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Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12-15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.

Objective. Physiological data are often low quality and thereby compromises the effectiveness of related health monitoring. The primary goal of this study is to develop a robust foundation model that can effectively handle low-quality issue in physiological data.Approach. We introduce SiamQuality, a self-supervised learning approach using convolutional neural networks (CNNs) as the backbone. SiamQuality learns to generate similar representations for both high and low quality photoplethysmography (PPG) signals that originate from similar physiological states. We leveraged a substantial dataset of PPG signals from hospitalized intensive care patients, comprised of over 36 million 30 s PPG pairs.Main results. After pre-training the SiamQuality model, it was fine-tuned and tested on six PPG downstream tasks focusing on cardiovascular monitoring. Notably, in tasks such as respiratory rate estimation and atrial fibrillation detection, the models performance exceeded the state-of-the-art by 75% and 5%, respectively. The results highlight the effectiveness of our model across all evaluated tasks, demonstrating significant improvements, especially in applications for heart monitoring on wearable devices.Significance. This study underscores the potential of CNNs as a robust backbone for foundation models tailored to physiological data, emphasizing their capability to maintain performance despite variations in data quality. The success of the SiamQuality model in handling real-world, variable-quality data opens new avenues for the development of more reliable and efficient healthcare monitoring technologies.

Determining the life stage of animals is crucial for assessing their overall health and making informed decisions for their care. However, traditional veterinary practices often rely on subjective methods, resulting in fuzziness or ambiguity. A quantitative metric via measurable vital signs has yet to be developed, which could be due to lack of a sophisticated methodology that can correlate the vital signs with age. To tackle this challenge, we present a method for equine life stage (young to old) classification from photoplethysmography (PPG) waveforms collected from 50 equine subjects with various ages, sex, and breeds. The data were collected using compact, wearable PPG sensors on their tails. The collected waveforms served as the input for the classification by an echo state network (ESN). It was found that the classification accuracy depended on the age split, suggesting a fussiness in classifying the youth and elder. Using the 17th year as the splitting age, the highest training and testing accuracies of 81.3% and 81.1% were achieved, respectively. In addition, input features from the reservoirs of the ESN were extracted and analyzed by a kernel principal component analysis, which afforded a 3D PCA map with clear clusters according to the age groups. This suggests that the ESN can learn hidden information from the PPG waves to classify the life stage. The proposed algorithm exhibits immense potential in veterinary medicine by offering a more objective and reliable approach to animal life stage assessment. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications. [ABSTRACT FROM AUTHOR]

