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17 pages, 931 KiB  
Article
How to Improve the Repeatability, Reproducibility and Accuracy in the Dynamic Structuration of Water by Electromagnetic Waves?
by Marie-Valérie Moreno, Sid Ahmed Ben Mansour and Frédéric Roscop
Biophysica 2025, 5(3), 29; https://doi.org/10.3390/biophysica5030029 - 21 Jul 2025
Viewed by 201
Abstract
This study represents a first step toward improving the repeatability, reproducibility, and accuracy of a process designed to enhance dynamic water structuring. We aim is to investigate the optical reflectivity of a watery magnesium chloride solution treated with electromagnetic waves, we employ a [...] Read more.
This study represents a first step toward improving the repeatability, reproducibility, and accuracy of a process designed to enhance dynamic water structuring. We aim is to investigate the optical reflectivity of a watery magnesium chloride solution treated with electromagnetic waves, we employ a novel methodology derived from human plethysmography (PPG) with three wavelengths spanning the visible and infrared spectra. We measured the reflectance of 17 flasks at 536 nm, 660 nm, and 940 nm before and after treatment, first using the succussion method (control) and second using a 50 Hz signal. The observed variability was acceptable, with repeatability errors below 0.15% and reproducibility errors below 3.5% across all wavelengths before and after treatment. Out of 51 samples dynamically structured using the succussion method, we obtained two false negatives, while one false negative was recorded out of 51 samples dynamically structured using the electromagnetic (EM) method. PPG appears to be a relevant sensor, as it correctly detected dynamically structured water in 99 out of 102 cases, using either the succussion or electromagnetic method. Our results show significant differences in reflectance (supposedly correlated with water’s structured status) at 536 nm between dynamically structured and dynamic non-structured samples (p < 0.001). Future improvements will include a validation protocol against gold-standard spectrophotometry with a larger sample size. Full article
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34 pages, 3135 KiB  
Article
Effects of Transcutaneous Electroacupuncture Stimulation (TEAS) on Eyeblink, EEG, and Heart Rate Variability (HRV): A Non-Parametric Statistical Study Investigating the Potential of TEAS to Modulate Physiological Markers
by David Mayor, Tony Steffert, Paul Steinfath, Tim Watson, Neil Spencer and Duncan Banks
Sensors 2025, 25(14), 4468; https://doi.org/10.3390/s25144468 - 18 Jul 2025
Viewed by 513
Abstract
This study investigates the effects of transcutaneous electroacupuncture stimulation (TEAS) on eyeblink rate, EEG, and heart rate variability (HRV), emphasising whether eyeblink data—often dismissed as artefacts—can serve as useful physiological markers. Sixty-six participants underwent four TEAS sessions with different stimulation frequencies (2.5, 10, [...] Read more.
This study investigates the effects of transcutaneous electroacupuncture stimulation (TEAS) on eyeblink rate, EEG, and heart rate variability (HRV), emphasising whether eyeblink data—often dismissed as artefacts—can serve as useful physiological markers. Sixty-six participants underwent four TEAS sessions with different stimulation frequencies (2.5, 10, 80, and 160 pps, with 160 pps as a low-amplitude sham). EEG, ECG, PPG, and respiration data were recorded before, during, and after stimulation. Using non-parametric statistical analyses, including Friedman’s test, Wilcoxon, Conover–Iman, and bootstrapping, the study found significant changes across eyeblink, EEG, and HRV measures. Eyeblink laterality, particularly at 2.5 and 10 pps, showed strong frequency-specific effects. EEG power asymmetry and spectral centroids were associated with HRV indices, and 2.5 pps stimulation produced the strongest parasympathetic HRV response. Blink rate correlated with increased sympathetic and decreased parasympathetic activity. Baseline HRV measures, such as lower heart rate, predicted participant dropout. Eyeblinks were analysed using BLINKER software (v. 1.1.0), and additional complexity and entropy (‘CEPS-BLINKER’) metrics were derived. These measures were more predictive of adverse reactions than EEG-derived indices. Overall, TEAS modulates multiple physiological markers in a frequency-specific manner. Eyeblink characteristics, especially laterality, may offer valuable insights into autonomic function and TEAS efficacy in neuromodulation research. Full article
(This article belongs to the Section Biosensors)
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23 pages, 7152 KiB  
Article
A Programmable Gain Calibration Method to Mitigate Skin Tone Bias in PPG Sensors
by Connor MacIsaac, Macros Nguyen, Alexander Uy, Tianmin Kong and Ava Hedayatipour
Biosensors 2025, 15(7), 423; https://doi.org/10.3390/bios15070423 - 2 Jul 2025
Viewed by 461
Abstract
Photoplethysmography (PPG) is a widely adopted optical technique for cardiovascular monitoring, but its accuracy is often compromised by skin pigmentation, which attenuates the signal in individuals with darker skin tones. This research addresses the challenge of skin pigmentation by developing a PPG sensor [...] Read more.
