Search Results (317)

Reliable analysis of remote photoplethysmography (rPPG) signals depends on identifying physiologically plausible pulses. Traditional approaches rely on clustering self-similar pulses, which can discard valid variability. Automating pulse quality assessment could capture the true underlying morphology while preserving physiological variability. In this manuscript, individual rPPG pulses were manually labelled as plausible, borderline and implausible and used to train multilayer perceptron classifiers. Two independent datasets were used to ensure strict separation between training and test data: the Vision-MD dataset (4036 facial videos from 1270 participants) and a clinical laboratory dataset (235 videos from 58 participants). Vision-MD data were used for model development with an 80/20 training–validation split and 5-fold cross-validation, while the clinical dataset served exclusively as an independent test set. A three-class model was evaluated achieving $F_1$-scores of 0.92, 0.24 and 0.79 respectively. Recall was highest for plausible and implausible pulses but lower for borderline pulses. To test separability, three pairwise binary classifiers were trained, with ROC-AUC > 0.89 for all three category pairs. When combining borderline and implausible pulses into a single class, the binary classifier achieved an $F_1$-score of 0.93 for the plausible category. Finally, usability analysis showed that automated labelling identified more usable pulses per signal than the previously used agglomerative clustering method, while preserving physiological variability. Full article

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with ischemic heart disease (IHD) being the most prevalent and deadly subtype. The growing burden of IHD underscores the urgent need for effective early detection methods that are scalable and non-invasive. Heart Rate Variability (HRV), a non-invasive physiological marker influenced by the autonomic nervous system (ANS), has shown clinical relevance in predicting adverse cardiac events. This study presents a photoplethysmography (PPG)-based Zhurek IoT device, a custom-developed Internet of Things (IoT) device for non-invasive HRV monitoring. The platform’s effectiveness was evaluated using HRV metrics from electrocardiography (ECG) and PPG signals, with machine learning (ML) models applied to the task of early IHD risk detection. ML classifiers were trained on HRV features, and the Random Forest (RF) model achieved the highest classification accuracy of 90.82%, precision of 92.11%, and recall of 91.00% when tested on real data. The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.98, reaching a sensitivity of 88% and specificity of 100% at its optimal threshold. The preliminary results suggest that data collected with the “Zhurek” IoT devices are promising for the further development of ML models for IHD risk detection. This study aimed to address the limitations of previous work, such as small datasets and a lack of validation, by utilizing real and synthetically augmented data (conditional tabular GAN (CTGAN)), as well as multi-sensor input (ECG and PPG). The findings of this pilot study can serve as a starting point for developing scalable, remote, and cost-effective screening systems. The further integration of wearable devices and intelligent algorithms is a promising direction for improving routine monitoring and advancing preventative cardiology. Full article

Cardiac arrhythmia is a leading cause of sudden cardiac death. Its early detection and continuous monitoring hold significant clinical value. Photoplethysmography (PPG) signals, owing to their non-invasive nature, low cost, and convenience, have become a vital information source for monitoring cardiac activity and vascular health. However, the inherent non-stationarity of PPG signals and significant inter-individual variations pose a major challenge in developing highly accurate and efficient arrhythmia classification methods. To address this challenge, we propose a Fusion Deep Multi-domain Attention Network (Fusion-DMA-Net). Within this framework, we innovatively introduce a cross-scale residual attention structure to comprehensively capture discriminative features in both the time and frequency domains. Additionally, to exploit complementary information embedded in PPG signals across these domains, we develop a fusion strategy integrating interactive attention, self-attention, and gating mechanisms. The proposed Fusion-DMA-Net model is evaluated for classifying four major types of cardiac arrhythmias. Experimental results demonstrate its outstanding classification performance, achieving an overall accuracy of 99.05%, precision of 99.06%, and an F1-score of 99.04%. These results demonstrate the feasibility of the Fusion-DMA-Net model in classifying four types of cardiac arrhythmias using single-channel PPG signals, thereby contributing to the early diagnosis and treatment of cardiovascular diseases and supporting the development of future wearable health technologies. Full article

