Volume Pulse Research Articles

Improved Vanadium Flow Battery Performance through a Pulsating Electrolyte Flow Regime

AbstractThe interest in flow batteries as energy storage devices is growing due to the rising share of intermittent renewable energy sources. In this work, the performance of a vanadium flow battery is improved, without altering the electrochemical cell, by applying a pulsating flow. This novel flow battery flow regime aims to enhance performance by improving its mass transfer properties. Both the pulse volume and frequency have an influence on the battery performance, and lead to a 38.7 % increase in accessible discharge capacity compared to the conventional steady flow regime at values of 0.40 cm3 and 2.4 Hz in this cell, respectively. Furthermore, at these parameter settings a reduction of the mass transfer resistance by 71.4 % is achieved at 0.2 cm3/min ⋅ cm2. These results show that a pulsating flow can be beneficial in certain conditions, opening new possibilities for boosting performance in flow batteries.

Can peripheral psychophysiological markers predict response to exposure-based cognitive behavioral therapy in youth with severely impairing irritability? A study protocol

BackgroundIrritability, an increased proneness to anger, is a primary reason youth present for psychiatric care. While initial evidence supports the efficacy of exposure-based cognitive behavioral therapy (CBT) for youth with clinically impairing irritability, treatment mechanisms remain unclear. Here, we propose to measure peripheral psychophysiological indicators of arousal—heart rate (HR)/electrodermal activity (EDA)—and regulation—heart rate variability (HRV)—during exposures to anger-inducing stimuli as potential predictors of treatment efficacy. The objective of this study is to evaluate whether in-situ biosensing data provides peripheral physiological indicators of in-session response to exposures.MethodsBlood volume pulse (BVP; from which HR and HRV canl be derived) and EDA will be collected ambulatorily using the Empatica EmbracePlus from 40 youth (all genders; ages 8-17) undergoing six in-person exposure treatment sessions, as part of a multiple-baseline trial of exposure-based CBT for clinically impairing irritability. Clinical ratings of irritability will be conducted at baseline, weekly throughout treatment, and at 3-month and 6-month follow-ups via the Clinical Global Impressions Scale (CGI) and the Affective Reactivity Index (ARI; clinician-, parent-, and child-report). Multilevel modeling will be used to assess within- and between-person changes in physiological arousal and regulation throughout exposure-based CBT and to determine whether individual differences are predictive of treatment response.DiscussionThis study protocol leverages a wearable biosensor (Empatica) to continuously record HR/HRV (derived from BVP) and EDA during in-person exposure sessions for youth with clinically impairing irritability. Here, the goal is to identify changes in physiological arousal (EDA, HR) and regulation (HRV) over the course of treatment in tandem with changes in clinical symptoms.Trial registrationThe participants in this study come from an overarching clinical trial (trial registration numbers: NCT02531893 first registered on 8/25/2015; last updated on 8/25/2023). The research project and all related materials were submitted and approved by the appropriate Institutional Review Board of the National Institute of Mental Health (NIMH).

Feasibility and Safety of the Routine Distal Transradial Approach in the Anatomical Snuffbox for Coronary Procedures: The ANTARES Randomized Trial.

The distal transradial approach (dTRA) through the anatomical snuffbox is hypothesized to offer greater benefits than the conventional transradial access (cTRA) for patients undergoing coronary procedures. Our goal was to assess the safety and efficacy of dTRA. Out of 465 consecutive Caucasian patients, 400 were randomized (1:1) to dTRA or cTRA in a prospective single-center trial. Clinical and ultrasound follow-ups were obtained at 24 h and 60 days post-procedure. The primary combined endpoint consisted of access crossover, access-related complications, and major adverse cardiovascular events (MACE). Secondary endpoints included clinical success endpoints (puncture success, crossover, and access time), access-site complications endpoints, and MACE at 60 days. The primary endpoint was significantly higher in the dTRA [odds ratio (OR): 2.31, 95% confidence interval (CI): 1.38-3.86, p = 0.001]. Clinical success endpoints, namely crossover (10% vs. 3.5%, p < 0.05) and access-time [median: 140s (85-322) vs. 80s (58-127), p < 0.001], did not favor the dTRA, despite a similar success rate in radial artery puncture between the dTRA and cTRA (99.5% vs. 99%). Radial artery spasm (19% vs. 4.5%, p < 0.0001), physical discomfort during access, and transient thumb numbness after the procedure occurred more frequently with the dTRA. However, early (2.5% vs. 4.5%, p = 0.41) and mid-term (2.5% vs. 3%, p = 0.98) forearm radial artery occlusion rates were comparable between the dTRA and cTRA. Randomization to the dTRA, lower forearm radial pulse volume, higher body mass index, and lower body surface area independently predicted the primary endpoint in multivariate analysis. In the interaction effect analysis, only diabetes increased the incidence of the primary endpoint with the dTRA (OR: 18.67, 95% CI: 3.96-88.07). The dTRA was a less favorable strategy than cTRA during routine coronary procedures due to a higher incidence of arterial spasm and the necessity for access crossover. The majority of local complications following the dTRA were clinically minor complications. Individuals with diabetes were particularly susceptible to complications associated with the dTRA.