Photoplethysmography (PPG) is a widely adopted optical technique for cardiovascular monitoring, but its accuracy is often compromised by skin pigmentation, which attenuates the signal in individuals with darker skin tones. This research addresses the challenge of skin pigmentation by developing a PPG sensor system with a novel gain calibration strategy. We present a hardware prototype integrating a programmable gain amplifier (PGA), specifically the OPA3S328 operational amplifier, controlled by a microcontroller. The system performs a one-time gain adjustment at initialization based on the user’s skin tone, which is quantified using RGB image analysis. This “set-and-hold” approach normalizes the signal amplitude across various skin tones while effectively preserving the native morphology of the PPG waveform, which is essential for advanced cardiovascular diagnostics. Experimental validation with over 70 human volunteers demonstrated the PGA’s ability to apply calibrated gain levels, derived from a first-degree polynomial relationship between skin pigmentation and red light absorption. This approach significantly improved signal consistency across different skin tones. The findings highlight the efficacy of pre-measurement gain correction for achieving reliable PPG sensing in diverse populations and lay the groundwork for future optimization of PPG sensor designs to improve reliability in wearable health monitoring devices. Full article
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14 pages, 3350 KiB  
Article
Feasibility of Photoplethysmography in Detecting Arterial Stiffness in Hypertension
by Parmis Karimpour, James M. May and Panicos A. Kyriacou
Photonics 2025, 12(5), 430; https://doi.org/10.3390/photonics12050430 - 29 Apr 2025
Viewed by 840
Abstract
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 [...] Read more.
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 ba 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 ba 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
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25 pages, 3869 KiB  
Article
Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography
by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon and Fadi Aloul
Appl. Sci. 2025, 15(9), 4770; https://doi.org/10.3390/app15094770 - 25 Apr 2025
Cited by 1 | Viewed by 864
Abstract
Atrial fibrillation (AF) is a type of cardiac arrhythmia with a worldwide prevalence of more than 37 million among the adult population. This elusive disease is a major risk factor for ischemic stroke, along with increased rates of significant morbidity and eventual mortality. [...] Read more.
