Search Results (26)

Physiological signal emulators support the calibration, validation, and stress testing of cardiovascular monitoring devices. However, many existing systems generate electrocardiography (ECG) or photoplethysmography (PPG) independently and offer limited control over arrhythmia detection and ECG–PPG coupling. This study presents a programmable physiological signal emulator that integrates a unified event-driven ECG–PPG model with synchronized multichannel hardware. The model represents atrial pacing, atrioventricular conduction, and ventricular activation as separate functional blocks, enabling normal sinus rhythm, first-degree atrioventricular block, second-degree atrioventricular block Mobitz I, complete atrioventricular block, atrial tachycardia, and ventricular tachycardia. A Gaussian-based ECG is generated from the atrial and ventricular event sequences, while a multi-Gaussian PPG waveform is derived from ventricular activation using a beat-class-dependent electromechanical delay. The same processing architecture supports playback of recorded 12-lead clinical ECG data through an inverse lead transformation. The hardware uses an STM32F407VET6 microcontroller and MCP4921 digital-to-analog converters (DACs) to generate 10 synchronized analog outputs, comprising 09 ECG electrodes and 01 PPG channel. Validation covered physiological timing, analog-chain performance, and end-to-end signal reproduction. PR interval errors relative to a commercial electrocardiograph were 1.23 ms for normal sinus rhythm and 1.66 ms for first-degree atrioventricular block. The measured beat-to-beat PR increment during Mobitz I conduction was $40.02\pm 0.04$ ms for a programmed value of 40 ms. At commanded amplitudes of at least 800 mV, both output channels achieved absolute amplitude errors below 0.60%, total harmonic distortion below 1%, and signal-to-noise ratios (SNRs) above 30 dB. Inter-channel R-peak skew remained below the 2 ms sampling interval, and all monitored metrics varied by less than 1.5% during 60 min of continuous operation. Reproduction of a clinical 12-lead recording yielded per-lead $R^2$ values of 0.967–0.986 and a cycle-to-cycle correlation of 0.996. The emulator also reproduced amplitude-dependent bias in automated interval measurements and interpretation labels. These results demonstrate a low-cost, open-source platform for reproducible device calibration, algorithm stress testing, medical training, and physiological signal processing research. Full article

This study evaluates the feasibility of morphological feature-based contactless blood pressure (BP) estimation using remote photoplethysmography (rPPG). A total of 52 morphological features are extracted from each cardiac cycle of rPPG and its first and second derivatives and arranges as temporal sequences for a long short-term memory (LSTM)-based model. In a calibration-based evaluation on a 16-subject public dataset, the 52-feature model achieved mean absolute errors (MAEs) of 11.45 mmHg for systolic BP (SBP) and 8.15 mmHg for diastolic BP (DBP), outperforming direct rPPG waveform input under the same training protocol. Model comparisons also showed that estimation performance varied with the architecture and model capacity. To identify an effective feature representation, recursive feature elimination (RFE) and principal component analysis (PCA) were compared across multiple dimensions. At 24 dimensions, PCA reduced MAE by 1.59 mmHg for SBP and 1.09 mmHg for DBP relative to RFE, while the 15-dimensional PCA representation was selected as the final compact representation considering both performance and feature dimension. The proposed approach was further evaluated against measurement-free and measurement-based reference baselines, while leave-one-subject-out (LOSO) validation was used to assess subject-independent generalization. LOSO showed substantial performance variation across held-out subjects, indicating limited generalizability to unseen subjects. Despite this limitation, the overall results support the feasibility of compact morphological rPPG feature representations for contactless BP estimation and highlight the importance of appropriate feature-space and model selection. Full article

Non-contact blood pressure (BP) estimation from remote photoplethysmography (rPPG) can yield unstable window-level predictions. This study combines a pulse-feature long short-term memory (LSTM) estimator with temporal variance-based prediction filtering. Fifty-two features from the rPPG waveform and its first and second derivatives were arranged into sliding-window sequences for systolic BP (SBP) and diastolic BP (DBP) estimation. The filter selects locally stable predictions from temporally ordered prediction sequences without using ground-truth BP values. Performance was first assessed in 10 repeated subject-dependent randomized-block experiments. Filtering reduced the SBP mean absolute error (MAE) from $7.04\pm 1.52$ to $5.34\pm 1.62$ mmHg and the DBP MAE from $4.63\pm 1.32$ to $3.07\pm 0.97$ mmHg while retaining $76.39\pm 7.59\%$ of predictions. A subject-specific mean predictor was also competitive under the subject-dependent setting and achieved lower DBP MAE at full coverage than the filtered LSTM. Chronological rolling-origin and leave-one-subject-out (LOSO) evaluations further showed that the improvement persisted for later intervals from the same subjects but not for unseen subjects. Because the filter operates only on model outputs, it requires neither additional trainable parameters nor model retraining. These findings show a low-overhead accuracy–coverage trade-off for within-subject rPPG-based BP estimation, while subject-independent generalization remains limited. Full article