Inversion of the photoplethysmographic (PPG) signal is a rarely reported case. This signal anomaly can have implications for PPG-based cardiovascular assessments. The conditions for PPG signal inversion in the vicinity of the dorsalis pedis (DPA) artery of the foot were investigated. Wireless multi-wavelength PPG sensing with skin-probe contact pressure and local skin temperature were studied at different sensor positions, and the occurrence of inversion (OOI) was investigated. Twelve healthy adult volunteers were studied over four LED wavelengths at three levels of contact pressure for 11 probe positions. A novel algorithm quantified the proportion of inverted samples with respect to the abovementioned variables. Our algorithm classifying inverted vs. non-inverted pulses achieved 98.3% accuracy. Ten of the participants had at least one inverted signal identified. The impact of interindividual variation on inversion prevalence was large, but different LEDs, relative position to the DPA and sensor contact pressure also affected OOI. Skin surface and room temperatures showed no impact on OOI. Lateral measurements showed 39.6% more inversion at maximum compared to minimum contact pressure. Mechanical capillary bed variations and arterial reflections during venous engorgement are considered viable explanations for our observations. These findings motivate an expanded study of the occurrence of PPG signal inversion. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4 million individuals yearly. OHCA has a poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve OHCA survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential to "witness" cardiac arrest and activate emergency services. In this study, we used an arterial occlusion model to simulate cardiac arrest and investigated the ability of infrared photoplethysmogram (PPG) sensors, often utilized in consumer wearable devices, to differentiate normal cardiac pulsation, pulseless cardiac (i.e., resembling a cardiac arrest), and non-physiologic (i.e., off-body) states. Across the classification models trained and evaluated on three anatomical locations, higher classification performances were observed on the finger (macro average F1-score of 0.964 on the fingertip and 0.954 on the finger base) compared to the wrist (macro average F1-score of 0.837). The wrist-based classification model, which was trained and evaluated using all PPG measurements, including both high- and low-quality recordings, achieved a macro average precision and recall of 0.922 and 0.800, respectively. This wrist-based model, which represents the most common form factor in consumer wearables, could only capture about 43.8% of pulseless events. However, models trained and tested exclusively on high-quality recordings achieved higher classification outcomes (macro average F1-score of 0.975 on the fingertip, 0.973 on the finger base, and 0.934 on the wrist). The fingertip model had the highest performance to differentiate arterial occlusion pulselessness from normal cardiac pulsation and off-body measurements with macro average precision and recall of 0.978 and 0.972, respectively. This model was able to identify 93.7% of pulseless states (i.e., resembling a cardiac arrest event), with a 0.4% false positive rate. All classification models relied on a combination of time-, power spectral density (PSD)-, and frequency-domain features to differentiate normal cardiac pulsation, pulseless cardiac, and off-body PPG recordings. However, our best model represented an idealized detection condition, relying on ensuring high-quality PPG data for training and evaluation of machine learning algorithms. While 90.7% of our PPG recordings from the fingertip were considered of high quality, only 53.2% of the measurements from the wrist passed the quality criteria. Our findings have implications for adapting consumer wearables to provide OHCA detection, involving advancements in hardware and software to ensure high-quality measurements in real-world settings, as well as development of wearables with form factors that enable high-quality PPG data acquisition more consistently. Given these improvements, we demonstrate that OHCA detection can feasibly be made available to anyone using PPG-based consumer wearables. [ABSTRACT FROM AUTHOR]

(1) Background: Wearable sensors support healthcare professionals in clinical decision-making by measuring vital parameters such as heart rate (HR), respiration rate (RR), and blood oxygenation saturation (SpO2). This study assessed the validity and reliability of two types of wearable sensors, based on electrocardiogram or photoplethysmography, compared with continuous monitoring of patients recovering from trauma surgery at the postanesthesia care unit. (2) Methods: In a prospective observational study, HR, RR, SpO2, and temperature of patients were simultaneously recorded with the VitalPatch and Radius PPG and compared with reference monitoring. Outcome measures were formulated as correlation coefficient for validity and mean difference with 95% limits of agreement for reliability for four random data pairs and 30-min pairs per vital sign per patient. (3) Results: Included were 60 patients. Correlation coefficients for VitalPatch were 0.57 to 0.85 for HR and 0.08 to 0.16 for RR, and for Radius PPG, correlation coefficients were 0.60 to 0.83 for HR, 0.20 to 0.12 for RR, and 0.57 to 0.61 for SpO2. Both sensors presented mean differences within the cutoff values of acceptable difference. (4) Conclusions: Moderate to strong correlations for HR and SpO2 were demonstrated. Although mean differences were within acceptable cutoff values for all vital signs, only limits of agreement for HR measured by electrocardiography were considered clinically acceptable. [ABSTRACT FROM AUTHOR]

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of −0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring. [ABSTRACT FROM AUTHOR]