Atrial fibrillation (AF) is a type of cardiac arrhythmia with a worldwide prevalence of more than 37 million among the adult population. This elusive disease is a major risk factor for ischemic stroke, along with increased rates of significant morbidity and eventual mortality. It is clinically diagnosed using medical-grade electrocardiogram (ECG) sensors in ambulatory settings. The recent emergence of consumer-grade wearables equipped with photoplethysmography (PPG) sensors has exhibited considerable promise for non-intrusive continuous monitoring in free-living conditions. However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). We derive and feed heart rate variability (HRV) and pulse rate variability (PRV) features as auxiliary inputs to the framework for robustness. A larger teacher model is trained using the MIT-BIH Arrhythmia ECG dataset. Through transfer learning (TL), its learned representation is adapted to a compressed student model (32x smaller) variant by using knowledge distillation (KD) for classifying AF with the UMass and MIMIC-III datasets of PPG signals. This results in the student model yielding average improvements in accuracy, sensitivity, F1 score, and Matthews correlation coefficient of 2.0%, 15.05%, 11.7%, and 9.85%, respectively, across both PPG datasets. Additionally, we employ Gradient-weighted Class Activation Mapping (Grad-CAM) to confer a notion of interpretability to the model decisions. We conclude that through a combination of techniques such as TL and KD, i.e., pre-trained initialization, we can utilize learned ECG concepts for scarcer PPG scenarios. This can reduce resource usage and enable deployment on edge devices. Full article
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23 pages, 11253 KiB  
Article
Evaluation of the Geomorphon Approach for Extracting Troughs in Polygonal Patterned Ground Across Different Permafrost Environments
by Amin Wen, Tonghua Wu, Xiaofan Zhu, Jie Chen, Jianzong Shi, Peiqing Lou, Dong Wang, Xin Ma and Xiaodong Wu
Remote Sens. 2025, 17(6), 1040; https://doi.org/10.3390/rs17061040 - 16 Mar 2025
Viewed by 776
Abstract
As the climate continues to warm, the thawing of ice-rich permafrost leads to changes in the polygonal patterned ground (PPG) landscape, exhibiting an array of spatial heterogeneity in trough patterns, governing permafrost stability and hydrological and ecosystem dynamics. Developing accurate methods for detecting [...] Read more.
As the climate continues to warm, the thawing of ice-rich permafrost leads to changes in the polygonal patterned ground (PPG) landscape, exhibiting an array of spatial heterogeneity in trough patterns, governing permafrost stability and hydrological and ecosystem dynamics. Developing accurate methods for detecting trough areas will allow us to better understand where the degradation of PPG occurs. The Geomorphon approach is proven to be a computationally efficient method that utilizes digital elevation models (DEMs) for terrain classification across multiple scales. In this study, we firstly evaluate the appliance of the Geomorphon algorithm in trough mapping in Prudhoe Bay (PB) in Alaska and the Wudaoliang region (WDL) on the central Qinghai–Tibet Plateau. We used the optimized DEM resolution, flatness threshold (t), and search radius (L) as input parameters for Geomorphon. The accuracy of trough recognition was evaluated against that of hand-digitized troughs and field measurements, using the mean intersection over union (mIOU) and the F1 Score. By setting a classification threshold, the troughs were detected where the Geomorphon values were larger than 6. The results show that (i) the lowest t value (0°) captured the microtopograhy of the troughs, while the larger L values paired with a DEM resolution of 50 cm diminished the impact of minor noise, improving the accuracy of trough detection; (ii) the optimized Geomorphon model produced trough maps with a high accuracy, achieving mIOU and F1 Scores of 0.89 and 0.90 in PB and 0.84 and 0.87 in WDL, respectively; and (iii) compared with the polygonal boundaries, the trough maps can derive the heterogeneous features to quantify the degradation of PPG. By comparing with the traditional terrain indices for trough classification, Geomorphon provides a direct classification of troughs, thus advancing the scientific reproducibility of comparisons in PB and WDL. This work provides a valuable method that may propel future pan-Arctic studies of trough mapping. Full article
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12 pages, 10206 KiB  
Proceeding Paper
Portable Biomedical System for Acquisition, Display and Analysis of Cardiac Signals (SCG, ECG, ICG and PPG)
by Valery Sofía Zúñiga Gómez, Adonis José Pabuena García, Breiner David Solorzano Ramos, Saúl Antonio Pérez Pérez, Jean Pierre Coll Velásquez, Pablo Daniel Bonaveri and Carlos Gabriel Díaz Sáenz
Eng. Proc. 2025, 83(1), 19; https://doi.org/10.3390/engproc2025083019 - 23 Jan 2025
Viewed by 1094
Abstract
This study introduces a mechatronic biomedical device engineered for concurrent acquisition and analysis of four cardiac non-invasive signals: Electrocardiogram (ECG), Phonocardiogram (PCG), Impedance Cardiogram (ICG), and Photoplethysmogram (PPG). The system enables assessment of individual and simultaneous waveforms, allowing for detailed scrutiny of cardiac [...] Read more.
