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Dye-encoded bead-based assays are widely used for diagnostics. Multiple bead populations are required for multiplexing and can be produced using different dye colors, labeling levels, or combinations of dye ratios. Ready-to-use multiplex bead populations restrict users to specific targets, are costly, or require specialized instrumentation. In-house methods produce few bead plexes or require many fine-tuning steps. To expand bead encoding strategies, we present a simple, safe, and cost-effective bench-top system for generating bead populations using photobleaching. By photobleaching commercially available dye-encoded magnetic beads for different durations, we produce three times as many differentiable bead populations on flow cytometry from a single dye color. Our photobleaching system uses a high-power LED module connected to a light concentrator and a heat sink. The beads are photobleached in solution homogeneously by constant mixing. We demonstrate this photobleaching method can be utilized for cross-testing antibodies, which is the first step in developing immunoassays. The assay uses multiple photobleached encoded beads conjugated with capture antibodies to test many binding pairs simultaneously. To further expand the number of antibodies that can be tested at once, several antibodies were conjugated to the same bead, forming a pooled assay. Our assay predicts the performance of antibody pairs used in ultrasensitive Simoa assays, narrowing the number of cross-tested pairs that need to be tested by at least two-thirds and, therefore, providing a rapid alternative for an initial antibody pair screening. The photobleaching system can be utilized for other applications, such as multiplexing, and for photobleaching other particles in solution. [ABSTRACT FROM AUTHOR]

Integrated sensing and communications (ISAC) based on radio frequency (RF), merging the sensing and communication functionalities into a single platform, can effectively address the increasing demand for spectrums due to the proliferation of wireless devices. However, the ISAC commonly suffers from spectrum scarcity, high power consumption, and limited sensing capabilities. Here flexible self‐powered optical ISAC (O‐ISAC) is proposed and fabricated by preparing a high‐speed near‐infrared organic photodetector (NIR OPD) that exhibits a broad photodetection ranging from 300 to 1100 nm, specific detectivity over 1013 Jones, and −3 dB cutoff frequency over 1 MHz (λ = 1050 nm) in self‐powered mode, ranking as the fastest among self‐powered NIR OPDs in the region above 1000 nm. The device is fabricated by incorporating a narrow‐bandgap non‐fullerene acceptor BTPSV‐4Cl into the PCE‐10:BTPSV‐4F system to finely tune the film morphology and reduce trap states and energetic disorder. The semitransparent and regular NIR OPDs are integrated on one substrate to fabricate flexible O‐ISAC, realizing simultaneous accurate vital signs monitoring and high‐speed data transmission under ambient, sunlight, and NIR lighting conditions. It is envisioned that the proposed O‐ISAC will inspire a new generation of wireless technology to address RF limitations for emerging applications, such as intelligent healthcare, autonomous vehicles, and security. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to evaluate the accuracy of peripheral oxygen saturation (SpO2) measurements from Polar Elixir™ pulse oximetry technology compared with arterial oxygen saturation (SaO2) measurements during acute stepwise steady-state inspired hypoxia at rest. A post hoc objective was to determine if SpO2 measurements could be improved by recalibrating the Polar Elixir™ algorithm with SaO2 values from a random subset of participants. Methods: The International Organization for Standardization (ISO) protocol (ISO 80601-2-61:2017) for evaluating the SpO2 accuracy of pulse oximeter equipment was followed whereby five plateaus of SaO2 between 70% and 100%were achieved using stepwise reductions in inspired O2 during supine rest. Blood samples drawn through a radial arterial catheter from 25 participants were first used to compare SaO2 with SpO2 measurements from Polar Elixir™. Then the Polar Elixir™ algorithm was recalibrated using SaO2 data from 13 random participants, and SpO2 estimates were recalculated for the other 12 participants. For SaO2 values between 70%and 100%, root mean square error, intraclass correlation coefficients (ICC), Pearson correlations, and Bland--Altman plots were used to assess the accuracy, agreement, and strength of relationship between SaO2 values and SpO2 values from Polar Elixir™. Results: The initial root mean square error for Polar Elixir™ was 4.13%. After recalibrating the algorithm, the RMSE was improved to 2.67%. The ICC revealed excellent levels of agreement between SaO2 and Polar Elixir™SpO2 values both before (ICC(1,3) = 0.837, df = 574, P < 0.001) and after (ICC(1,3) = 0.942, df = 287, P < 0.001) recalibration. Conclusions: Relative to ISO standards, Polar Elixir™ yielded accurate SpO2 measurements during stepwise inspired hypoxia at restwhen comparedwith SaO2 values, which were improved by recalibrating the algorithm using a subset of the SaO2 data. [ABSTRACT FROM AUTHOR]

BACKGROUND More habitual time spent engaging in prolonged sedentary behaviors increases the risk of developing hypertension. Beat-by-beat systolic (SBPV) and diastolic blood pressure variability (DBPV) are more pronounced in persons with hypertension and may be an early manifestation of blood pressure dysregulation. We tested the hypothesis that a single bout of prolonged sitting augments very short-term SBPV and DBPV. The secondary aim was to explore sex differences in prolonged sitting-induced increases in SBPV and DBPV. METHODS Thirty-three adults (22.9 ± 1.9 years; 17 females) completed a single, 3-hour bout of prolonged sitting with beat-by-beat arterial pressure determined at baseline, 1.5-hour, and 3-hour via finger photoplethysmography. RESULTS There were no sex differences observed for baseline brachial SBP (males: 122 ± 10 mm Hg; females: 111 ± 9 mm Hg), SBPV (males: 1.87 ± 0.63 mm Hg; females: 1.51 ± 0.38 mm Hg), DBP (males: 68 ± 6 mm Hg; females: 66 ± 8 mm Hg), or DBPV (males: 1.40 ± 0.41 mm Hg; females: 1.27 ± 0.32 mm Hg) (all, P  > 0.41). In the pooled sample, baseline SBPV (1.68 ± 0.54 mm Hg) remained unchanged after 1.5 hours (1.80 ± 0.60 mm Hg; P  = 0.59) but increased after 3.0 hours (1.84 ± 0.52 mm Hg; P  = 0.01). This post-sitting increase was driven by males (P  = 0.009), with no difference observed in females (P  = 1.00). Similarly, baseline DBPV (1.33 ± 0.36 mm Hg) was similar after 1.5 hours (1.42 ± 0.41 mm Hg; P  = 0.72) but was increased at 3 hours (1.50 ± 0.34 mm Hg; P  = 0.02). However, no sex differences in DBPV (all, P  > 0.07) were observed across the time points. CONCLUSIONS In young, normotensive adults, a single bout of prolonged sitting augmented beat-by-beat blood pressure variability, which may provide a link between uninterrupted sitting and the development of blood pressure dysregulation. [ABSTRACT FROM AUTHOR]

Introduction. The aim of our study is to comparatively analyze the canal cyclic fatigue resistance of widely used rotary file systems, including EndoArt Touch Gold (ETG), Perfect MTF Plus Gold (PPG), Fanta V‐Taper Gold (FVG), and ProTaper Next (PTN). Methods. Stainless steel canals with a 60° angle and a 3‐mm curvature radius were specially prepared. The canals were shaped with each rotary file system and tested for resistance using a cyclic fatigue testing apparatus. The number of fracture cycles (NFC; K file tip separation) was measured. Data were analysed by one‐way ANOVA and post hoc LSD tests. Results. PTN and PPG rotary file systems exhibited the highest NFC. The NFC value for PTN was 589 ± 63, and for PPG, it was 507 ± 51. Conclusion. The results of this study demonstrate that rotary file systems such as PTN and PPG exhibit higher cyclic fatigue resistance. ETG and FVG rotary file systems also possess generally acceptable cyclic fatigue resistance levels. [ABSTRACT FROM AUTHOR]