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality in the elderly worldwide. Over the past two decades, there has been a wealth of evidence of a close relationship between autonomic nervous system activity and cardiovascular mortality, including sudden cardiac death. Heart rate variability (HRV), derived from photoplethysmographic (PPG) signals, is increasingly recognized as a promising non-invasive digital marker for evaluating autonomic nervous system function and stratifying CVD risk. The application of machine learning algorithms to PPG-derived HRV analysis offers a promising approach for improving CVD risk stratification and facilitating the development of personalized medicine strategies. Background/Objectives: To evaluate the potential of heart rate variability indicators in predicting the risk of developing CVD in individuals aged 65 years and older. Methods: The study involved individuals aged 65 years and older, divided into two groups: those with a risk of developing CVD (n = 54) and those without risk (n = 46). The first stage included a questionnaire as well as anthropometric and hemodynamic measurements. At the second stage, a PPG was performed using the Eldar computer photoplethysmograph and Eldar-Vario software, followed by an analysis of time-domain and spectral HRV parameters. Statistical data analysis was conducted using the SPSS Statistics 22.0 software package, focusing on the evaluation of associations between HRV indicators and the presence of CVD. Interpretable machine learning models were developed using logistic regression and a random forest algorithm within a nested cross-validation framework. In addition to the discriminatory characteristics, Brier score, LogLoss, calibration analysis, error matrices, permutation importance, and SHAP interpretation were analyzed in the study. Results: In patients with cardiovascular diseases, a statistically significant decrease in heart rate variability was revealed: SDNN by 2 times (26 [Q1–Q3: 15, 35] ms), pNN50 by 3.5 times (4 [3, 5]%), TINN by 5 times (31 [20, 51] ms), and HRV by 2.5 times (6 [4, 8.7]). In addition, a decrease was seen in the spectral components of VLF by one-fold (2450 [Q1–Q3: 2450, 4500] ms²), LF by four-fold (750 [750, 1500] ms²) and HF by five-fold (450 [450, 750] ms²) (p < 0.05). At the same time, there was a significant increase in the VLF/HF and LF/HF ratios, which indicates a predominance of sympathetic activity. According to the results of the correlation analysis, statistically significant associations of HRV indicators with age, physical activity level, body mass index and systolic blood pressure were revealed. The results of machine learning also revealed the association of HRV with arterial hypertension, physical activity and BMI. The best final results were demonstrated by a random forest model with a combined set of clinical and HRV signs of HF and RMSSD (ROC-AUC was 0.9988). The signs of heart rate variability obtained by photoplethysmography demonstrated additional prognostic value in relation to clinical signs. PPG-derived HRV features demonstrated additional discriminatory value for cardiovascular risk stratification. Conclusions: The obtained data demonstrate a close association between the risk of developing cardiovascular disease and autonomic nervous system dysfunction. The decrease in heart rate variability is most pronounced in elderly individuals with existing cardiovascular disease and can be considered a potential tool for developing diagnostic, prognostic, and risk stratification strategies. The use of machine learning demonstrated that heart rate variability features obtained using photoplethysmography improve diagnostic prognostication and classification of cardiovascular diseases compared to models based solely on clinical data. Full article

This paper presents the design, implementation, and controlled evaluation of a proof-of-concept ear-level wearable system that integrates local artificial intelligence for real-time physiological monitoring in construction safety applications. The proposed architecture combines photoplethysmography (PPG), non-contact infrared thermometry, and nine-axis inertial sensing on a Raspberry Pi Pico microcontroller, enabling local inference that reduces dependence on cloud processing. A lightweight logistic regression model with three binary outputs, trained on a subset of the publicly available WESAD dataset (subjects S2–S4), classifies three physiological states relevant to worker safety—elevated PPG variability, drowsiness, and fatigue—directly from the device’s 2 MB flash memory. The principal contribution is demonstrating that ear-level multi-sensor fusion combined with on-device machine learning achieves high agreement with clustering-derived proxy labels under controlled conditions (average F1-score: 97.80% on an unseen test subject) while sustaining sub-second inference latency (<0.5 s). These results support timely supervisor alerting and motivate subsequent field validation in operational construction environments. Full article