HYDRAULIC CHARACTERISTICS AND CONSUMPTION PARAMETERS OF PIPELINE FOR MULTIPHASE FLOWS

Multiphase flows are found in almost all facilities and objects of the oil industry - oil and gas wells, oil transportation and storage facilities, oil refineries and petrochemical enterprises. Accurate modeling of the hydrodynamics of these processes is required to predict the productivity and operational efficiency of both hydrocarbon deposits and pipelines [2,7,8,10] . However, developing a reliable model for the correct modeling of processes is a very complex issue. Therefore, it is understandable that carrying out laboratory experiments are preferred. According to theoretical assessments and experimental data on the joint transportation of gas, oil, and water through the same pipeline the resistance to flow and the energy required for transportation will be significantly reduced if not oil, water is in contact with the pipe during flow, (although the pipeline's discharge capacity increases due to the addition of water) [8,10]. Experiments demonstrate that the high hydraulic resistance of the pipeline at ambient temperature makes it inefficient to transport high viscosity and paraffinic oils—which freeze at higher temperatures—as well as oil products using conventional methods. There are various multiphase flow structures that can be achieved when transporting oil and water together, regardless of whether the mixing is natural or artificial (for example, coaxial, emulsified, phase-separated, etc.), The coaxial structure forms because the water around the oil keeps it from coming into contact with the pipe wall. Since the contact of the water phase with the inner surface greatly reduces the friction, it significantly facilitates the transportation of the oil water mixture. Transport of multiphase mixtures is often accompanied by phase transformation, which leads to blockages caused by the gas liquid or solid phase is the collection or transport systems. Pipeline plugging results in a variety of technological complications and extra expenses incurred to eliminate their consequences. Most often phase imbalance at specific pressure values and a sudden shift in the carried product's viscosity lead to the formation of difficulties [2]. The article's primary goal is to investigate the hydraulic properties of pipes for multiphase flows. The primary techniques for the combined transportation of gas and oil, as well as an examination of the primary characteristics and structural configurations of multiphase flows. Keywords: multiphase flows, pipelines, consumption, pressure, temperature, volume, hydraulic characteristics, and pressure pulses.

Peripheral artery disease diagnosis based on deep learning-enabled analysis of non-invasive arterial pulse waveforms

This paper intends to investigate the feasibility of peripheral artery disease (PAD) diagnosis based on the analysis of non-invasive arterial pulse waveforms. We generated realistic synthetic arterial blood pressure (BP) and pulse volume recording (PVR) waveform signals pertaining to PAD present at the abdominal aorta with a wide range of severity levels using a mathematical model that simulates arterial blood circulation and arterial BP-PVR relationships. We developed a deep learning (DL)-enabled algorithm that can diagnose PAD by analyzing brachial and tibial PVR waveforms, and evaluated its efficacy in comparison with the same DL-enabled algorithm based on brachial and tibial arterial BP waveforms as well as the ankle-brachial index (ABI). The results suggested that it is possible to detect PAD based on DL-enabled PVR waveform analysis with adequate accuracy, and its detection efficacy is close to when arterial BP is used (positive and negative predictive values at 40 % abdominal aorta occlusion: 0.78 vs 0.89 and 0.85 vs 0.94; area under the ROC curve (AUC): 0.90 vs 0.97). On the other hand, its efficacy in estimating PAD severity level is not as good as when arterial BP is used (r value: 0.77 vs 0.93; Bland-Altman limits of agreement: −32%-+32 % vs −20%-+19 %). In addition, DL-enabled PVR waveform analysis significantly outperformed ABI in both detection and severity estimation. In sum, the findings from this paper suggest the potential of DL-enabled non-invasive arterial pulse waveform analysis as an affordable and non-invasive means for PAD diagnosis.