Continuous blood pressure (BP) monitoring is essential for the early detection and prevention of cardiovascular diseases like hypertension. Recently, interest in continuous BP estimation systems and algorithms has grown. Various physiological signals reflect BP variations from different perspectives, and combining multiple signals can enhance the accuracy of BP measurements. However, research integrating electrocardiogram (ECG), photoplethysmography (PPG), and impedance cardiography (ICG) signals for BP monitoring remains limited, with related technologies still in early development. A major challenge is the increased system complexity associated with acquiring multiple signals simultaneously, along with the difficulty of efficiently extracting and integrating key features for accurate BP estimation. To address this, we developed a BP monitoring system that can synchronously acquire and process ECG, PPG, and ICG signals. Optimizing the circuit design allowed ECG and ICG modules to share electrodes, reducing components and improving compactness. Using this system, we collected 400 min of signals from 40 healthy subjects, yielding 4390 records. Experiments were conducted to evaluate the system’s performance in BP estimation. The results demonstrated that combining pulse wave analysis features with the XGBoost model yielded the most accurate BP predictions. Specifically, the mean absolute error for systolic blood pressure was 3.76 ± 3.98 mmHg, and for diastolic blood pressure, it was 2.71 ± 2.57 mmHg, both of which achieved grade A performance under the BHS standard. These results are comparable to or better than existing studies based on multi-signal methods. These findings suggest that the proposed system offers an efficient and practical solution for BP monitoring. Full article

This study investigates the effects of heart rate and arm tremor on performance in elite archery, using non-contact physiological monitoring from real Olympic competition footage. A total of 50 video segments were extracted from publicly available international broadcasts, comprising athletes of various backgrounds. From these, heart rate signals were estimated via remote photoplethysmography (rPPG) from facial regions, and micro-movements were quantified from right and left arm regions using feature point tracking. Ordinal logistic regression was employed to evaluate the relationship between biometric variables and archery scores (10, 9, ≤8 points). Results showed that elevated heart rate ($\beta$ = −0.1166; $p<$ 0.001) and greater right-arm movement ($\beta$ = −6.1747; p = 0.008) were significantly associated with lower scores. Athletes scoring 10 points exhibited significantly lower heart rate ($p<$ 0.001) and reduced right-arm tremor (p = 0.010) compared to others. These findings support the hypothesis that physiological arousal and biomechanical instability impair performance, and they further demonstrate the feasibility of contactless monitoring in real competition environments. The proposed method enables objective, in-game performance evaluation and supports the development of personalized training systems for precision sports. Full article

(1) Background. Although treatment with enhanced external counterpulsation (EECP) in patients with ischemic chronic heart failure (CHF) is pathophysiologically justified, its long-term vascular effects remain insufficiently defined. We aimed to study the vascular effects of long-term complex treatment (36 months) including EECP in patients with ischemic CHF, and to examine the relationship between these effects and clinical outcomes. (2) Methods. A total amount of 120 patients with ischemic CHF were randomized to receive one course of EECP per year (35 h; Group 1), two courses of EECP per year (70 h; Group 2), or one course of placebo-counterpulsation per year (35 h; Group 0;). For a period of 36 months, all patients underwent annual assessments including transthoracic echocardiography, nailfold videocapillaroscopy, finger photoplethysmography, applanation tonometry, exercise tolerance testing, and clinical outcome monitoring. (3) Results. Compared to the placebo group, long-term EECP treatment in patients with ischemic CHF, was accompanied by a significantly greater increase in exercise tolerance (∆23.5–45.0% vs. 7.0%; p < 0.001) and improvements in left ventricular ejection fraction (∆9.9–19.6% vs. 5.6%; p < 0.001) and myocardial stress (decrease in NT-proBNP level ∆−80.4–−82.4% vs. −75.8%; p < 0.001), as well as both functional and structural vascular parameters (p < 0.001). The effect size depended on the annual number of EECP courses. The highest event-free survival was found in Group 2. At 36 months, improvement of vascular parameters emerged as stronger predictors of reduced cardiovascular event risk compared to the 12-month. (4) Conclusions. Long-term EECP treatment of patients with ischemic CHF improves both functional and structural vascular parameters, with an increasing role of their improvement in reducing the risk of cardiovascular events after 36 months. Full article

Optical brain monitoring techniques, including near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and photoplethysmography (PPG) have gained attention for their non-invasive, affordable, and portable nature. These methods offer real-time insights into cerebral parameters like cerebral blood flow (CBF), intracranial pressure (ICP), and oxygenation. However, confounding factors like extracerebral layers, skin pigmentation, skull thickness, and brain-related pathologies may affect measurement accuracy. This review examines the potential impact of confounders, focusing on in vitro studies that use phantoms to simulate human head properties under controlled conditions. A systematic search identified six studies on extracerebral layers, two on skin pigmentation, two on skull thickness, and four on brain pathologies. While variation in phantom designs and optical devices limits comparability, findings suggest that the extracerebral layer and skull thickness influence measurement accuracy, and skin pigmentation introduces bias. Pathologies like oedema and haematomas affect the optical signal, though their influence on parameter estimation remains inconclusive. This review highlights limitations in current research and identifies areas for future investigation, including the need for improved brain phantoms capable of simulating pulsatile signals to assess the impact of confounders on PPG systems, given the growing interest in PPG-based cerebral monitoring. Addressing these challenges will improve the reliability of optical monitoring technologies. Full article