Photomultiplication‐type organic photodetectors (PM‐OPDs) effectively amplify photogenerated signals, eliminating the need for external amplifiers and simplifying detection systems. Current designs require high bias voltages, leading to significant power consumption. This study introduces PM‐OPDs with reduced bias voltage by incorporating a hole‐blocking layer (HBL) between the active layer and electrode. Deep HOMO level and thin HBL facilitate hole accumulation and energy level bending, lowering bias voltage and enhancing electron injection. The elevated LUMO level of the HBL minimizes non‐radiative recombination and prolongs the lifetime of accumulated holes, resulting in greater photogains. Four HBLs—BCP, PFN‐Br, BPhen, and C60—are explored with ZnO serving as an electron transporting layer (ETL). By applying a bi‐directional bias of ±0.5 V, PM‐OPDs are enabled to operate with an EQE of 19560% and a detectivity of 2.8 × 1013 Jones. The versatility of this mechanism is validated across three active layer combinations. Compared to commercial silicon photodetector, the flexible PM‐OPDs achieved a 139.3‐fold increase in photocurrent in photoplethysmography under faint light conditions. This work presents a promising strategy for developing bi‐directional biased PM‐OPDs with reduced power consumption while maintaining high gains for various practical applications. [ABSTRACT FROM AUTHOR]

The conventional medical assistive strategy for wearable devices involves sensing physiological signals and subsequent analysis while it lacks the capability for visual interaction and often necessitates the use of additional bulky devices for display. Herein, a perceptual and interaction strategy based on electroluminescent and triboelectric sensing 3D stacked wearable device (ETD) is proposed to address current issues. The ETD utilizes a bottom triboelectric sensor for tactile physiological information, which is processed and analyzed, ultimately triggering the selective illumination of the top electroluminescent array through external power circuitry. The ETD possesses the capability to generate a 100 V with 1.5 cm2 and stable visual interaction ability at 30 V drive voltage. Based on outstanding features of the ETD, two medical assistance scenarios are explored. I) The ETD captures physiological signals, such as pulse rate, respiratory rate, and special joint flexion, and in situ displays the corresponding physiological information, effectively assisting the patients/elders in self‐monitoring. II) A bidirectional interactive telemedicine assistive system utilizing ETDs is implemented, which not only gathers real‐time activity status from patients/elders but also realizes that remote physician assesses the safety status and the assessment results are wirelessly back‐communicated to the ETD, enabling real‐time interaction of the physician advice. [ABSTRACT FROM AUTHOR]

Advanced signal processing of photoplethysmographic data enables novel analyses which may improve the understanding of the pathogenesis of dysglycemia associated with sleep disorders. We aimed to identify sleep-related pulse wave characteristics in diabetic patients compared to normoglycemic individuals, independent of cardiovascular-related comorbidities. This cross-sectional evaluation of the population-based Swedish CArdioPulmonary bioImage Study (SCAPIS) included overnight oximetry-derived pulse wave data from 3997 subjects (45 % males, age 50–64 years). Metabolic status was classified as normoglycemic (n = 3220), pre-diabetic (n = 544), or diabetic (n = 233). Nine validated pulse wave features proposed to influence cardiovascular risk were derived and compared between metabolic status groups. Logistic prediction models and genetic matching were applied to capture diabetes-related pulse wave characteristics during sleep. The model was controlled for anthropometrics, lifestyle, sleep apnea, and in the final adjustment even for cardiometabolic factors like dyslipidaemia, hypertension, and coronary artery calcification. Pulse wave-derived parameters differed between normoglycemic and diabetic individuals in eight dimensions in unadjusted as well as in the partially adjusted model (anthropometric factors and sleep apnea, p ≤ 0.001). All covariates confirmed significant differences between normoglycemic and diabetic subjects (all p ≤ 0.001). Reduced cardio-respiratory coupling (respiratory-related pulse oscillations) (β = −0.010, p = 0.012), as well as increased vascular stiffness (shortened pulse propagation time (β = −0.015, p = 0.001), were independently associated with diabetes even when controlled for cardiometabolic factors. These results were confirmed through a matched cohort comparative analysis. Photoplethysmographic pulse wave analysis during sleep can be utilized to capture multiple features of modified autonomic regulation and cardiovascular consequences in diabetic subjects. Dampened heart rate variability and increased vascular stiffness during sleep showed the strongest associations with diabetes. • Several characteristics of the nocturnal pulse wave signal (PPG) are associated with diabetes. • Elevated vascular stiffness and reduced heart rate modulation identified in diabetic subjects. • Overnight PPG may detect diabetes-related consequences of metabolic dysfunction during sleep. [ABSTRACT FROM AUTHOR]

Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18–64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = –.27, p =.03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98–1.00 vs. 0.75–0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95–0.99 vs. 0.85–0.98; concurrent validity: 0.95–1.00 vs. 0.75–0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99–1.00 vs. 0.89–0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest. [ABSTRACT FROM AUTHOR]

Postoperative bleeding is the most significant complication of tonsillectomy. Regular monitoring of post‐surgical wound healing in the pharynx is required. For this purpose, we propose endoscope‐based non‐invasive perfusion mapping and quantification. The combination of imaging photoplethysmography and image processing provides automated wound area selection and microcirculation characterization. In this feasibility study, we demonstrate the first results of the proposed approach to wound monitoring in clinical trial on eight patients after tonsillectomy. Combination of probe‐based optical system and image processing algorithms can provide the valuable and consistent data on perfusion distribution. The quantitative microcirculation data obtained 1, 4, and 7 days after surgery are in good agreement with existing monitoring protocols. [ABSTRACT FROM AUTHOR]

The fabrication and characterization of an all polymers‐based bulk heterojunction type organic photodiode having a narrowband response in the near‐infrared spectral region with full width at half‐maxima of 63 nm is reported. The active layer of the photodiode constitutes a 1:1 (by weight) blend of poly[4,8‐bis(5‐(2‐ethylhexyl)thiophen‐2‐yl)benzo[1,2‐b;4,5‐b']dithiophene‐2,6‐diyl‐alt‐(4‐(2‐ethylhexyl)‐3‐fluorothieno[3,4‐b]thiophene‐)‐2‐carboxylate‐2‐6‐diyl)] (PCE10) and poly{[N,N′‐bis(2‐octyldodecyl)‐naphthalene‐1,4,5,8‐bis(dicarboximide)‐2,6‐diy1]‐alt‐5,5′‐(2,2′‐dithiophene)} (N2200) deposited using solution processing. The device exhibits a linear dynamic range of 35 dB, response speed of 893 kHz, and specific‐detectivity of 109 Jones at 808 nm excitation wavelength. The narrowband response is achieved using the charge collection narrowing mechanism in a 2.7 μm thick junction device. [ABSTRACT FROM AUTHOR]

Purpose: To introduce a simple system exploitation with the potential to turn MRI scanners into general‐purpose radiofrequency (RF) motion monitoring systems. Methods: Inspired by Pilot Tone (PT), this work proposes Beat Pilot Tone (BPT), in which two or more RF tones at arbitrary frequencies are transmitted continuously during the scan. These tones create motion‐modulated standing wave patterns that are sensed by the receiver coil array, incidentally mixed by intermodulation in the receiver chain, and digitized simultaneously with the MRI data. BPT can operate at almost any frequency as long as the intermodulation products lie within the bandwidth of the receivers. BPT's mechanism is explained in electromagnetic simulations and validated experimentally. Results: Phantom and volunteer experiments over a range of transmit frequencies suggest that BPT may offer frequency‐dependent sensitivity to motion. Using a semi‐flexible anterior receiver array, BPT appears to sense cardiac‐induced body vibrations at microwave frequencies (≥$$ \ge $$1.2 GHz). At lower frequencies, it exhibits a similar cardiac signal shape to PT, likely due to blood volume changes. Other volunteer experiments with respiratory, bulk, and head motion show that BPT can achieve greater sensitivity to motion than PT and greater separability between motion types. Basic multiple‐input multiple‐output (4×22$$ 4\times 22 $$ MIMO) operation with simultaneous PT and BPT in head motion is demonstrated using two transmit antennas and a 22‐channel head‐neck coil. Conclusion: BPT may offer a rich source of motion information that is frequency‐dependent, simultaneous, and complementary to PT and the MRI exam. [ABSTRACT FROM AUTHOR]