A Cloud-Based Assessment of Arterial Stiffness Through Contour Analysis of A Photoplethysmography

&lt;p&gt;Cardiovascular diseases (CVDs) are the leading cause of mortality globally. To effectively prevent CVDs, a variety of techniques have been employed to evaluate the mechanical properties of arteries, among which, aortic stiffness measured by aortic pulse wave velocity (PWV) has been proven to be an independent predictor of CVDs. However, the traditional way to measure PWV is complex and time consuming. Recent studies suggest the digital volume pulse (DVP) waveform to be an effective non-invasive method to obtain PWV. In this study, we present a cloud computing system that analyzes and calculates the relevant indices of arterial stiffness after receiving the measured DVP signals. The result of the analysis can be retrieved online for the user to view or download for further analysis. With this technique, arterial Stiffness Index (SI) for population can be obtained easily and inexpensively. This will help health authorities to do mass screening at population level and, hence, establish references of arterial SI for different cohorts by age, gender, ethnicity, and diseases.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

P64: Wearable sensing technology for Parkinson’s disease: preliminary results from the DIGI.PARK pilot

Background:Assessment scales for motor symptoms in Parkinson’s disease (PD) lack the sensitivity and resolution to monitor symptoms over time. Wearable sensors in people with PD have shown potential to assess motor symptoms. The DIGI.PARK study explores the use of consumer- and research-grade wearables such as Fitbit Sense (FS), Oura ring (OR) and Empatica E4 (EM) to track behavioral patterns and symptoms of PD over time.Method:The DIGI.PARK pilot study (12.2021 to 12.2022) included N = 30 participants living in Bergen, Norway (N=15 persons with PD and N=15 controls). Outcome measures: self-reported diary of symptoms and behavior combined with data streams from three wearable devices (FS, OR, EM). Data was collected over 2 weeks: continuously by devices, and diary data every second day consisting of activities, sleep, medication timing (PD) and symptom occurrence (PD). The device data were segmented into 24-hour epochs. Heart rate (HR), heart rate variability (HRV), acceleration, blood volume pulse (BVP), inter-beat interval (IBI), electrodermal activity, metabolic equivalent of task (MET) and hypnogram were visualized as time series. The resulting graphs were annotated with the reported diary data and a manual checking procedure was applied to determine the correlation between sensor outputs and the logged instances of activity, sleep and symptoms.Results:Self-reported behavior was discernable in the measurements of HR, EDA, BVP, HRV, acceleration, MET and hypnogram. We found considerable differences in device outputs regarding data type, data size, resolution, and periods of active measurements. Tremor symptoms were observable in the raw data provided by EM when worn on the affected hand. Behavioral patterns such as sleep, waking and physical activities were illustrated using aggregated data.Conclusion:Sensor congruence with diary data support their usefulness for long term monitoring of behavioral patterns and symptoms in PD. For PD research, output from consumer- and research-grade devices have both shown usefulness. The choice of device should be tailored to the purpose and be mindful of the specific strengths and weaknesses of different device types. Aggregated data allow for monitoring behavioral patterns over time, whereas raw data provided the resolution to discern symptoms.

Virtual reality exposure aided-diagnosis system for anxiety disorders: Long short-term memory architecture for three levels of anxiety recognition.

The COVID-19 pandemic has resulted in increased psychological pressure on mental health since 2019. The resulting anxiety and stress have permeated every aspect of life during confinement. To provide psychologists with an unbiased measure that can aid in the preliminary diagnosis of anxiety disorders and be used as an initial treatment in cognitive-behavioral therapy, this article introduces automated recognition of three levels of anxiety. Anxiety was elicited by exposing participants to virtual environments inspired by social situations in reference to the Liebowitz social anxiety scale. Relevant parameters, such as heart rate variability and vasoconstriction were derived from the measurement of the blood volume pulse (BVP) signal. A long short-term memory architecture achieved an accuracy of approximately 98% on the training and test set. The generated model allowed for careful study of the state of seven phobic participants during virtual reality exposure (VRE).