Pulsatile signal detection plays an important role in monitoring various physiological parameters, primarily heart rate and blood oxygen saturation. Their applications range from clinical settings to personal health and wellness monitoring. PPG (photoplethysmography) can provide non-invasive optical measurements to detect blood volume changes in peripheral tissues. Yet, it suffers from low spatial resolution to precisely detect the pulsatile signal originating over 2 mm in human tissue. Ultrasound (US) provides a deep detectable range compared to the pure optical method. However, its low contrast to red blood cells and cluster artifacts makes it only detect the indirect pulsation from the surrounding tissue of blood vessels. Recent advances in PA imaging show its capability to precisely measure pulsatile signals originating from blood vessels in deep regions (over 10 mm) and its potential to accurately record blood oxygen saturation with high spatial and temporal resolution. This review article summarizes studies on photoacoustic (PA) pulsatile signal monitoring, highlights the technical advances, and compares it against optical and ultrasonic approaches. Full article

Objectives: Virtual reality (VR) has emerged as a promising tool for assessing anxiety-related disorders through immersive exposure and physiological monitoring. This study aimed to evaluate whether multimodal data, including heart rate variability (HRV), skin conductance response (SCR), and self-reported anxiety, collected during VR exposure could classify patients with panic disorder and agoraphobia using machine learning models. Methods: Seventy-six participants (38 patients with panic disorder and agoraphobia, 38 healthy controls) completed 295 total VR exposure sessions. Each session involved two road and two supermarket scenarios designed to induce anxiety. Inside the sessions, self-reported anxiety was measured along with physiological signals recorded by photoplethysmography and SCR sensors. HRV measures of heart rate, standard deviation of normal-to-normal intervals, and low-frequency to high-frequency ratio were extracted along with SCR peak frequency and average amplitude. These features were analyzed using Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (k-NN), Logistic Ridge Regression (LRR), C-Support Vector Machine (SVC), Random Forest (RF), and Stochastic Gradient Boosting (SGB) classifiers. Results: The best model achieved an accuracy of 0.83. Most models showed specificity and precision ≥0.80, while sensitivity varied across models, with several reaching ≥0.82. Performance was stable across major hyperparameters, VR-stimulus settings, and medication status. The patients reported higher subjective anxiety but exhibited blunted physiological responses, particularly in SCR amplitude. Self-reported anxiety demonstrated higher feature importance scores compared to other physiological properties. Conclusion: VR exposure with self-reported anxiety and physiological measures may serve as a feasible diagnostic aid for panic disorder and agoraphobia. Further refinement is needed to improve sensitivity and clinical applicability. Full article

Remote photoplethysmography (rPPG) offers a promising solution for non-contact driver monitoring by detecting subtle blood flow-induced facial color changes from video. However, motion artifacts in dynamic driving environments remain key challenges. This study presents an rPPG framework that combines signal processing techniques before and after applying Eulerian Video Magnification (EVM) for pulse rate (PR) estimation in driving simulators. While not novel, the approach offers insights into the efficiency of the EVM method and its time complexity. We compare results of the proposed rPPG approach against reference Empatica E4 data and also compare it with existing achievements from the literature. Additionally, the possible bias of the Empatica E4 is further assessed using an independent dataset with both the Empatica E4 and the Faros 360 measurements. EVM slightly improves PR estimation, reducing the mean absolute error (MAE) from 6.48 bpm to 5.04 bpm (the lowest MAE (~2 bpm) was achieved under strict conditions) with an additional time required for EVM of about 20 s for 30 s sequence. Furthermore, statistically significant differences are identified between younger and older drivers in both reference and rPPG data. Our findings demonstrate the feasibility of using rPPG-based PR monitoring, encouraging further research in driving simulations. Full article

For an intuitive understanding of traditional remote photoplethysmography (rPPG), this study categorizes existing algorithms into two main types: spatial vector projection and spatial angle projection in RGB color space. The RGB variation induced by noise (RGB_noise) is visualized in color space and approximated by the raw RGB signal. We propose APON (Adaptive Projection plane Orthogonal to Noise) to suppress artifacts. Two rPPG methods, APON_Vector and APON_Angle, are then developed from this adaptive plane. Comparative experiments on the public databases show that APON_Vector is comparable to state-of-the-art methods like CHROM and POS (achieving an Accuracy of 87.35%), while APON_Angle outperforms other angle projection methods (reducing MAE to 0.78 bpm). The results show that the simple yet effective APON contains significant pulse variation and holds potential for more pulse detection methods. Full article