The construction industry substantially contributes to the economic growth of a country. However, it records a large number of workplace injuries and fatalities annually due to its hesitant adoption of automated safety monitoring systems. To address this critical concern, this study presents a real-time monitoring approach that uses the Internet of Things and ensemble learning. This study leverages wearable sensor technology, such as photoplethysmography and electroencephalography sensors, to continuously track the physiological parameters of construction workers. The sensor data is processed using an ensemble learning approach called the ChronoEnsemble Fatigue Analysis System (CEFAS), comprising deep autoregressive and temporal fusion transformer models, to accurately predict potential physical and mental fatigue. Comprehensive evaluation metrics, including mean square error, mean absolute scaled error, and symmetric mean absolute percentage error, demonstrated the superior prediction accuracy and reliability of the proposed model compared to standalone models. The ensemble learning model exhibited remarkable precision in predicting physical and mental fatigue, as evidenced by the mean square errors of 0.0008 and 0.0033, respectively. The proposed model promptly recognizes potential hazards and irregularities, considerably enhancing worker safety and reducing on-site risks. [ABSTRACT FROM AUTHOR]

Oxygen uptake ( V ˙ O 2 ) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate ( H R ) is a prominent physiological indicator correlated with V ˙ O 2 and is often used for indirect V ˙ O 2 prediction. This study investigates the impact of H R placement on V ˙ O 2 prediction accuracy by analyzing H R data combined with the respiratory rate ( R E S P ) 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 ˙ O 2 , R E S P , V ˙ E , and H R were collected and used to develop a neural network model for V ˙ O 2 prediction. The influence of H R 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 H R combined with R E S P and V ˙ E ( V ˙ O 2 H R + R E S P + V ˙ E ) produces the most accurate V ˙ O 2 predictions (MAE: 165 mL/min, R2: 0.87, MAPE: 15.91%). Notably, as exercise intensity increases, the accuracy of V ˙ O 2 prediction decreases, particularly within high-intensity exercise. The substitution of H R with different anatomical sites significantly impacts V ˙ O 2 prediction accuracy, with wrist placement showing a more profound effect compared to arm placement. In conclusion, this study underscores the importance of considering H R placement in V ˙ O 2 prediction models, with R E S P and V ˙ E serving as effective compensatory factors. These findings contribute to refining indirect V ˙ O 2 estimation methods, enhancing their predictive capabilities across different exercise intensities and anatomical placements. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring. [ABSTRACT FROM AUTHOR]

Background: The NOL® system (PMD-200™ Nociception Level Monitor; Medasense Ltd., Ramat Gan, Israel) is used for the real-time detection of physiological nociception in anesthetized patients by assessing the parameters indicative of sympathetic activity, such as photoplethysmography, skin conductance, peripheral temperature, and accelerometry, which are quantified into the NOL®-Index. This index is more sensitive than traditional clinical parameters in estimating pain and stress responses. While its effectiveness in general anesthesia is well documented, its efficacy in epidural anesthesia needs further investigation. Methods: This retrospective study analyzed NOL®-Index dynamics compared to conventional parameters after epidural administration of bupivacaine. Following ethics committee approval, 119 NOL® measurements were retrospectively analyzed after thoracic epidural catheter administration in 40 patients undergoing abdominal and urological surgery. The NOL-Index® was assessed at 0, 1, 3, and 5 min post application and compared to heart rate, blood pressure, and bispectral index dynamics. Results: This study showed a significant decrease in the NOL®-Index post-local-anesthetic administration with better sensitivity than classical clinical parameters (0 min = 38 ± 11; 1 min = 22 ± 13*; 3 min = 17 ± 11*; 5 min = 12 ± 10*). Higher doses of local anesthetics led to a significant, dose-dependent decrease in NOL®-Index (low dose, 5 min = 15 ± 10*; high dose, 5 min = 8 ± 8*). Conclusions: This study is the first to demonstrate the effectiveness of the NOL®-Index in measuring nociceptive effects following epidural administration, highlighting its potential superiority over conventional parameters and its sensitivity to dose variations. [ABSTRACT FROM AUTHOR]

Activity and stability are two scientific issues of concern in catalyst design and synthesis. In this study, supported (CoS/CdS) and encapsulated (CdS@CoS) catalysts were synthesized with controlled variations in solvent. Remarkably, the CoS/CdS exhibited higher activity, achieving an impressive average H 2 generation rate of 29.26 mmol g−1 h−1 for the optimized sample. Conversely, the CdS@CoS demonstrated superior stability, showing no significant activity decline over a 20-h period. These findings highlight the importance of the cocatalyst deposition state in affecting the photocatalytic performance of catalysts. This research provides valuable insights for the synthesis of photocatalysts. [Display omitted] • The controllable synthesis of CoS/CdS and CdS@CoS heterostructures was achieved. • The CoS/CdS demonstrated high activity, whereas the CdS@CoS exhibited long-term stability. • The impact of the deposition state of CoS on catalytic performance was examined. • The photocatalytic mechanism was detailedly analyzed. [ABSTRACT FROM AUTHOR]

Mitochondrial dysregulation plays a significant role in the carcinogenesis. On the other hand, its destabilization strongly represses the viability and metastatic potential of cancer cells. Photodynamic and photothermal therapies (PDT and PTT) target mitochondria effectively, providing innovative and non-invasive anticancer therapeutic modalities. Cyanine dyes, with strong mitochondrial selectivity, show significant potential in enhancing PDT and PTT. The potential and limitations of cyanine dyes for mitochondrial PDT and PTT are discussed, along with their applications in combination therapies, theranostic techniques, and optimal delivery systems. Additionally, novel approaches for sonodynamic therapy using photoactive cyanine dyes are presented, highlighting advances in cancer treatment. Photodynamic and photothermal therapies (PDT and PTT) target mitochondria effectively, providing innovative and non-invasive anticancer therapeutic modalities. Here, the authors summarize the promise and limitations of cyanine dyes in enhancing mitochondrial PDT and PTT in cancer treatment. [ABSTRACT FROM AUTHOR]

Audio presentation is often attributed as being capable of influencing a viewer's feeling of immersion during an audiovisual experience. However, there is limited empirical research supporting this claim. This study aimed to explore this effect by presenting a clip renowned for its immersive soundtrack to two groups of participants with either high-end or basic audio presentation. To measure immersion, a novel method is applied, which utilises a camera instead of an electroencephalogram (ECG) for acquiring a heart rate synchronisation feature. The results of the study showed no difference in the feature, or in the responses to an established immersion questionnaire, between the two groups of participants. However, the camera-sourced HR synchronicity feature correlated with the results of the immersion questionnaire. Moreover, the camera-sourced HR synchronicity feature was found to correlate with an equivalent feature sourced from synchronously recorded ECG data. Hence, this shows the viability of using a camera instead of an ECG sensor to quantify heart rate synchronisation but suggests that audio presentation alone is not capable of eliciting a measurable difference in the feeling of immersion in this context. [ABSTRACT FROM AUTHOR]

Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely, Lyapunov Exponent, Approximate Entropy, and Poincaré indices. We found an optimum window duration of 3 s with 50 % overlap and achieved accuracies of 0.75 and 0.74 for both arousal and valence, respectively. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.94 for arousal. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This study proposes integrating a customized channel attention mechanism to compress DL neural networks for AF detection, allowing the model to focus only on the most salient time-series features. The results demonstrate that applying compression through channel attention significantly reduces the total number of model parameters and file size while minimizing loss in detection accuracy. Notably, after compression, performance increases for certain model variants in key AF databases (ADB and C2017DB). Moreover, analyzing the learned channel attention distributions after training enhances the explainability of the AF detection models by highlighting the salient temporal ECG/PPG features most important for its diagnosis. Overall, this research establishes that integrating attention mechanisms is an effective strategy for compressing large DL models, making them deployable on low-power wearable devices. We show that this approach yields compressed, accurate, and explainable AF detectors ideal for wearables. Incorporating channel attention enables simpler yet more accurate algorithms that have the potential to provide clinicians with valuable insights into the salient temporal biomarkers of AF. Our findings highlight that the use of attention is an important direction for the future development of efficient, high-performing, and interpretable AF screening tools for wearable technology. [ABSTRACT FROM AUTHOR]

Body posture and biological sex exhibit independent effects on the sympathetic neural responses to dynamic exercise. However, the neural mechanisms (e.g., baroreflex) by which posture impacts sympathetic outflow during rhythmic muscular contractions, and whether biological sex affects posture-mediated changes in efferent sympathetic nerve traffic during exercise, remain unknown. Thus, we tested the hypotheses that increases in muscle sympathetic nerve activity (MSNA) would be greater during upright compared with supine rhythmic handgrip (RHG) exercise, and that females would demonstrate smaller increases in MSNA during upright RHG exercise than males. Twenty young (30 [6] yr; means [SD]) individuals (9 males, 11 females) underwent 6 min of supine and upright (head-up tilt 45°) RHG exercise at 40% maximal voluntary contraction with continuous measurements of MSNA (microneurography), blood pressure (photoplethysmography), and heart rate (electrocardiogram). In the pooled group, absolute MSNA burst frequency (P < 0.001), amplitude (P = 0.009), and total MSNA (P < 0.001) were higher during upright compared with supine RHG exercise. However, body posture did not impact the peak change in MSNA during RHG exercise (range: P = 0.063–0.495). Spontaneous sympathetic baroreflex gain decreased from rest to RHG exercise (P = 0.006) and was not impacted by posture (P = 0.347). During upright RHG exercise, males demonstrated larger increases in MSNA burst amplitude (P = 0.002) and total MSNA (P = 0.001) compared with females, which coincided with greater reductions in sympathetic baroreflex gain among males (P = 0.004). Collectively, these data indicate that acute attenuation of baroreflex-mediated sympathoinhibition permits increases in MSNA during RHG exercise and that males exhibit a greater reserve for efferent sympathetic neural recruitment during orthostasis than females. NEW & NOTEWORTHY: The impact of posture and sex on cardiovascular control during rhythmic handgrip (RHG) exercise is unknown. We show that increases in muscle sympathetic nerve activity (MSNA) during RHG are partly mediated by a reduction in sympathetic baroreflex gain. In addition, males demonstrate larger increases in total MSNA during upright RHG than females. These data indicate that the baroreflex partly mediates increases in MSNA during RHG and that males have a greater sympathetic vasoconstrictor reserve than females. [ABSTRACT FROM AUTHOR]

Beat-by-beat monitoring of hemodynamic parameters in the left ventricle contributes to the early diagnosis and treatment of heart failure, valvular heart disease, and other cardiovascular diseases. Current accurate measurement methods for ventricular hemodynamic parameters are inconvenient for monitoring hemodynamic indexes in daily life. The objective of this study is to propose a method for estimating intraventricular hemodynamic parameters in a beat-to-beat style based on non-invasive PCG (phonocardiogram) and PPG (photoplethysmography) signals. Three beagle dogs were used as subjects. PCG, PPG, electrocardiogram (ECG), and invasive blood pressure signals in the left ventricle were synchronously collected while epinephrine medicine was injected into the veins to produce hemodynamic variations. Various doses of epinephrine were used to produce hemodynamic variations. A total of 40 records (over 12,000 cardiac cycles) were obtained. A deep neural network was built to simultaneously estimate four hemodynamic parameters of one cardiac cycle by inputting the PCGs and PPGs of the cardiac cycle. The outputs of the network were four hemodynamic parameters: left ventricular systolic blood pressure (SBP), left ventricular diastolic blood pressure (DBP), maximum rate of left ventricular pressure rise (MRR), and maximum rate of left ventricular pressure decline (MRD). The model built in this study consisted of a residual convolutional module and a bidirectional recurrent neural network module which learnt the local features and context relations, respectively. The training mode of the network followed a regression model, and the loss function was set as mean square error. When the network was trained and tested on one subject using a five-fold validation scheme, the performances were very good. The average correlation coefficients (CCs) between the estimated values and measured values were generally greater than 0.90 for SBP, DBP, MRR, and MRD. However, when the network was trained with one subject's data and tested with another subject's data, the performance degraded somewhat. The average CCs reduced from over 0.9 to 0.7 for SBP, DBP, and MRD; however, MRR had higher consistency, with the average CC reducing from over 0.9 to about 0.85 only. The generalizability across subjects could be improved if individual differences were considered. The performance indicates the possibility that hemodynamic parameters could be estimated by PCG and PPG signals collected on the body surface. With the rapid development of wearable devices, it has up-and-coming applications for self-monitoring in home healthcare environments. [ABSTRACT FROM AUTHOR]

Remote photoplethysmography (rPPG) is an emerging non-contact method for monitoring cardiovascular health based on facial videos. The quality of the captured videos largely determines the efficacy of rPPG in this application. Traditional rPPG techniques, while effective for heart rate (HR) estimation, often produce signals with an inadequate signal-to-noise ratio (SNR) for reliable vital sign measurement due to artifacts like head motion and measurement noise. Another pivotal factor is the overlooking of the inherent properties of signals generated by rPPG (rPPG-signals). To address these limitations, we introduce DiffPhys, a novel deep generative model particularly designed to enhance the SNR of rPPG-signals. DiffPhys leverages the conditional diffusion model to learn the distribution of rPPG-signals and uses a refined reverse process to generate rPPG-signals with a higher SNR. Experimental results demonstrate that DiffPhys elevates the SNR of rPPG-signals across within-database and cross-database scenarios, facilitating the extraction of cardiovascular metrics such as HR and HRV with greater precision. This enhancement allows for more accurate monitoring of health conditions in non-clinical settings. [ABSTRACT FROM AUTHOR]