Several people must frequently evaluate their blood glucose since it is an important indicator of health problems mainly diabetes. Different medical systems are commercialized to measure blood glucose levels; some are invasive others are noninvasive. The main purpose of this article is to develop a non-invasive device for measuring blood glucose levels based on the detection and analysis of the photoplethysmogram signal. The developed systems include an optical sensor to detect the photoplethysmography (PPG) signal, digitalizing and acquiring boards to a computer and a software program to process and analyze the digitalized PPG signal regarding some features extracted from its waveform. These features are the systolic amplitude Sa and the b/a amplitude ratio in the second derivative PPG (SDPPG) waveform. An invasive glucometer is also used along with the Sa and b/a ratio determined from the developed system to generate a calibration model which is used to deduce blood glucose level (BGL) values. The result showed that the calibration model using the b/a ratio is more accurate for non-invasive blood level measurement then that of Sa with a difference in glucose estimation around 2 mg/dl and with the correlation coefficient (R²) of the glucose level prediction between 0.8904 and 0.9775. [ABSTRACT FROM AUTHOR]

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

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads. [ABSTRACT FROM AUTHOR]

Mixed Reality (MR) environments hold immense potential for inducing a sense of embodiment, where users feel like their bodies are present within the virtual space. This subjective experience has been traditionally assessed using subjective reports and behavioral measures. However, neurophysiological approaches offer unique advantages in objectively characterizing embodiment. This review article explores the current state of the art in utilizing neurophysiological techniques, particularly Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal activity (EDA), to investigate the neural and autonomic correlates of embodiment in MR for out-of-the-lab applications. More specifically, it was investigated how EEG, with its high temporal resolution, PPG, and EDA, can capture transient brain activity associated with specific aspects of embodiment, such as visuomotor synchrony, visual feedback of a virtual body, and manipulations of virtual body parts. The potential of such neurophysiological signals to differentiate between subjective experiences of embodiment was discussed, with a particular regard to identify the neural and autonomic markers of early embodiment formation during MR exposure in real settings. Finally, the strengths and limitations of the neurophysiological approach in the context of MR embodiment research were discussed, in order to achieve a more comprehensive understanding of this multifaceted phenomenon. [ABSTRACT FROM AUTHOR]

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]

Ballistocardiography (BCG) studies ballistic forces on the body generated during a heartbeat. As it is an unobtrusive detection method, that requires sensors measuring small dynamic forces. Piezoelectric sensors are ideal, but improvements in sensitivity are needed. Here, a universal method is demonstrated to obtain enhanced effective transverse charge constants by utilizing thin and long sensors fabricated upon compliant substrates. This approach is validated using the uniquely printable and patternable piezoelectric polymer, poly(vinylidene fluoride‐co‐trifluoroethylene) (P(VDF‐TrFE). Discrete sensors detect displacements of less than 1 µm, dynamic forces of ∼mN, and accelerations of ≈1 mm−1 s2. Since the sensor is screen‐printable, it is upscaled to a human‐sized array (60 × 90 cm, 255 sensors). High quality spatially resolved BCG measurements of a person is demonstrated in seated and supine positions. The obtained heart rate is verified using photoplethysmography. This development opens the door to ever‐more sensitive piezoelectric sensors, crucial for applications including and beyond healthcare. [ABSTRACT FROM AUTHOR]