This study introduces a mechatronic biomedical device engineered for concurrent acquisition and analysis of four cardiac non-invasive signals: Electrocardiogram (ECG), Phonocardiogram (PCG), Impedance Cardiogram (ICG), and Photoplethysmogram (PPG). The system enables assessment of individual and simultaneous waveforms, allowing for detailed scrutiny of cardiac electrical and mechanical dynamics, encompassing heart rate variability, systolic time intervals, pre-ejection period (PEP), and aortic valve opening and closing timings (ET) through an application programmed with MATLAB App Designer, which applies derivative filters, smoothing, and FIR digital filters and evaluates the delay of each one, allowing the synchronization of all signals. These metrics are indispensable for deriving critical hemodynamic indices such as Stroke Volume (SV) and Cardiac Output (CO), paramount in the diagnostic armamentarium against cardiovascular pathologies. The device integrates an assembly of components including five electrodes, operational and instrumental amplifiers, infrared opto-couplers, accelerometers, and advanced filtering subsystems, synergistically tailored for precision and fidelity in signal processing. Rigorous validation utilizing a cohort of healthy subjects and benchmarking against established commercial instrumentation substantiates an accuracy threshold below 4.3% and an Interclass Correlation Coefficient (ICC) surpassing 0.9, attesting to the instrument’s exceptional reliability and robustness in quantification. These findings underscore the clinical potency and technical prowess of the developed device, empowering healthcare practitioners with an advanced toolset for refined diagnosis and management of cardiovascular disorders. Full article
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24 pages, 3521 KiB  
Article
Assessing the Efficacy of Various Machine Learning Algorithms in Predicting Blood Pressure Using Pulse Transit Time
by Ahmad F. Turki
Diagnostics 2025, 15(3), 261; https://doi.org/10.3390/diagnostics15030261 - 23 Jan 2025
Cited by 1 | Viewed by 1840
Abstract
Background/Objectives: This study investigates the potential of Pulse Transit Time (PTT) derived from Impedance Plethysmography (IPG), Photoplethysmography (PPG), and Electrocardiography (ECG) for non-invasive and cuffless blood pressure monitoring. IPG measures blood volume changes through electrical conductivity, while PPG detects variations in microvascular blood [...] Read more.
Background/Objectives: This study investigates the potential of Pulse Transit Time (PTT) derived from Impedance Plethysmography (IPG), Photoplethysmography (PPG), and Electrocardiography (ECG) for non-invasive and cuffless blood pressure monitoring. IPG measures blood volume changes through electrical conductivity, while PPG detects variations in microvascular blood flow, providing essential insights for wearable health monitoring devices. Methods: Data were collected from 100 healthy participants under resting and post-exercise conditions using a custom IPG system synchronized with ECG, PPG, and blood pressure readings to create controlled blood pressure variations. Machine learning models, including Random Forest, Logistic Regression, Support Vector Classifier, and K-Neighbors, were applied to predict blood pressure categories based on PTT and cardiovascular features. Results: Among the various machine learning models evaluated, Random Forest demonstrated effective performance, achieving an overall accuracy of 90%. The model also exhibited robustness, effectively handling the challenge of unbalanced classes, with a 95% confidence interval (CI) for accuracy ranging from 80% to 95%. This indicates its reliability across different data splits despite the class imbalance. Notably, PTT derived from PPG emerged as a critical predictive feature, further enhancing the model’s ability to accurately classify blood pressure categories and solidifying its utility in non-invasive cardiovascular monitoring. Conclusions: The findings affirm the efficacy of using PTT measurements from PPG, IPG, and ECG as reliable predictors for non-invasive blood pressure monitoring. This study substantiates the integration of these techniques into wearable devices, offering a significant advancement for continuous, cuffless, and non-invasive blood pressure assessment. Full article
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17 pages, 7070 KiB  
Article
Research on Heart Rate Detection from Facial Videos Based on an Attention Mechanism 3D Convolutional Neural Network
by Xiujuan Sun, Ying Su, Xiankai Hou, Xiaolan Yuan, Hongxue Li and Chuanjiang Wang
Electronics 2025, 14(2), 269; https://doi.org/10.3390/electronics14020269 - 10 Jan 2025
Viewed by 1300
Abstract
Remote photoplethysmography (rPPG) has attracted growing attention due to its non-contact nature. However, existing non-contact heart rate detection methods are often affected by noise from motion artifacts and changes in lighting, which can lead to a decrease in detection accuracy. To solve this [...] Read more.