The compensatory reserve index (CRI), derived from machine learning algorithms from peripherally obtained photoplethysmography signals, provides a non‐invasive assessment of cardiovascular stability, that may be useful clinically. Briefly, the CRI device provides a value between 0 and 1, with 1 reflecting full compensable capabilities and 0 reflecting little to no compensable capabilities. However, the CRI algorithm was developed in younger to middle aged adults, such that it is unknown if older age modulates CRI responses to cardiovascular challenges. In young and older subjects, we compared CRI responses to normothermic and hyperthermic progressive lower body negative pressure (LBNP), and volume loading with saline infusion. Eleven younger (20–36 years) and 10 older (61–75 years) healthy participants underwent (1) graded normothermic LBNP up to 30 mmHg, (2) graded hyperthermic (1.5°C increase in blood temperature) LBNP up to 30 mmHg, and (3) infusion of 15 mL/kg saline (volume loading) with hyperthermia maintained. CRI was obtained throughout each procedure. CRI at 30 mmHg LBNP was 0.18 and 0.24 units greater in the older group during normothermic and hyperthermic LBNP, respectively. However, CRI was not different between age groups at any other LBNP stage, nor did CRI change with volume loading regardless of age. In response to passive hyperthermia alone, regression analyses showed that heart rate was the strongest predictor of CRI. Blood temperature, rate pressure product, and stroke volume were also predictive of CRI but to a lesser extent. In conclusion, age attenuates the reduction in CRI during progressive normothermic and hyperthermic LBNP, but only at 30 mmHg. Second, the CRI was unchanged during volume loading in all subjects. Future studies should determine whether the age differences in CRI reflect age differences in LBNP tolerance. [ABSTRACT FROM AUTHOR]

This article discusses a study on the prevalence of isolated inter-scalene compression in patients with suspected thoracic outlet syndrome (TOS). The study used photoplethysmography to analyze arterial and venous results in 116 subjects. The results showed that a significant proportion of limbs had isolated arterial inflow or venous outflow impairment. The authors suggest that this method of analysis can help differentiate between arterial and venous compression and potentially aid in the evaluation and treatment of TOS. Additionally, the document includes a study on TOS using photoplethysmography to assess hemodynamic responses, which found that symptoms were present in more than half of the limbs with apparently normal responses. The document also provides a list of references related to TOS, covering various aspects of the condition. [Extracted from the article]

Summary: Several stress‐related mental disorders are characterised by disturbed sleep, but objective sleep biomarkers are not routinely examined in psychiatric patients. We examined the use of wearable‐based sleep biomarkers in a psychiatric sample with headband electroencephalography (EEG) including pulse photoplethysmography (PPG), with an additional focus on microstructural elements as especially the shift from low to high frequencies appears relevant for several stress‐related mental disorders. We analysed 371 nights of sufficient quality from 83 healthy participants and those with a confirmed stress‐related mental disorder (anxiety‐affective spectrum). The median value of macrostructural, microstructural (spectral slope fitting), and heart rate variables was calculated across nights and analysed at the individual level (N = 83). The headbands were accepted well by patients and the data quality was sufficient for most nights. The macrostructural analyses revealed trends for significance regarding sleep continuity but not sleep depth variables. The spectral analyses yielded no between‐group differences except for a group × age interaction, with the normal age‐related decline in the low versus high frequency power ratio flattening in the patient group. The PPG analyses showed that the mean heart rate was higher in the patient group in pre‐sleep epochs, a difference that reduced during sleep and dissipated at wakefulness. Wearable devices that record EEG and/or PPG could be used over multiple nights to assess sleep fragmentation, spectral balance, and sympathetic drive throughout the sleep–wake cycle in patients with stress‐related mental disorders and healthy controls, although macrostructural and spectral markers did not differ between the two groups. [ABSTRACT FROM AUTHOR]

Energy deficiency (ED) and psychological stress affect athlete health. Given the emerging capabilities of wearable technology, the purpose of this study was to explore associations of wearable technology metrics in relation to lab-based measures of ED and psychological stress. We investigated the associations between (a) wearable-derived heart rate variability (HRV), resting heart rate (RHR), exercise strain and recovery, and (b) laboratory-derived measures of metabolism (resting metabolic rate (RMR), total triiodothyronine (TT3)) and a validated stress assessment (Recovery-Stress Questionnaire for Athletes (RESTQ)-52 items) in NCAA swimmers (n = 23, 10 male, 13 female) during heavy training. Swimmers were grouped by degree of metabolic adaption to ED using the ratio of actual-to-predicted RMR (utilizing the validated cutoff of <0.94) and by sex. Metabolically suppressed swimmers had lower HRV (81 ± 27 ms vs.110 ± 35 ms, p = 0.04). HRV correlated positively with RMR (kcal·kg LBM−1·day−1; where LBM stands for lean body mass) (r = 0.45; p = 0.03). HRV was negatively correlated with sport-specific (r = −0.46; p = 0.03) and total stress (r = −0.46; p = 0.03). In males, HRV correlated negatively with general stress (r = −0.72; p = 0.02) and total stress (r = −0.74, p = 0.01) (RESTQ). Additionally, in males only, the strain was correlated positively with RESTQ recovery–stress balance (r = 0.69; p = 0.03), and negatively correlated with general stress (r = −0.81, p = 0.01), and sport stress (r = −0.89, p < 0.01). No correlations between HRV, RHR, strain or recovery, and stress variables were observed in females. Associations between wearable technology measures of HRV, RHR, strain, and recovery with validated measures of ED and psychological stress should continue to be explored with a focus on underlying mechanisms and moderating influences of biological sex. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) technology enables the measurement of multiple physiological and psychological parameters with low‐cost wearable sensors and is reshaping modern healthcare. Advanced materials play a vital role in improving reliability and accuracy of PPG sensors. Recently, various advanced materials have been explored to optimize PPG sensor design, while some challenges exist toward large‐scale validation and mass production. This paper focuses on advanced materials applied in the photodetectors, light sources, and circuits of PPG sensors. The materials are categorized into four groups: inorganic, organic, nanomaterials, and hybrid materials. The properties and fabrication processes are summarized. Other technical details including the mode of operation, measurement sites, testing, and validation are discussed. The merits and limitations of the state of the art are highlighted to provide some suggestions for the future development of PPG sensors based on advanced materials. [ABSTRACT FROM AUTHOR]

While pulse oximetry using remote photoplethysmography (rPPG) is used in medicine and consumer health, sound theoretical foundations for this methodology are not established. Similarly to traditional pulse oximetry, rPPG oximetry uses two wavelengths to calculate the tissue oxygenation using the so-called ratio-of-ratios, R. However, the relationship between R and tissue oxygenation has not been derived analytically. As such, rPPG oximetry relies mostly on empirical methods. This article aimed to build theoretical foundations for pulse oximetry in rPPG geometry. Using the perturbation approach in diffuse approximation for light propagation in tissues, we obtained an explicit expression of the AC/DC ratio for the rPPG signal. Based on this ratio, the explicit expression for "ratio-of-ratios" was obtained. We have simulated the dependence of "ratio-of-ratios" on arterial blood saturation across a wide range (SaO2 = 70–100%) for several commonly used R/IR light sources (660/780, 660/840, 660/880, and 660/940 nm) and found that the obtained relationship can be modeled by linear functions with an extremely good fit (R2 = 0.98–0.99) for all considered R/IR pairs. Moreover, the location of the pulsatile volume can be extracted from rPPG data. From experimental data, we found that the depth of blood pulsations in the human forehead can be estimated as 0.6 mm on the arterial side, which points to the papillary dermis/subpapillary vascular plexus origin of the pulsatile volume. [ABSTRACT FROM AUTHOR]