Background: Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) infection can cause feared consequences, such as affecting microcirculatory activity. The combined use of HRV analysis, genetic algorithms, and machine learning classifiers can be helpful in better understanding the characteristics of microcirculation that are mainly affected by COVID-19 infection. Methods: This study aimed to verify the presence of microcirculation alterations in patients with COVID-19 infection, performing Heart Rate Variability (HRV) parameters analysis extracted from PhotoPlethysmoGraphy (PPG) signals. The dataset included 97 subjects divided into two groups: healthy (50 subjects) and patients affected by mild-severity COVID-19 (47 subjects). A total of 26 parameters were extracted by the HRV analysis and were investigated using genetic algorithms with three different subject selection methods and five different machine learning classifiers. Results: Three parameters: meanRR, alpha1, and sd2/sd1 were considered significant, combining the results obtained by the genetic algorithm. Finally, machine learning classifications were performed by training classifiers with only those three features. The best result was achieved by the binary Decision Tree classifier, achieving accuracy of 82%, specificity (or precision) of 86%, and sensitivity of 79%. Conclusions: The study's results highlight the ability to use HRV parameters extraction from PPG signals, combined with genetic algorithms and machine learning classifiers, to determine which features are most helpful in discriminating between healthy and mild-severity COVID-19-affected subjects. [ABSTRACT FROM AUTHOR]

This study evaluated the relationship of non-invasive arterial stiffness parameters with an individual 10-year risk of fatal and non-fatal atherosclerotic cardiovascular disease (ASCVD) events in the cohort post-coronavirus disease 2019 (COVID-19). The study group included 203 convalescents aged 60.0 (55.0–63.0) and 115 (56.7%) women. The ASCVD risk was assessed as low to moderate to very high based on medical history (for 62 participants with pre-existing ASCVD/diabetes/chronic kidney disease in the entire cohort) or calculated in percentages using the Systemic Coronary Risk Evaluation 2 (SCORE2) algorithm based on age, sex, smoking status, systolic blood pressure (BP), and non-high-density lipoprotein cholesterol (for 141 healthy participants). The stiffness index (SI) and reflection index (RI) measured by photoplethysmography, as well as pulse pressure (PP), calculated as the difference between systolic and diastolic BP, were markers of arterial stiffness. Stiffness parameters increased significantly with the increase in ASCVD risk in the entire cohort. In 30 (14.8%) patients in the low- to moderate-risk group, the median SI was 8.07 m/s (7.10–8.73), RI 51.40% (39.40–65.60), and PP 45.50 mmHg (40.00–57.00); in 111 (54.7%) patients in the high-risk group, the median SI was 8.70 m/s (7.40–10.03), RI 57.20% (43.65–68.40), and PP 54.00 mmHg (46.00–60.75); and in 62 (30.5%) patients in the very-high-risk group, the median was SI 9.27 m/s (7.57–10.44), RI 59.00% (50.40–72.40), and PP 60.00 mmHg (51.00–67.00). In healthy participants, the SI ≤ 9.0 m/s (sensitivity of 92.31%, area under the curve [AUC] 0.686, p < 0.001) based on the receiver operating characteristics was the most sensitive variable for discriminating low to moderate risk, and PP > 56.0 mmHg (sensitivity of 74.36%, AUC 0.736, p < 0.001) was used for discriminating very high risk. In multivariate logistic regression, younger age, female sex, PP ≤ 50 mmHg, SI ≤ 9.0 m/s, and triglycerides < 150 mg/dL had the best relationship with low to moderate SCORE2 risk. In turn, older age, currently smoking, PP > 56.0 mmHg, RI > 68.6%, and diastolic BP ≥ 90 mmHg were related to very high SCORE2 risk. In conclusion, arterial stiffness is significantly related to ASCVD risk in post-COVID-19 patients and can be helpful as a single risk marker in everyday practice. Cut-off points for arterial stiffness parameters determined based on SCORE2 may help make individual decisions about implementing lifestyle changes or pharmacological treatment of ASCVD risk factors [ABSTRACT FROM AUTHOR]