Abstract 13947: Racial Differences in Peripheral Microvasculature's Association With Mild Cognitive Impairment

Introduction: Cardiovascular disease (CVD) and Alzheimer’s Dementia (AD) share numerous risk factors, with vascular dysfunction a common underlying mechanism. Individuals with mild cognitive impairment (MCI) have a high likelihood of developing AD in their lifetime. The link between microvascular dysfunction (MVD) and AD has yet to be fully established. Hypothesis: We examined the contribution of MVD to MCI with the hypothesis that Black Americans, who have a higher prevalence of MVD, will have higher rates of MCI. Methods: Participants &gt;50 years with MCI or with normal cognition (NC) were enrolled for neuropsychological and microvascular function testing using peripheral arterial tonometry. Microvascular function was measured as the reactive hyperemia index (RHI) and calculated as the ratio of the post- to pre-occlusion pulse volume in the fingertip (EndoPAT). Multivariate analyses, adjusting for demographic and CVD risk factors associated with MCI, evaluated the relationship between RHI and MCI in Black and White participants. Causal mediation analysis, using the Monte Carlo approach, was performed to investigate whether the effect of RHI on MCI was mediated through plausible mediators, including risk factors. Results: Of 152 participants (NC=104, MCI=48), 41.4% were Black and 33.6% male. Participants with MCI were more likely to be Black (58.3% vs 33.7%%, p=0.007) and have lower RHI (1.93 vs 2.27, p=0.004) compared to NC. RHI was an independent predictor of MCI with a 96% adjusted greater odds of MCI per unit reduction in RHI. Black race remained an independent predictor of MCI (OR 2.90, 95% CI [1.16, 7.56]). RHI was lower in Black compared to White participants (1.86 vs 2.37, p&lt;0.001). There was no heterogeneity in the impact of RHI on MCI. In a casual mediation analysis, MCI was mediated via RHI more in Black than in White participants (proportion mediated 24.2% vs 18.4%, p=0.034) after multivariate adjustment. Conclusions: Peripheral MVD, measured as RHI, was independently associated with MCI. Furthermore, Black participants exhibited lower RHI levels, a higher prevalence of MCI, and potentially mediated MCI through RHI to a greater extent compared to their White counterparts.

Automatic detection of artifacts and improved classification models for emotional activity detection from multimodal physiological data

This manuscript proposes an automated artifacts detection and multimodal classification system for human emotion analysis from human physiological signals. First, multimodal physiological data, including the Electrodermal Activity (EDA), electrocardiogram (ECG), Blood Volume Pulse (BVP) and respiration rate signals are collected. Second, a Modified Compressed Sensing-based Decomposition (MCSD) is used to extract the informative Skin Conductance Response (SCR) events of the EDA signal. Third, raw features (edge and sharp variations), statistical and wavelet coefficient features of EDA, ECG, BVP, respiration and SCR signals are obtained. Fourth, the extracted raw features, statistical and wavelet coefficient features from all physiological signals are fed into the parallel Deep Convolutional Neural Network (DCNN) to reduce the dimensionality of feature space by removing artifacts. Fifth, the fused artifact-free feature vector is obtained for neutral, stress and pleasure emotion classes. Sixth, an artifact-free feature vector is used to train the Random Forest Deep Neural Network (RFDNN) classifier. Then, a trained RFDNN classifier is applied to classify the test signals into different emotion classes. Thus, leveraging the strengths of both RF and DNN algorithms, more comprehensive feature learning using multimodal psychological data is achieved, resulting in robust and accurate classification of human emotional activities. Finally, an extensive experiment using the Wearable Stress and Affect Detection (WESAD) dataset shows that the proposed system outperforms other existing human emotion classification systems using physiological data.

Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction from Faces.

Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurable. We identify and evaluate the possible choices in the critical steps of unsupervised rPPG methodologies. We assess a state-of-the-art processing pipeline in six different datasets, incorporating important corrections in the methodology that ensure reproducible and fair comparisons. In addition, we extend the pipeline by proposing three novel ideas; 1) a new method to stabilize the detected face based on a rigid mesh normalization; 2) a new method to dynamically select the different regions in the face that provide the best raw signals, and 3) a new RGB to rPPG transformation method, called Orthogonal Matrix Image Transformation (OMIT) based on QR decomposition, that increases robustness against compression artifacts. We show that all three changes introduce noticeable improvements in retrieving rPPG signals from faces, obtaining state-of-the-art results compared with unsupervised, non-learning-based methodologies and, in some databases, very close to supervised, learning-based methods. We perform a comparative study to quantify the contribution of each proposed idea. In addition, we depict a series of observations that could help in future implementations.