Introduction: During heatwaves, it is important to monitor workers' cardiovascular health since 35% of those working in hot environments experience symptoms of heat strain. Wearable technology has been popularized for monitoring heart rate (HR) during recreational activities, but it can also be used to monitor occupational heat strain based on core and skin temperatures and HR. To our knowledge, no devices estimate the cardiovascular strain directly based on stroke volume (SV) or cardiac output (CO). In addition to the hardware, there are limitations regarding the lack of suitable algorithms that would provide such an index based on relevant physiological responses. The validation of the formulae already existing in literature was the principle aim of the present study. Methods: We monitored the cardiovascular responses of our participants to a supine and 60° head-up tilt at the same time each day. During the test, we measured blood pressure derived by finger photoplethysmography, which also provided beat-by-beat measures of SV and CO. Afterwards, we compared the SV derived from the photoplethysmography with the one calculated with the different equations that already exist in literature. Results: The evaluation of the formulae was based on comparing the error of prediction. This residual analysis compared the sum of the squared residuals generated by each formula using the same data set. Conclusion: Our findings suggest that estimating SV with existing formulae is feasible, showing a good correlation and a relatively small bias. Thus, simply measuring workers' blood pressure during breaks could estimate their cardiac strain. [ABSTRACT FROM AUTHOR]

Long-term patient monitoring is required for detection of episodes of atrial fibrillation, one of the most widespread cardiac pathologies. Today, the most used non-invasive technique is Holter electrocardiographic (ECG) monitoring, which can often prove ineffective because of the short duration of recordings (e.g., one day). Other techniques such as photo-plethysmography are adopted by smartwatches for much longer duration monitoring, but this has the disadvantage of offering only intermittent measurements. This study proposes an Internet of Things (IoT) sensor that can provide a very long period of continuous monitoring. The sensor consists of an ECG-integrated Analog Front End (MAX30003), a microcontroller (STM32F401RE), and an IoT narrowband module (STEVAL-STMODLTE). The instantaneous heart rate is extracted from the ECG recording in real time. At intervals of two minutes, the sequence of inter-beat intervals is transmitted to an IoT cloud platform (ThingSpeak). Settled atrial fibrillation event recognition software runs on the cloud and generates alerts when it recognizes such arrhythmia. Performances of the proposed sensor were evaluated by generating analog ECG signals from a public dataset of ECG signals with atrial fibrillation episodes, the MIT-BIH Atrial Fibrillation Database, each recording lasting approximately 10 h. Software implementing the Lorentz algorithm, one of the best detectors of atrial fibrillation, was implemented on the cloud platform. The accuracy, sensitivity, and specificity in recognizing atrial fibrillation episodes of the proposed system was calculated by comparison with a cardiologist's reference data. Across all patients, the proposed method achieved an accuracy of 0.88, a sensitivity 0.71, and a specificity 0.99. The results obtained suggest that the developed system can continuously record and transmit heart rhythms effectively and efficiently and, in addition, offers considerable performance in recognizing atrial fibrillation episodes in real time. [ABSTRACT FROM AUTHOR]

Continuous blood pressure (BP) monitoring is essential for managing cardiovascular disease. However, existing devices often require expert handling, highlighting the need for alternative methods to simplify the process. Researchers have developed various methods using physiological signals to address this issue. Yet, many of these methods either fall short in accuracy according to the BHS, AAMI, and IEEE standards for BP measurement devices or suffer from low computational efficiency due to the complexity of their models. To solve this problem, we developed a BP prediction system that merges extracted features of PPG and ECG from two pulses of both signals using convolutional and LSTM layers, followed by incorporating the R-to-R interval durations as additional features for predicting systolic (SBP) and diastolic (DBP) blood pressure. Our findings indicate that the prediction accuracies for SBP and DBP were 5.306 ± 7.248 mmHg with a 0.877 correlation coefficient and 3.296 ± 4.764 mmHg with a 0.918 correlation coefficient, respectively. We found that our proposed model achieved a robust performance on the MIMIC III dataset with a minimum architectural design and high-level accuracy compared to existing methods. Thus, our method not only meets the passing category for BHS, AAMI, and IEEE guidelines but also stands out as the most rapidly accurate deep-learning-based BP measurement device currently available. [ABSTRACT FROM AUTHOR]

Intrapulse modulation classification of radar signals plays an important role in modern electronic reconnaissance, countermeasures, etc. In this paper, to improve the recognition rate at low signal-to-noise ratio (SNR), we propose a recognition method using the second-order short-time Fourier transform (STFT)-based synchrosqueezing transform (FSST2) combined with a modified convolution neural network, which we name MeNet. In particular, the radar signals are first preprocessed via the time–frequency analysis and STFT-based FSST2. Then, the informative features of the time–frequency images (TFIs) are deeply learned and classified through the MeNet with several specific convolutional blocks. The simulation results show that the overall recognition rate for seven types of intrapulse modulation radar signals can reach 95.6%, even when the SNR is −12 dB. Compared with other networks, the excellent recognition rate proves the superiority of our method. [ABSTRACT FROM AUTHOR]

[Display omitted] Obstructive sleep apnea (OSA) is a serious type of sleep disorder that can lead to cardiometabolic and neurocognitive diseases. We utilized smart device-based photoplethysmography technology to collect sleep data from the Chinese population from 2019 to 2022. Distributed lag nonlinear models combined with a generalized nonlinear model or a linear mixed effects model were used to investigate the short-term associations between daily temperature and indicators of OSA severity. We included a total of 6,232,056 d of sleep monitoring data from 51,842 participants with moderate to severe risk of OSA from 313 Chinese cities. The relationships between ambient temperature and OSA exacerbation, apnea-hypopnea index (AHI), and minimum oxygen saturation (MinSpO 2) were almost linear and present only on the same day. Higher temperatures were associated with a greater risk of OSA exacerbation, with an 8.4% (95% confidence interval (CI): 7.6%–9.3%) increase per 10 °C increase in temperature. A 10 °C increase in daily temperature corresponded to an AHI increase of 0.70 events/h (95% CI: 0.65–0.76) and a MinSpO 2 decrease of 0.18% (95% CI: 0.16%–0.19%). Exposure to elevated temperatures during the night can also lead to adverse effects. The effects of higher temperatures on OSA severity were stronger among men, participants with a body mass index ≥ 24 kg/m2, those aged 45 years and older, individuals with a history of hypertension and diabetes, and during the cold season. This large-scale, nationwide, longitudinal study provides robust evidence suggesting that higher ambient temperatures may immediately worsen OSA. [ABSTRACT FROM AUTHOR]

Microplasma switches have attracted considerable attention in harsh environment applications, such as satellites, space exploration, nuclear reactors, and oil drilling, because of their inherent characteristics. A microplasma switch is generally constructed from a source, drain, and gate electrodes, and current conduction is generated between the drain and source (DS), and modulated by the gate. In this work, to improve the gate lifespan and device stability, a microplasma switch with a gate dielectric barrier structure is fabricated due to the even and stable discharge of a dielectric barrier discharge (DBD), and a parameterized nanosecond pulse voltage signal is applied to the gate. Under the effect of the DS voltage, a pulsed DS current is triggered by the gate pulse since a large number of charged particles are generated by the gate DBD, which shows that the DS switching behavior is triggered by the gate pulse. The microplasma switch operates stably (with an average delay jitter of less than 50 ps) at the repetition frequencies (up to 80 kHz). Moreover, the influence of experimental conditions on the switching performance is systematically investigated. The conduction current and delay, which are related to the discharge intensity and speed, are influenced by the electric-field strength of the channel (determined from the pulse amplitude and DS voltage) and its variation rate (determined from the rising and falling edge time of the pulse). In addition, the device performance is influenced by varying the breakdown voltage of the DS (determined from the gas pressure multiplied by DS spacing), which can result in variation of the working coefficient. It is also influenced by varying the wall voltage (decided by pulse width and frequency), which can result in the decrease in the total voltage of the channel. [ABSTRACT FROM AUTHOR]