Simple Summary: The venoarteriolar reflex (VAR) consists of a local vasoconstriction occurring in response to an increase in venous transmural pressure. Its underlying mechanisms, including its impact on contralateral perfusion, still need clarification. In this study we assessed VAR in a group of healthy subjects by performing a unilateral limb dependency procedure. During dependency skin perfusion decreased significantly in both the dependent and the contralateral hands. Cutaneous sympathetic activity increased during dependency and upon returning to the initial position. These results corroborate previous reports that unilateral limb dependency impacts contralateral perfusion and suggest the participation of the sympathetic nervous system. This sympathetic activation seems to occur in response to the postural change itself and does not seem to be related to the VAR. The venoarteriolar reflex (VAR) is described as a vasoconstriction occurring in response to an increase in venous transmural pressure. Its underlying mechanisms are still not clarified, particularly the neural pathway that supposedly evokes this reflex. In addition, recent studies have shown that the postural maneuvers that evoke VAR also produce a decrease in contralateral perfusion, which is also poorly understood. Our study aimed to explore the contralateral response to unilateral upper limb dependency and its underlying mechanisms. Fifteen young, healthy subjects (24.1 ± 5.8 y.o.) participated in this study after giving informed consent. While seated, subjects remained for 7 min with both arms at heart level (baseline), after which a random hand was placed 40 cm below the heart level for 5 min (dependency) before resuming the initial position for another 7 min (recovery). Skin perfusion was assessed bilaterally with photoplethysmography, and electrodermal activity (EDA) was assessed in the contralateral hand. During hand dependency, perfusion decreased significantly in both limbs, although it was more pronounced in the dependent limb, corroborating previous reports that unilateral limb dependency evokes a decrease in contralateral perfusion. Transient EDA peaks were detected in the first seconds of the dependency and recovery phases. These results support the participation of the sympathetic nervous system as a mechanism regulating contralateral perfusion during unilateral limb dependency. This sympathetic activation is probably attributed to the postural changes themselves and is likely not related to the VAR. [ABSTRACT FROM AUTHOR]

A cleanroom free optimized fabrication of a low-cost facile tungsten diselenide (WSe2) combined with chitosan-based hydrogel device is reported for multifunctional applications including tactile sensing, pulse rate monitoring, respiratory rate monitoring, human body movements detection, and human electrophysiological signal detection. Chitosan being a natural biodegradable, non-toxic compound serves as a substrate to the semiconducting WSe2 electrode which is synthesized using a single step hydrothermal technique. Elaborate characterization studies are performed to confirm the morphological, structural, and electrical properties of the fabricated chitosan/WSe2 device. Chitosan/WSe2 sensor with copper contacts on each side is put directly on skin to capture human body motions. The resistivity of the sample was calculated as 26 kΩ m−1. The device behaves as an ultrasensitive pressure sensor for tactile and arterial pulse sensing with response time of 0.9 s and sensitivity of around 0.02 kPa−1. It is also capable for strain sensing with a gauge factor of 54 which is significantly higher than similar other reported electrodes. The human body movements sensing can be attributed to the piezoresistive character of WSe2 that originates from its non-centrosymmetric structure. Further, the sensor is employed for monitoring respiratory rate which measures to 13 counts/min for healthy individual and electrophysiological signals like ECG and EOG which can be used later for detecting numerous pathological conditions in humans. Electrophysiological signal sensing is carried out using a bio-signal amplifier (Bio-Amp EXG Pill) connected to Arduino. The skin-friendly, low toxic WSe2/chitosan dry electrodes pave the way for replacing wet electrodes and find numerous applications in personalized healthcare. [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]