Arrhythmia is a common and potentially life-threatening cardiovascular condition. Photoplethysmography (PPG) has emerged as a noninvasive alternative to electrocardiography for cardiac rhythm monitoring, yet most PPG-based methods remain limited to binary classification. In this study, a new deep learning approach is suggested for categorizing six arrhythmia types from PPG data: sinus rhythm (SR), premature ventricular contraction (PVC), premature atrial contraction (PAC), ventricular tachycardia (VT), supraventricular tachycardia (SVT), and atrial fibrillation (AF). The raw PPG signal is enhanced by extracting its first and second derivatives to capture morphological features not readily apparent in the original signal. A hybrid architecture, VGG-BiLSTM, is utilized, merging VGG convolutional layers for spatial features extraction with bidirectional long short-term memory layers for modeling temporal dependencies. A stratified data splitting strategy is further adopted to address class imbalance across arrhythmia types. A publicly available dataset containing 46,827 PPG segments from 91 individuals was employed to assess the effectiveness of the suggested technique. The method yielded an overall accuracy, sensitivity, specificity and F1 score of 88.7%, 78.5%, 97.6% and 80.5% correspondingly. Full article

The development of reliable smartphone-based methods for vascular assessment is limited by the scarcity of large-scale, high-quality, real-world photoplethysmography (PPG) datasets. This work introduces two openly reusable smartphone camera-based PPG datasets curated from over one million unconstrained recordings, designed to support vascular morphology analysis and vascular aging research. The first dataset comprises approximately 5000 high-fidelity PPG heartbeat templates labeled into four morphological classes based on dicrotic notch characteristics, enabling assessment of arterial waveform structure beyond chronological age. The second dataset contains about 10,000 demographically balanced PPG samples curated for chronological age regression using rigorous subject-level balancing and correlation-based quality control. A standardized processing pipeline is presented, including beat alignment, ensemble averaging, and objective signal acceptance criteria to ensure morphological stability. To validate dataset utility, multiple machine learning models were benchmarked using raw signals, second derivatives, and compact Gaussian representations, achieving classification accuracy up to 90.08% and age prediction error below 10 years. By prioritizing real-world data quality, transparency, and reuse, this work provides a robust foundation for scalable, interpretable, and reproducible research in smartphone-based vascular assessment. Full article

Continuous blood pressure (BP) monitoring remains a major challenge in wearable healthcare systems, as conventional cuff-based sphygmomanometers are intermittent and unsuitable for long-term use. This study presents a Smart Sock platform for cuffless BP estimation using single-site photoplethysmography (PPG). Unlike approaches based on pulse transit time or fiducial point detection, the proposed framework relies on peak-independent features extracted from PPG and its first and second derivatives, capturing blood volume and hemodynamic dynamics in the lower limb. PPG signals from 60 participants were segmented into overlapping 30 s windows and processed through a unified preprocessing pipeline. A compact set of physiologically meaningful statistical and information-theoretic features was extracted from each window, and temporal lag modelling (5–15 s) was employed to encode short-term hemodynamic memory without explicit peak detection. Multiple regression models were assessed using leakage-safe cross-validation strategies. In a subject-independent diagnosis scenario, the system achieved errors of 8.60 mmHg for systolic BP and 6.42 mmHg for diastolic BP. In a monitoring scenario with single-point calibration, performance substantially improved, yielding mean absolute errors of 1.3–1.7 mmHg and R² > 0.90. These results demonstrate that foot-based PPG, combined with peak-independent feature engineering and temporal context modeling, enables accurate and comfortable continuous personalized blood pressure monitoring after calibration, while subject-independent estimation remains more challenging. Full article

The second derivative of the photoplethysmographic signal presents five relevant points that provide information about the structural properties of arteries. This study investigates the ratio between the amplitude of the d wave (end of systole) and the a wave (beginning of systole) as a potential indicator of vascular aging. The research combines an in vitro study on silicone models with different stiffness and an in vivo study on volunteers aged between 26 and 63 years. The results show a strong negative correlation between the d/a ratio and arterial stiffness, confirming the potential of this parameter as a noninvasive index for assessing vascular health status. Full article