Introduction: Brain positron emission tomography/computed tomography (PET/CT) scans are useful for identifying the cause of dementia by evaluating glucose metabolism in the brain with F-18-fluorodeoxyglucose or Aβ deposition with F-18-florbetaben. However, since imaging time ranges from 10 to 30 minutes, movements during the examination might result in image artifacts, which interfere with diagnosis. To solve this problem, data-driven brain motion correction (DDBMC) techniques are capable of performing motion corrected reconstruction using highly accurate motion estimates with high temporal resolution. In this study, we investigated the effectiveness of DDBMC techniques on PET/CT images using a Hoffman phantom, involving continuous rotational and tilting motion, each expanded up to approximately 20 degrees. Materials and methods: Listmode imaging was performed using a Hoffman phantom that reproduced rotational and tilting motions of the head. Brain motion correction processing was performed on the obtained data. Reconstructed images with and without brain motion correction processing were compared. Visual evaluations by a nuclear medicine specialist and quantitative parameters of images with correction and reference still images were compared. Results: Normalized Mean Squared Error (NMSE) results demonstrated the effectiveness of DDBMC in compensating for rotational and tilting motions during PET imaging. In Cases 1 and 2 involving rotational motion, NMSE decreased from 0.15–0.2 to approximately 0.01 with DDBMC, indicating a substantial reduction in differences from the reference image across various brain regions. In the Structural Similarity Index (SSIM), DDBMC improved it to above 0.96 Contrast assessment revealed notable improvements with DDBMC. In continuous rotational motion, % contrast increased from 42.4% to 73.5%, In tilting motion, % contrast increased from 52.3% to 64.5%, eliminating significant differences from the static reference image. These findings underscore the efficacy of DDBMC in enhancing image contrast and minimizing motion induced variations across different motion scenarios. Conclusions: DDBMC processing can effectively compensate for continuous rotational and tilting motion of the head during PET, with motion angles of approximately 20 degrees. However, a significant limitation of this study is the exclusive validation of the proposed method using a Hoffman phantom; its applicability to the human brain has not been investigated. Further research involving human subjects is necessary to assess the generalizability and reliability of the presented motion correction technique in real clinical scenarios. [ABSTRACT FROM AUTHOR]

We developed a centromere-associated protein E (CENP-E) inhibitor employing trans to cis photoisomerization with 405 nm visible light illumination and fast thermal relaxation. This photoswitching characteristic of the inhibitor enabled selective blockage or release of the motion of particular chromosomes within a single mitotic cell. Using this technique, we successfully demonstrated targeted chromosome gain and loss in daughter cells by introducing asymmetric chromosome segregation. [ABSTRACT FROM AUTHOR]

Introduction: Concussion is known to cause transient autonomic and cerebrovascular dysregulation that generally recovers; however, few studies have focused on individuals with an extensive concussion history. Method: The case was a 26-year-old male with a history of 10 concussions, diagnosed for bipolar type II disorder, mild attention-deficit hyperactivity disorder, and a history of migraines/headaches. The case was medicated with Valproic Acid and Escitalopram. Sensor-based baseline data were collected within six months of his injury and on days 1–5, 10, and 14 post-injury. Symptom reporting, heart rate variability (HRV), neurovascular coupling (NVC), and dynamic cerebral autoregulation (dCA) assessments were completed using numerous biomedical devices (i.e., transcranial Doppler ultrasound, 3-lead electrocardiography, finger photoplethysmography). Results: Total symptom and symptom severity scores were higher for the first-week post-injury, with physical and emotional symptoms being the most impacted. The NVC response showed lowered activation in the first three days post-injury, while autonomic (HRV) and autoregulation (dCA) were impaired across all testing visits occurring in the first 14 days following his concussion. Conclusions: Despite symptom resolution, the case demonstrated ongoing autonomic and autoregulatory dysfunction. Larger samples examining individuals with an extensive history of concussion are warranted to understand the chronic physiological changes that occur following cumulative concussions through biosensing devices. [ABSTRACT FROM AUTHOR]

(1) Background: The objective of this study was to predict the vascular health status of elderly women during exercise using pulse wave data and Temporal Convolutional Neural Networks (TCN); (2) Methods: A total of 492 healthy elderly women aged 60–75 years were recruited for the study. The study utilized a cross-sectional design. Vascular endothelial function was assessed non-invasively using Flow-Mediated Dilation (FMD). Pulse wave characteristics were quantified using photoplethysmography (PPG) sensors, and motion-induced noise in the PPG signals was mitigated through the application of a recursive least squares (RLS) adaptive filtering algorithm. A fixed-load cycling exercise protocol was employed. A TCN was constructed to classify flow-mediated dilation (FMD) into "optimal", "impaired", and "at risk" levels; (3) Results: TCN achieved an average accuracy of 79.3%, 84.8%, and 83.2% in predicting FMD at the "optimal", "impaired", and "at risk" levels, respectively. The results of the analysis of variance (ANOVA) comparison demonstrated that the accuracy of the TCN in predicting FMD at the impaired and at-risk levels was significantly higher than that of Long Short-Term Memory (LSTM) networks and Random Forest algorithms; (4) Conclusions: The use of pulse wave data during exercise combined with the TCN for predicting the vascular health status of elderly women demonstrated high accuracy, particularly in predicting impaired and at-risk FMD levels. This indicates that the integration of exercise pulse wave data with TCN can serve as an effective tool for the assessment and monitoring of the vascular health of elderly women. [ABSTRACT FROM AUTHOR]

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users' stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study. [ABSTRACT FROM AUTHOR]

This study presents an integrated analog front-end (AFE) tailored for photoplethysmography (PPG) sensing. The AFE module introduces a novel transimpedance amplifier (TIA) that incorporates capacitive feedback techniques alongside common drain feedback (CDF) TIA. The unique TIA topology achieves both high gain and high sensitivity while maintaining low power consumption. The resultant PPG sensor module demonstrates impressive specifications, including an input noise current of 4.81 pA/sqrt Hz, a transimpedance gain of 18.43 MΩ, and a power consumption of 68 µW. Furthermore, the sensory system integrates an LED driver featuring automatic light control (ALC), which dynamically adjusts the LED power based on the strength of the received signal. Employing 0.35 µm CMOS technology, the AFE implementation occupies a compact footprint of 1.98 mm × 2.475 mm. [ABSTRACT FROM AUTHOR]