Remote photoplethysmography (rPPG) has attracted growing attention due to its non-contact nature. However, existing non-contact heart rate detection methods are often affected by noise from motion artifacts and changes in lighting, which can lead to a decrease in detection accuracy. To solve this problem, this paper initially employs manual extraction to precisely define the facial Region of Interest (ROI), expanding the facial area while avoiding rigid regions such as the eyes and mouth to minimize the impact of motion artifacts. Additionally, during the training phase, illumination normalization is employed on video frames with uneven lighting to mitigate noise caused by lighting fluctuations. Finally, this paper introduces a 3D convolutional neural network (CNN) method incorporating an attention mechanism for heart rate detection from facial videos. We optimize the traditional 3D-CNN to capture global features in spatiotemporal data more effectively. The SimAM attention mechanism is introduced to enable the model to precisely focus on and enhance facial ROI feature representations. Following the extraction of rPPG signals, a heart rate estimation network using a bidirectional long short-term memory (BiLSTM) model is employed to derive the heart rate from the signals. The method introduced here is experimentally validated on two publicly available datasets, UBFC-rPPG and PURE. The mean absolute errors were 0.24 bpm and 0.65 bpm, the root mean square errors were 0.63 bpm and 1.30 bpm, and the Pearson correlation coefficients reached 0.99, confirming the method’s reliability. Comparisons of predicted signals with ground truth signals further validated its accuracy. Full article
(This article belongs to the Section Artificial Intelligence)
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11 pages, 908 KiB  
Article
Evaluation of Photoplethysmography-Based Monitoring of Respiration Rate During High-Intensity Interval Training: Implications for Healthcare Monitoring
by Marjolein Muller, Kambiz Ebrahimkheil, Tara Vijgeboom, Casper van Eijck and Eelko Ronner
Biosensors 2024, 14(12), 631; https://doi.org/10.3390/bios14120631 - 20 Dec 2024
Cited by 1 | Viewed by 1130
Abstract
Monitoring respiration rate (RR) is crucial in various healthcare settings, particularly during demanding (physical) activities where respiratory dynamics are critical indicators of health status. This study aimed to evaluate the accuracy of photoplethysmography (PPG)-based monitoring of RR during high-intensity interval training (HIIT) and [...] Read more.
Monitoring respiration rate (RR) is crucial in various healthcare settings, particularly during demanding (physical) activities where respiratory dynamics are critical indicators of health status. This study aimed to evaluate the accuracy of photoplethysmography (PPG)-based monitoring of RR during high-intensity interval training (HIIT) and its potential applications in healthcare. Between January and March 2024, healthy volunteers participated in a cycling HIIT session with increasing resistance levels. The RR measurements obtained using the PPG-based CardioWatch 287-2 (Corsano Health) were compared with an ECG patch-derived (Vivalink) reference. Subgroup analyses were conducted based on skin type and sex. A total of 35 participants contributed 1794 paired RR measurements. The PPG algorithm for RR monitoring showed an average root mean square (Arms) error of 2.13 breaths per minute (brpm), a bias of −0.09 brpm, and limits of agreement (LoA) from −4.28 to 4.09 brpm. Results were consistent across the different demographic subgroups. The CardioWatch 287-2 therefore demonstrated reliable RR monitoring during HIIT, supporting its potential use in healthcare settings for continuous, non-invasive respiratory monitoring, particularly in physical rehabilitation and chronic respiratory condition management. Full article
(This article belongs to the Section Biosensors and Healthcare)
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14 pages, 793 KiB  
Article
MFF-Net: A Lightweight Multi-Frequency Network for Measuring Heart Rhythm from Facial Videos
by Wenqin Yan, Jialiang Zhuang, Yuheng Chen, Yun Zhang and Xiujuan Zheng
Sensors 2024, 24(24), 7937; https://doi.org/10.3390/s24247937 - 12 Dec 2024
Cited by 1 | Viewed by 1015
Abstract
Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these [...] Read more.
Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios. Hence, we propose a lightweight multi-frequency network named MFF-Net to measure heart rhythm via facial videos in a short time. Firstly, we propose a multi-frequency mode signal fusion (MFF) mechanism, which can separate the characteristics of different modes of the original rPPG signals and send them to a processor with independent parameters, helping the network recover blood volume pulse (BVP) signals accurately under a complex noise environment. In addition, in order to help the network extract the characteristics of different modal signals effectively, we designed a temporal multiscale convolution module (TMSC-module) and spectrum self-attention module (SSA-module). The TMSC-module can expand the receptive field of the signal-refining network, obtain more abundant multiscale information, and transmit it to the signal reconstruction network. The SSA-module can help a signal reconstruction network locate the obvious inferior parts in the reconstruction process so as to make better decisions when merging multi-dimensional signals. Finally, in order to solve the over-fitting phenomenon that easily occurs in the network, we propose an over-fitting sampling training scheme to further improve the fitting ability of the network. Comprehensive experiments were conducted on three benchmark datasets, and we estimated HR and HRV based on the BVP signals derived by MFF-Net. Compared with state-of-the-art methods, our approach achieves better performance both on HR and HRV estimation with lower computational burden. We can conclude that the proposed MFF-Net has the opportunity to be applied in many real-world scenarios. Full article
(This article belongs to the Section Sensor Networks)
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21 pages, 3342 KiB  
Article
Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring
by Rinaldi Anwar Buyung, Alhadi Bustamam and Muhammad Remzy Syah Ramazhan
Sensors 2024, 24(23), 7537; https://doi.org/10.3390/s24237537 - 26 Nov 2024
Cited by 1 | Viewed by 2633
Abstract
Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood [...] Read more.
Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood circulation cycle. However, this technique is sensitive to environmental lightning and different skin pigmentation, resulting in unreliable results. This research presents a multimodal approach to non-contact heart rate estimation by combining facial video and physical attributes, including age, gender, weight, height, and body mass index (BMI). For this purpose, we collected local datasets from 60 individuals containing a 1 min facial video and physical attributes such as age, gender, weight, and height, and we derived the BMI variable from the weight and height. We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. These findings highlight the potential for interpretable, non-contact, real-time heart rate measurement systems to contribute effectively to applications in telemedicine and mass screening. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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16 pages, 2832 KiB  
Article
Deriving Accurate Nocturnal Heart Rate, rMSSD and Frequency HRV from the Oura Ring
by Tian Liang, Gizem Yilmaz and Chun-Siong Soon
Sensors 2024, 24(23), 7475; https://doi.org/10.3390/s24237475 - 23 Nov 2024
Viewed by 6591
Abstract
Cardiovascular diseases are a major cause of mortality worldwide. Long-term monitoring of nighttime heart rate (HR) and heart rate variability (HRV) may be useful in identifying latent cardiovascular risk. The Oura Ring has shown excellent correlation only with ECG-derived HR, but not HRV. [...] Read more.