This study presents an algorithm for non-invasive blood pressure (BP) estimation in neonates using photoplethysmography (PPG), suitable for resource-constrained neonatal telecare platforms. Using the Windkessel model, the algorithm processes PPG signals from a MAX 30102 sensor, (Analog Devices (formerly Maxim Integrated), based in San Jose, CA, USA) filtering motion noise and extracting cardiac cycle time and systolic time (ST). These parameters inform a derived blood flow signal, the input for the Windkessel model. Calibration utilizes average parameters based on the newborn’s post-conceptional age, weight, and gestational age. Performance was validated against readings from a standard non-invasive BP cuff at SES Hospital Universitario de Caldas. Two parameter estimation methods were evaluated. The first yielded root mean square errors (RMSEs) of 24.14 mmHg for systolic and 19.13 mmHg for diastolic BP. The second method significantly improved accuracy, achieving RMSEs of 2.31 mmHg and 5.13 mmHg, respectively. The successful adaptation of the Windkessel model to single PPG signals allows for BP calculation alongside other physiological variables within the telecare program. A device analysis was conducted to determine the appropriate device based on computational capacity, availability of programming tools, and ease of integration within an Internet of Things environment. This study paves the way for future research that focuses on parameter variations due to cardiovascular changes in newborns during their first month of life. Full article

Asymptomatic peripheral artery disease (PAD) poses a silent risk, potentially leading to severe conditions if undetected. Integrating new screening tools into routine general practitioner (GP) visits could enable early detection. This study investigates the feasibility of photoplethysmography (PPG) monitoring for assessing vascular health across different blood pressure (BP) conditions. Custom femoral artery phantoms representing healthy (0.82 MPa), intermediate (1.48 MPa), and atherosclerotic (2.06 MPa) vessels were tested under hypertensive, normotensive, and hypotensive conditions to evaluate PPG’s ability to distinguish between vascular states. Extracted features from the PPG signal, including amplitude, area under the curve (AUC), median upslope–downslope ratio, and median end datum difference, were analysed. Kruskal–Wallis tests revealed significant differences between healthy and unhealthy vessels across BP states, supporting PPG as a screening tool. The fiducial points from the second derivative of the photoplethysmography signal (SDPPG) were analysed. The ${\displaystyle \frac{b}{a}}$ ratio was most pronounced between healthy and unhealthy phantoms under hypertensive conditions (ranging from –2.13 to –2.06), suggesting a change in vascular wall distensibility. Under normotensive conditions, the difference in ${\displaystyle \frac{b}{a}}$ ratios between healthy and unhealthy phantoms was smaller (0.01), and no meaningful difference was observed under hypotensive conditions, suggesting the reduced sensitivity of this metric at lower perfusion pressures. Intermediate states were challenging to detect, particularly under hypotension, suggesting a need for further research. Nonetheless, this study highlights the promise of PPG monitoring in identifying vascular stiffness. Full article

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. Full article

The paper describes a method developed for the indirect cuffless estimation of arterial blood pressure (ABP) from two/three-channel photoplethysmography (PPG) signals. It is important when the actual ABPs cannot be measured, e.g., during scanning inside a magnetic resonance imager. The proposed procedure uses heart pulse transmission parameters (HPTPs) extracted from the second derivative PPG signals. The linear regression method was used to calculate the relation between the determined HPTPs and the ABPs measured in parallel by a blood pressure monitor. The ABP values were estimated by the inverse conversion characteristic calculated from these linear relations. Three auxiliary investigations were performed first to find appropriate settings for PPG signal processing. We tested the accuracy of ABP estimation using two small corpora of multi-channel PPG records sensed during our previous experiments. We also analyzed the distribution of the determined HPTP values depending on the hand and gender for the mapping of a mutual relationship of HPTPs and measured ABPs. The final estimation errors were evaluated graphically (by correlation scatter plots and Bland–Altman plots) and numerically (by a correlation coefficient between the measured and estimated ABPs and by enumeration of the relative estimation error). The obtained results achieve acceptable mean values of −2.6/−3.5 mm Hg for systolic/diastolic ABPs. Full article

The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals. Full article

The paper describes the development, testing, and verification of practical usability of the indirect cuffless method for estimation of arterial blood pressure (ABP) values from the photo-plethysmography (PPG) signal sensed by the optical PPG sensor. The proposed procedure uses time domain features (systolic/diastolic pulse time ratios and partial areas around the pulses) extracted from the second derivative of the PPG signal. The linear regression method is next used to calculate the relation between the determined PPG wave features and the blood pressure values measured in parallel using a blood pressure monitor. ABP values are finally estimated by the inverse conversion characteristic calculated from these linear relations. Summary estimation errors obtained from first-step experiments achieve acceptable values of about 8/3% for systolic/diastolic ABPs. However, further improvements are necessary before usage of the proposed procedure. Full article