Aim: Hypertension and diabetes mellitus (DM) are major causes of morbidity and mortality, with growing burdens in low‐income countries where they are underdiagnosed and undertreated. Advances in machine learning may provide opportunities to enhance diagnostics in settings with limited medical infrastructure. Materials and Methods: A non‐interventional study was conducted to develop and validate a machine learning algorithm to estimate cardiovascular clinical and laboratory parameters. At two sites in Kenya, digital retinal fundus photographs were collected alongside blood pressure (BP), laboratory measures and medical history. The performance of machine learning models, originally trained using data from the UK Biobank, were evaluated for their ability to estimate BP, glycated haemoglobin, estimated glomerular filtration rate and diagnoses from fundus images. Results: In total, 301 participants were enrolled. Compared with the UK Biobank population used for algorithm development, participants from Kenya were younger and would probably report Black/African ethnicity, with a higher body mass index and prevalence of DM and hypertension. The mean absolute error was comparable or slightly greater for systolic BP, diastolic BP, glycated haemoglobin and estimated glomerular filtration rate. The model trained to identify DM had an area under the receiver operating curve of 0.762 (0.818 in the UK Biobank) and the hypertension model had an area under the receiver operating curve of 0.765 (0.738 in the UK Biobank). Conclusions: In a Kenyan population, machine learning models estimated cardiovascular parameters with comparable or slightly lower accuracy than in the population where they were trained, suggesting model recalibration may be appropriate. This study represents an incremental step toward leveraging machine learning to make early cardiovascular screening more accessible, particularly in resource‐limited settings. [ABSTRACT FROM AUTHOR]

In recent years, vector-to-vector mapping-based raw waveform biosignal enhancement methods have gained significant attention in remote health monitoring system. In this paper, a novel end-to-end convolutional encoder–decoder (CED) model with stacked gated recurrent unit (SGRU) is proposed to learn sequential information of biosignal for signal enhancement. The proposed model CED-SGRU employs convolutional neural network to capture the spatial features and SGRU to capture temporal distributions of the biosignal layer by layer which increases the robustness of the proposed model. This work applies mean absolute error as a loss function for CED-SGRU-based vector-to-vector model. The proposed method has been evaluated on three foremost required biosignals, namely electrocardiogram, photoplethysmography and heart rate signals for diagnosing cardiovascular diseases. Experimental result shows outstanding denoising capability of the proposed CED-SGRU model on three biosignals which yields significantly higher reconstruction signal-to-noise ratio value and lower average absolute error, root mean square error and percent root mean square difference values when compared with state-of-the-art-methods. Moreover, the simple architecture of SGRU lowers the complexity of the model; thereby, reducing the inference time for denoising and restoring compressed biosignal tasks is fairly compared with baseline models, namely recurrent neural network model and long short-term memory model. [ABSTRACT FROM AUTHOR]

Summary: This study provides design criteria of multiphase injection locking for an oscillator loop with multiple rotary pulses that can be utilized for an effective frequency multiplier. Coupled oscillator lattice loops can support phase waves. In the case where N pulses rotate in the loop, the oscillatory signal exhibits N times higher frequency than the rotation frequency of a single rotary pulse. Once an external signal succeeds in injection locking the single‐pulse rotation, the loop operates as an efficient N times frequency multiplier. As in a ring oscillator, the progressive multiphase injection locking is best employed, when the loop supports only a single rotary pulse. Because the phase of the external signal becomes different for each pulse, progressive phase assignment does not work well for multiple rotary pulses; therefore, a more efficient scheme of phase assignment is investigated and validated by bifurcation analyses and measurements. The most effective multiphase injection locking is expected for the phase difference between two neighboring oscillators to be set to −4π/N (4π/N), when N pulses rotate to the cell address increases (decreases) in an oscillator loop. [ABSTRACT FROM AUTHOR]

Wearable photoplethysmography (PPG) sensors provide real‐time monitoring of essential human health parameters by integrating a light source with a photodetector. However, they face a common challenge: the presence of a faint pulsating (AC) signal amidst a highly noisy and drifting non‐pulsatile background. This necessitates fine‐tuning how the light is spread out to make sure that a significant number of emitted photons carry vital physiological information and trigger the detector's photocurrent. An innovative organic PPG sensor with a microcavity organic light‐emitting diode (OLED) that emits light in a controlled direction, coupled with an annular organic photodetector (OPD) is demonstrated here. By adjusting the OLED's cavity lengths, the microcavity resonance peak is shifted, achieving angled directional emission. Placing the microcavity OLEDs at the center of the annular OPD enhances the portion of light that reflects from arterial vessels in the skin and tissues. This increases the AC signal for recording arterial vessel pulsations by 47.66%. This advancement simplifies sensor recognition and achieves an unprecedented low power consumption of only 9.96 µW for heart rate sensing, meeting essential recognition criteria. [ABSTRACT FROM AUTHOR]

BACKGROUND: Photoplethysmography (PPG) signals are sensitive to motion-induced interference, leading to the emergence of motion artifacts (MA) and baseline drift, which significantly affect the accuracy of PPG measurements. OBJECTIVE: The objective of our study is to effectively eliminate baseline drift and high-frequency noise from PPG signals, ensuring that the signal's critical frequency components remain within the range of 1 ∼ 10 Hz. METHODS: This paper introduces a novel hybrid denoising method for PPG signals, integrating Variational Mode Decomposition (VMD) with an improved wavelet threshold function. The method initially employs VMD to decompose PPG signals into a set of narrowband intrinsic mode function (IMF) components, effectively removing low-frequency baseline drift. Subsequently, an improved wavelet thresholding algorithm is applied to eliminate high-frequency noise, resulting in denoised PPG signals. The effectiveness of the denoising method was rigorously assessed through a comprehensive validation process. It was tested on real-world PPG measurements, PPG signals generated by the Fluke ProSim™ 8 Vital Signs Simulator with synthesized noise, and extended to the MIMIC-III waveform database. RESULTS: The application of the improved threshold function let to a substantial 11.47% increase in signal-to-noise ratio (SNR) and an impressive 26.75% reduction in root mean square error (RMSE) compared to the soft threshold function. Furthermore, the hybrid denoising method improved SNR by 15.54% and reduced RMSE by 37.43% compared to the improved threshold function. CONCLUSION: This study proposes an effective PPG denoising algorithm based on VMD and an improved wavelet threshold function, capable of simultaneously eliminating low-frequency baseline drift and high-frequency noise in PPG signals while faithfully preserving their morphological characteristics. This advancement establishes the foundation for time-domain feature extraction and model development in the domain of PPG signal analysis. [ABSTRACT FROM AUTHOR]

Contact-free sensing gained much traction in the past decade. While remote monitoring of some parameters (heart rate) is approaching clinical levels of precision, others remain challenging (blood pressure). We investigated the feasibility of estimating blood pressure (BP) via pulse transit time (PTT) in a novel remote single-site manner, using a modified RGB camera. A narrow-band triple band-pass filter allowed us to measure the PTT between different skin layers, harvesting information from green and near-infrared wavelengths. The filter minimizes the inter-channel influence and band overlap, however, some overlap remains within the filter bands. We further resolve this using a color-channel model and a novel channel-separation method. Using the proposed setup and algorithm, we obtained multi-wavelength (MW) PTTs in an experiment inducing BP changes to 9 subjects. The results showed good absolute Pearson's correlation coefficient between both MW PTT and systolic BP (R = 0.61, p = 0.08) as well as diastolic BP (R = 0.54, p = 0.05), pointing to feasibility of the proposed novel remote MW BP estimation via PTT. This was further confirmed in a leave-one-subject-out experiment, where a simple Random Forest regression model achieved mean absolute errors of 3.59 and 2.63 mmHg for systolic and diastolic BP respectively. [ABSTRACT FROM AUTHOR]