Cardiovascular diseases are a major cause of mortality worldwide. Long-term monitoring of nighttime heart rate (HR) and heart rate variability (HRV) may be useful in identifying latent cardiovascular risk. The Oura Ring has shown excellent correlation only with ECG-derived HR, but not HRV. We thus assessed if stringent data quality filters can improve the accuracy of time-domain and frequency-domain HRV measures. 92 younger (<45 years) and 22 older (≥45 years) participants from two in-lab sleep studies with concurrent overnight Oura and ECG data acquisition were analyzed. For each 5 min segment during time-in-bed, the validity proportion (percentage of interbeat intervals rated as valid) was calculated. We evaluated the accuracy of Oura-derived HR and HRV measures against ECG at different validity proportion thresholds: 80%, 50%, and 30%; and aggregated over different durations: 5 min, 30 min, and Night-level. Strong correlation and agreements were obtained for both age groups across all HR and HRV metrics and window sizes. More stringent validity proportion thresholds and averaging over longer time windows (i.e., 30 min and night) improved accuracy. Higher discrepancies were found for HRV measures, with more than half of older participants exceeding 10% Median Absolute Percentage Error. Accurate HRV measures can be obtained from Oura’s PPG-derived signals with a stringent validity proportion threshold of around 80% for each 5 min segment and aggregating over time windows of at least 30 min. Full article
(This article belongs to the Section Wearables)
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33 pages, 9852 KiB  
Article
Assessment of Physiological Signals from Photoplethysmography Sensors Compared to an Electrocardiogram Sensor: A Validation Study in Daily Life
by Rana Zia Ur Rehman, Meenakshi Chatterjee, Nikolay V. Manyakov, Melina Daans, Amanda Jackson, Andrea O’Brisky, Tacie Telesky, Sophie Smets, Pieter-Jan Berghmans, Dongyan Yang, Elena Reynoso, Molly V. Lucas, Yanran Huo, Vasanth T. Thirugnanam, Tommaso Mansi and Mark Morris
Sensors 2024, 24(21), 6826; https://doi.org/10.3390/s24216826 - 24 Oct 2024
Cited by 2 | Viewed by 4959
Abstract
Wearables with photoplethysmography (PPG) sensors are being increasingly used in clinical research as a non-invasive, inexpensive method for remote monitoring of physiological health. Ensuring the accuracy and reliability of PPG-derived measurements is critical, as inaccuracies can impact research findings and clinical decisions. This [...] Read more.
Wearables with photoplethysmography (PPG) sensors are being increasingly used in clinical research as a non-invasive, inexpensive method for remote monitoring of physiological health. Ensuring the accuracy and reliability of PPG-derived measurements is critical, as inaccuracies can impact research findings and clinical decisions. This paper systematically compares heart rate (HR) and heart rate variability (HRV) measures from PPG against an electrocardiogram (ECG) monitor in free-living settings. Two devices with PPG and one device with an ECG sensor were worn by 25 healthy volunteers for 10 days. PPG-derived HR and HRV showed reasonable accuracy and reliability, particularly during sleep, with mean absolute error < 1 beat for HR and 6–15 ms for HRV. The relative error of HRV estimated from PPG varied with activity type and was higher than during the resting state by 14–51%. The accuracy of HR/HRV was impacted by the proportion of usable data, body posture, and epoch length. The multi-scale peak and trough detection algorithm demonstrated superior performance in detecting beats from PPG signals, with an F1 score of 89% during sleep. The study demonstrates the trade-offs of utilizing PPG measurements for remote monitoring in daily life and identifies optimal use conditions by recommending enhancements. Full article
(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health)
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16 pages, 1386 KiB  
Review
Photoplethysmography Features Correlated with Blood Pressure Changes
by Mohamed Elgendi, Elisabeth Jost, Aymen Alian, Richard Ribon Fletcher, Hagen Bomberg, Urs Eichenberger and Carlo Menon
Diagnostics 2024, 14(20), 2309; https://doi.org/10.3390/diagnostics14202309 - 17 Oct 2024
Cited by 2 | Viewed by 3465
Abstract
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 [...] Read more.
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
(This article belongs to the Section Biomedical Optics)
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