The monitoring of hemodynamic parameters, such as heart rate and blood pressure, provides valuable indications of overall cardiovascular health. It is preferable that such monitoring is non-invasive and in real time via an affordable, compact and small-scale device for maximum convenience. Numerous literature sources have exploited derivations of these parameters from photoplethysmogram (PPG) and electrical bioimpedance (EBI) signal measurements through the use of calculation algorithms of varying complexity. Compared to electrocardiogram (ECG), these measurement techniques have a merit of well-established practices of designing a wearable device that could conveniently be put on a wrist. The current paper provides a comprehensive review on the use of PPG and EBI measurement techniques in the context of hemodynamic parameter monitoring using a wearable device. A special emphasis is placed on the most basic hemodynamic parameter—heart rate—describing different algorithms of heart rate detection and monitoring. The last section provides an overview of commercially available and in-home wearable device technologies based on PPG and EBI measurements, their design challenges, and future prospects. [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]

To diagnose diabetes early or to maintain stable blood glucose levels in diabetics, blood glucose levels should be frequently checked. However, the only way to check blood glucose levels regularly is to use invasive methods, such as pricking the fingertip or using a minimally invasive patch. These invasive methods pose several problems, including being painful and potentially causing secondary infections. This study focuses on noninvasively measuring glycated hemoglobin (HbA1c) using PPG signals. In particular, the study relates to a method and a hardware design technology for removing noise that may be present in a PPG signal due to skin contact with a noninvasive HbA1c measurement device. The proposed HbA1c measurement device consists of the first sensor (PPG sensor) module including an optical barrier and the second sensor (cylindrical sensor) module for removing the skin effect. We have developed a Monte Carlo method to implement accurate, noninvasive HbA1c measurement by considering different skin properties among different subjects. Implementing this model in wearable devices will allow end users to not only monitor their glycated hemoglobin levels but also control diabetes with higher accuracy without needing any blood samples. This will be a groundbreaking advancement in modern wearable medical devices. [ABSTRACT FROM AUTHOR]

Abstract Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4 million individuals yearly. OHCA has a poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve OHCA survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential to “witness” cardiac arrest and activate emergency services. In this study, we used an arterial occlusion model to simulate cardiac arrest and investigated the ability of infrared photoplethysmogram (PPG) sensors, often utilized in consumer wearable devices, to differentiate normal cardiac pulsation, pulseless cardiac (i.e., resembling a cardiac arrest), and non-physiologic (i.e., off-body) states. Across the classification models trained and evaluated on three anatomical locations, higher classification performances were observed on the finger (macro average F1-score of 0.964 on the fingertip and 0.954 on the finger base) compared to the wrist (macro average F1-score of 0.837). The wrist-based classification model, which was trained and evaluated using all PPG measurements, including both high- and low-quality recordings, achieved a macro average precision and recall of 0.922 and 0.800, respectively. This wrist-based model, which represents the most common form factor in consumer wearables, could only capture about 43.8% of pulseless events. However, models trained and tested exclusively on high-quality recordings achieved higher classification outcomes (macro average F1-score of 0.975 on the fingertip, 0.973 on the finger base, and 0.934 on the wrist). The fingertip model had the highest performance to differentiate arterial occlusion pulselessness from normal cardiac pulsation and off-body measurements with macro average precision and recall of 0.978 and 0.972, respectively. This model was able to identify 93.7% of pulseless states (i.e., resembling a cardiac arrest event), with a 0.4% false positive rate. All classification models relied on a combination of time-, power spectral density (PSD)-, and frequency-domain features to differentiate normal cardiac pulsation, pulseless cardiac, and off-body PPG recordings. However, our best model represented an idealized detection condition, relying on ensuring high-quality PPG data for training and evaluation of machine learning algorithms. While 90.7% of our PPG recordings from the fingertip were considered of high quality, only 53.2% of the measurements from the wrist passed the quality criteria. Our findings have implications for adapting consumer wearables to provide OHCA detection, involving advancements in hardware and software to ensure high-quality measurements in real-world settings, as well as development of wearables with form factors that enable high-quality PPG data acquisition more consistently. Given these improvements, we demonstrate that OHCA detection can feasibly be made available to anyone using PPG-based consumer wearables.

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.

Abstract 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.

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]

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]

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]

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]

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]

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]

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]