Introduction: Prolonged cardiac monitoring after cryptogenic stroke or embolic stroke of undetermined source (ESUS) is necessary to identify atrial fibrillation (AF) that requires anticoagulation. Wearable devices may improve AF detection compared to conventional management. We aimed to review the evidence for the use of wearable devices in post-cryptogenic stroke and post-ESUS monitoring. Methods: We performed a systematic search of PubMed, EMBASE, Scopus and clinicaltrials.gov on 21 July 2022, identifying all studies that investigated the use of wearable devices in patients with cryptogenic stroke or ESUS. The outcomes of AF detection were analysed. Literature reports on electrocardiogram (ECG)-based (external wearable, handheld, patch, mobile cardiac telemetry [MCT], smartwatch) and photoplethysmography (PPG)-based (smartwatch, smartphone) devices were summarised. Results: A total of 27 relevant studies were included (two randomised controlled trials, seven prospective trials, 10 cohort studies, six case series and two case reports). Only four studies compared wearable technology to Holter monitoring or implantable loop recorder, and these studies showed no significant differences on meta-analysis (odds ratio 2.35, 95% confidence interval [CI] 0.74-7.48, I² = 70%). External wearable devices detected AF in 20.7% (95% CI 14.9-27.2, I² = 76%) of patients and MCT detected new AF in 9.6% (95% CI 7.4%-11.9%, I² = 56%) of patients. Other devices investigated included patch sensors, handheld ECG recorders and PPG-based smartphone apps, which demonstrated feasibility in the post-cryptogenic stroke and post-ESUS setting. Conclusion: Wearable devices that are ECG or PPG based are effective for paroxysmal AF detection after cryptogenic stroke and ESUS, but further studies are needed to establish how they compare with Holter monitors and implantable loop recorder. [ABSTRACT FROM AUTHOR]

This study aimed to examine the mechanisms of the photoplethysmography (PPG) signal formation using Monte Carlo simulations of light transport in biological tissues and experimental observations. Based on a three-layer skin model in backscattering geometry, we sequentially simulated volumetric blood changes and the aggregation/disaggregation of erythrocytes in the dermal layer and estimated their contribution to the registered PPG signal. The calculations were conducted for two wavelengths: 525 nm and 810 nm. For green light, absorption predominates over scattering in the formation of a PPG signal, whereas, for near-infrared light, scattering prevails over absorption. This theoretical result was verified using the Modified Beer–Lambert law and clinical in vivo PPG data of seven healthy subjects. Changes in the size of the scatterers during erythrocyte aggregation and disaggregation can significantly contribute to the PPG signal at near-infrared light. Thus, for the green waveband, the classical volumetric model can be considered dominant in the PPG signal formation. In contrast, for the near-infrared range, both volumetric and aggregation effects must be considered as being approximately equal. [ABSTRACT FROM AUTHOR]

Finger photo pulse plethysmography is a simple, inexpensive and non-invasive method for measurement of arterial stiffness. The objective is to assess the correlation of arterial stiffness in low back pain subjects with lumbar disc degeneration. Thirty-four back pain patients of both sexes in age group of 30–65 were included. Anthropometric measures like height, body weight, body mass index (BMI) were included. Stiffness index (SI) and reflection index (RI) were measured from the digital volume pulse waveform. There was a negative correlation between SI/RI and no correlation between SI and RI with BMI in both sexes. A significant correlation found between weight and BMI in both sexes. Arterial stiffness may not have any influence on disc degeneration. BMI showed some influence on disc degeneration and back pain. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) is the gold standard for monitoring vital signs and for diagnosing, controlling, and preventing cardiovascular diseases (CVDs). However, ECG requires continuous user participation, and cannot be used for continuous cardiac monitoring. In contrast to ECG, photoplethysmography (PPG) devices do not require continued user involvement, and can offer ongoing and long-term detection capabilities. However, from a medical perspective, ECG can provide more information about the heart. Currently, most existing work contains different signals recorded from the same subject in training and test sets. This study proposes a neural network model based on a 1D convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. This neural network model can directly reconstruct ECG signals from PPG signals. The learned features are captured from the CNN model and fed into the BiLSTM model. In order to verify the validity of the model, it is evaluated using the MIMIC II dataset in the completely subject-independent model (records are placed in a training set, and a test set appears once, but the test signal belongs to a record that is not in the training set). The Pearson's correlation coefficient between the reconstructed ECG and the reference ECG of the proposed model is 0.963 in the completely subject-independence model. The results of the proposed model are better than those of several cited state-of-the-art models. The results of our trained model indicate that we can obtain reconstructed ECGs that are highly similar to reference ECGs in the completely subject-independent model. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) is widely used to assess cardiovascular health. Yet, its effectiveness is often hindered by external factors like contact force (CF), which significantly affects the accuracy and reliability of measurements. This study investigates how variations in the CF at the index fingertips influence six signal quality indices (SQIs)—including the perfusion index, skewness, kurtosis, entropy, zero-crossing rate, and relative power—using data from 11 healthy participants. Our analysis of normalized CF values reveals that lower CF ranges (0.2 to 0.4) may be optimal for extracting information about perfusion and blood flow. However, they may not be the best range to capture all the physiological details within the PPG pulse. In contrast, higher CF ranges (0.4 to 0.6) enable capturing more complex signals that could be physiologically representative. The findings underscore the necessity of considering viscoelastic tissue properties and individual biomechanical differences, advocating for both the normalization of CF for improved cross-subject comparison and personalized CF calibration to adapt PPG devices to diverse populations. These strategies ensure measurement reliability and consistency, thereby advancing the accuracy of cardiac and vascular assessments. Our study offers guidelines for adjusting the CF levels to balance signal detail and perfusion quality, customized to meet specific analytical requirements, with direct implications for both clinical and research environments. [ABSTRACT FROM AUTHOR]

Introduction: Central aortic pressure waveform analyses can provide clinically relevant information beyond conventional brachial blood pressure (BP) assessment. This waveform can be reproduced noninvasively through application of a generalized transfer function (GTF) on a peripheral waveform, as conventionally performed by applanation tonometry. Photoplethysmography (PPG) is an alternate approach; however, differences in measurement site and modality demand the use of a transfer function (TF) specific for those differences. This study aimed to compare central aortic waveform features generated from radial tonometry (reference) using a proprietary GTF with a central aortic waveform and its features generated from a simultaneous fingertip PPG measurement using a selective method where one of three different TFs is chosen based on the input signal harmonic profile. Methods: Brachial BP was measured in triplicate under resting conditions and was used for subsequent calibration. Multiple simultaneous radial tonometry (SphygmoCor CVMS) and fingertip PPG measurements were then performed in individual participants (n = 21, 10 females, age: 39 ± 16 years). Measurements were converted into central aortic waveforms with their respective TFs. Twenty central aortic pressure waveform parameters were compared through correlation analysis, Bland-Altman plots, and a repeated measure mixed-effects ANOVA model. Central aortic waveform shape was compared using the root-mean-squared error (RMSE). Results: Correlation (r) of PPG-derived parameters with radially tonometry-derived central aortic parameters was high ranging from 0.79 to 0.99. Mean differences of pressure-related parameters were within 1.3 mm Hg, and differences of time-related parameters ranged from −2.2 to 3.4%. While some parameters were statistically different, these differences are not physiologically meaningful. Central aortic waveform shape had an average RMSE of 1.8 ± 0.9%. Conclusion: Fingertip PPG-derived central aortic waveform parameters using a novel selective TF were comparable to central aortic waveform features derived from radial tonometry using a previously validated GTF.