7 (SD 20.2) mm Hg, respectively (difference: mean 1.2, SD 7.1 mm Hg). The absolute differences between the test and reference SBPs were <5, <10, and <15 mm Hg in 57.5% (150/261), 84.3% (220/261 ), and 94.6% (247/261) of measurements. The mean test and reference DBPs were 80.1 (SD 12.6) and 81.1 (SD 14.4) mm Hg, respectively (difference: mean -1.0, SD 6.0 mm Hg). The absolute differences between the test and reference DBPs were <5, <10, and <15 mm Hg in 75.5% (197/261), 93.9% (245/261), and 97.3% (254/261) of measurements, respectively. The secondary analysis showed that after 4 weeks, the differences between SBP and DBP were 0.1 (SD 8.8) and -2.4 (SD 7.6) mm Hg, respectively.

Conclusions

AlwaysBP exhibited acceptable accuracy in SBP and DBP measurement compared with the standard measurement method, according to the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization protocol criteria. However, further validation studies with a specific validation protocol designed for cuffless blood pressure measuring devices are required to assess clinical accuracy. This technology can be easily applied in everyday life and may improve the general population's awareness of hypertension, thus helping to control it.

Age Differences in Physiological Reactivity to Daily Emotional Experiences

How does physiological reactivity to emotional experiences change with age? Previous studies addressing this question have mostly been conducted in laboratory settings during which emotions are induced via pictures, films, or relived memories, raising external validity questions. In the present research, we draw upon two datasets collected using ecological momentary assessment methods (totaling 134,723 daily reports from 14,436 individuals) to examine age differences in heart rate (HR) and blood pressure (BP) reactivity to naturally occurring emotional experiences. We first examined how older and younger individuals differ in the prevalence of emotions varying in valence and arousal. On average, people reported experiencing positive emotions (high or low arousal) more than 70% of the time they were asked, and older (vs. younger) individuals tended to report positive emotions more frequently. In terms of physiological reactivity, we found that age was associated with reduced HR and BP reactivity. Some evidence was also found that the magnitude of such age differences may depend on the valence or arousal of the experienced emotion. The present findings have implications for understanding how emotions can contribute to physical health across the lifespan.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-023-00207-z.

Temporal complexity in photoplethysmography and its influence on blood pressure

Objective: The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a model-based simulation, which may help optimize the BP estimation algorithms. Methods: The classic four-element Windkessel model is adapted in this study to incorporate BP-dependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used. Results: Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions. Conclusion: Temporal complexity patterns are essential to single-site PPG-based BP estimation. Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.

Estimating Blood Pressure during Exercise with a Cuffless Sphygmomanometer

Accurately measuring blood pressure (BP) is essential for maintaining physiological health, which is commonly achieved using cuff-based sphygmomanometers. Several attempts have been made to develop cuffless sphygmomanometers. To increase their accuracy and long-term variability, machine learning methods can be applied for analyzing photoplethysmogram (PPG) signals. Here, we propose a method to estimate the BP during exercise using a cuffless device. The BP estimation process involved preprocessing signals, feature extraction, and machine learning techniques. To ensure the reliability of the signals extracted from the PPG, we employed the skewness signal quality index and the RReliefF algorithm for signal selection. Thereafter, the BP was estimated using the long short-term memory (LSTM)-based neural network. Seventeen young adult males participated in the experiments, undergoing a structured protocol composed of rest, exercise, and recovery for 20 min. Compared to the BP measured using a non-invasive voltage clamp-type continuous sphygmomanometer, that estimated by the proposed method exhibited a mean error of 0.32 ± 7.76 mmHg, which is equivalent to the accuracy of a cuff-based sphygmomanometer per regulatory standards. By enhancing patient comfort and improving healthcare outcomes, the proposed approach can revolutionize BP monitoring in various settings, including clinical, home, and sports environments.

Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure

Continuous monitoring of arterial blood pressure (BP) outside of a clinical setting is crucial for preventing and diagnosing hypertension related diseases. However, current continuous BP monitoring instruments suffer from either bulky systems or poor user-device interfacial performance, hampering their applications in continuous BP monitoring. Here, we report a thin, soft, miniaturized system (TSMS) that combines a conformal piezoelectric sensor array, an active pressure adaptation unit, a signal processing module, and an advanced machine learning method, to allow real wearable, continuous wireless monitoring of ambulatory artery BP. By optimizing the materials selection, control/sampling strategy, and system integration, the TSMS exhibits improved interfacial performance while maintaining Grade A level measurement accuracy. Initial trials on 87 volunteers and clinical tracking of two hypertension individuals prove the capability of the TSMS as a reliable BP measurement product, and its feasibility and practical usability in precise BP control and personalized diagnosis schemes development.

Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

Measurement of stress-induced sympathetic nervous activity using multi-wavelength PPG

The onset of stress triggers sympathetic arousal (SA), which causes detectable changes to physiological parameters such as heart rate, blood pressure, dilation of the pupils and sweat release. The objective quantification of SA has tremendous potential to prevent and manage psychological disorders. Photoplethysmography (PPG), a non-invasive method to measure skin blood flow changes, has been used to estimate SA indirectly. However, the impact of various wavelengths of the PPG signal has not been investigated for estimating SA. In this study, we explore the feasibility of using various statistical and nonlinear features derived from peak-to-peak (AC) values of PPG signals of different wavelengths (green, blue, infrared and red) to estimate stress-induced changes in SA and compare their performances. The impact of two physical stressors: and Hand Grip are studied on 32 healthy individuals. Linear (Mean, s.d.) and nonlinear (Katz, Petrosian, Higuchi, SampEn, TotalSampEn) features are extracted from the PPG signal's AC amplitudes to identify the onset, continuation and recovery phases of those stressors. The results show that the nonlinear features are the most promising in detecting stress-induced sympathetic activity. TotalSampEn feature was capable of detecting stress-induced changes in SA for all wavelengths, whereas other features (Petrosian, AvgSampEn) are significant (AUC ≥ 0.8) only for IR and Red wavelengths. The outcomes of this study can be used to make device design decisions as well as develop stress detection algorithms.