Background: Respiratory rate (RR) is a key vital sign and one of the most sensitive indicators of physiological conditions, playing a crucial role in the early identification of clinical deterioration. The monitoring of RR using electrocardiography (ECG) and photoplethysmography (PPG) aims to overcome limitations of traditional methods in clinical settings. Methods: The proposed approach extracts RR from ECG and PPG signals using different morphological and temporal features from publicly available datasets (iAMwell and Capnobase). The algorithm was used to develop and test with a selection of relevant ECG (e.g., R-peak, QRS area, and QRS slope) and PPG (amplitude and frequency modulation) characteristics. Results: The results show promising performance, with the ECG-derived signal using the R-peak–based method yielding the lowest error, with a mean absolute error of 0.99 breaths/min in the iAMwell dataset and 3.07 breaths/min in the Capnobase dataset. In comparison, the RR PPG-derived signal showed higher errors of 5.10 breaths/min in the iAMwell dataset and 10.66 breaths/min in the Capnobase dataset, for the FM and AM method, respectively. Bland–Altman analysis revealed a small negative bias, approximately −0.97 breaths/min for the iAMwell dataset (with limits of agreement from −2.62 to 0.95) and −1.16 breaths/min for the Capnobase dataset (limits of agreement from −3.37 to 1.10) in the intra-subject analysis. In the inter-subject analysis, the bias was −0.84 breaths/min (limits of agreement from −1.76 to 0.20) for iAMwell and −1.22 breaths/min (limits of agreement from −7.91 to 5.35) for Capnobase, indicating a slight underestimation. Conversely, the PPG-derived signal tended to overestimate RR, resulting in higher variability and reduced accuracy. These findings highlight the higher reliability of ECG-derived features for RR estimation in the analyzed datasets. Conclusion: This study suggests that the proposed approach could guide the design of cost-effective, non-invasive methods for continuous respiration monitoring, offering a reliable tool for detecting conditions like stress, anxiety, and sleep disorders. Full article

We aimed to evaluate the non-invasive photoplethysmography waveform as a means to predict mean arterial pressure using artificial intelligence models. This was performed using datasets captured in large animal hemorrhage and resuscitation studies. An initial deep learning model trained using a subset of large animal data and was then evaluated for real-time blood pressure prediction. With the successful proof-of-concept experiment, we further tested different feature extraction approaches as well as different machine learning and deep learning methodologies to examine how various combinations of these methods can improve the accuracy of mean arterial pressure predictions from a non-invasive photoplethysmography sensor. Different combinations of feature extraction and artificial intelligence models successfully predicted mean arterial pressure throughout the study. Overall, manual feature extraction fed into a long short-term memory network tracked the mean arterial pressure through hemorrhage and resuscitation with the highest accuracy. Full article

Subway train operators face the risk of cumulative cognitive stress due to factors such as visual fatigue from prolonged high-speed tunnel driving, irregular shift patterns, and the monotony of automated operations. This can lead to cognitive decline and human error accidents. Current monitoring of cognitive stress risk predominantly relies on single-modal methods, which are susceptible to environmental interference and offer limited accuracy. This study proposes an intelligent multimodal framework for cognitive stress monitoring by leveraging the symmetry principles in physiological and behavioral manifestations. The symmetry of photoplethysmography (PPG) waveforms and the bilateral symmetry of head movements serve as critical indicators reflecting autonomic nervous system homeostasis and cognitive load. By integrating these symmetry-based features, this study constructs a spatiotemporal dynamic feature set through fusing physiological signals such as PPG and galvanic skin response (GSR) with head and facial behavioral features. Furthermore, leveraging deep learning techniques, a hybrid PSO-CNN-GRU-Attention model is developed. Within this model, the Particle Swarm Optimization (PSO) algorithm dynamically adjusts hyperparameters, and an attention mechanism is introduced to weight multimodal features, enabling precise assessment of cognitive stress states. Experiments were conducted using a full-scale subway driving simulator, collecting data from 50 operators to validate the model’s feasibility. Results demonstrate that the complementary nature of multimodal physiological signals and behavioral features effectively overcomes the limitations of single-modal data, yielding significantly superior model performance. The PSO-CNN-GRU-Attention model achieved a predictive coefficient of determination (R²) of 0.89029 and a mean squared error (MSE) of 0.00461, outperforming the traditional BiLSTM model by approximately 22%. This research provides a high-accuracy, non-invasive solution for detecting cognitive stress in subway operators, offering a scientific basis for occupational health management and the formulation of safe driving intervention strategies. 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