Brain blood flow pulse analysis may help to recognize individuals who suffer from hydrocephalus

BackgroundNormal pressure hydrocephalus (NPH) is often associated with altered cerebral blood flow. Recent research with the use of the ultrasonic method suggests specific changes in the shape of cardiac-related cerebral arterial blood volume (CaBV) pulses in NPH patients. Our study aims to provide a quantitative analysis of the shape of CaBV pulses, estimated based on transcranial Doppler ultrasonography (TCD) in NPH patients and healthy individuals.MethodsThe CaBV pulses were estimated using TCD cerebral blood flow velocity signals recorded from probable NPH adults and age-matched healthy individuals at rest. The shape of the CaBV pulses was compared to a triangular shape with 27 similarity parameters calculated for every reliable CaBV pulse and compared between patients and volunteers. The diagnostic accuracy of the most prominent parameter for NPH classification was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsThe similarity parameters were calculated for 31 probable NPH patients (age: 59 years (IQR: 47, 67 years), 14 females) and 23 healthy volunteers (age: 54 years (IQR: 43, 61 years), 18 females). Eighteen of 27 parameters were different between healthy individuals and NPH patients (p < 0.05). The most prominent differences were found for the ascending slope of the CaBV pulse with the AUC equal to 0.87 (95% confidence interval: 0.77, 0.97, p < 0.001).ConclusionsThe findings suggest that in NPH, the ascending slope of the CaBV pulse had a slower rise, was more like a straight line, and generally was less convex than in volunteers. Prospective research is required to verify the clinical utility of these findings.

Clustered photoplethysmogram pulse wave shapes and their associations with clinical data.

Photopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literature, it is common to classify DVPs into four classes based on the dicrotic notch position. However, when working with real data, labelling waveforms with one of these four classes is no longer straightforward and may be challenging. The correct identification of the DVP shape could enhance the precision and the reliability of the extracted bio markers. In this work we proposed unsupervised machine learning and deep learning approaches to overcome the data labelling limitations. Concretely we performed a K-medoids based clustering that takes as input 1) DVP handcrafted features, 2) similarity matrix computed with the Derivative Dynamic Time Warping and 3) DVP features extracted from a CNN AutoEncoder. All the cited methods have been tested first by imposing four medoids representative of the Dawber classes, and after by automatically searching four clusters. We then searched the optimal number of clusters for each method using silhouette score, the prediction strength and inertia. To validate the proposed approaches we analyse the dissimilarities in the clinical data related to obtained clusters.

Application of micro-bolus injection and piezoelectric sensors to improve the safety of radiopharmaceuticals bolus injection

Radiopharmaceutical dynamic imaging typically necessitates intravenous injection via the bolus method. However, manual bolus injection carries the risk of handling errors as well as radiological injuries. Hence, there is potential for automated injection devices to replace manual injection methods. In this study, the effect of micro-bolus pulse injection technology was compared and verified by radioactive experiments using a programmable injection pump, and the overall bubble recognition experiment and rat tail vein simulation injection verification were performed using the piezoelectric sensor preloading method. The results showed that at the same injection peak speed, the effective flushing volume of micro-bolus pulse flushing (about 83 μL/pulse) was 49.65% lower than that of uniform injection and 25.77% lower than that of manual flushing. In order to avoid the dilution effect of long pipe on the volume of liquid, the use of piezoelectric sensor for sealing preloading detection could accurately predict the bubbles of more than 100 μL in the syringe. In the simulated injection experiment of rat tail vein, when the needle was placed in different tissues by preloading 100 μL normal saline, the piezoelectric sensor fed back a large difference in pressure attenuation rate within one second, which was 2.78% in muscle, 17.28% in subcutaneous and 54.71% in vein. Micro-bolus pulse injection method and piezoelectric sensor sealing preloading method have application potential in improving the safety of radiopharmaceutical automatic bolus injection.

Assessing of exercise intensity for a rhythmik exercise program based on cardiopulmonary functions.

This study investigated the determination of exercise intensity for a rhythmik exercise program based on cardiopulmonary functions. Nine female participants in their 20s to 40s performed a rhythmik exercise program for 60 min per session, three sessions weekly, for 8 weeks. All participants underwent a grade exercise test to measure their minute ventilation (VE), oxygen uptake (VO2), maximal volume of minute oxygen consumption (VO2max), heart rate (HR), and oxygen pulse (O2 pulse) at the time of anaerobic threshold (AT) and maximal exercise time (ETmax). To determine the exercise intensity of the rhythmik exercise program, the Polar Heart Monitor was used for quantification, and the Borg Scale was used to measure the rating of perceived exertion (RPE). We did not observe any significant effects on the reaching time of AT, VE, VO2, VO2max, HR, and O2 pulse at either AT or ETmax. However, maximal value of VE (VEmax) was significantly enhanced at ETmax. The exercise intensity of the rhythmik exercise program was found to be low at 62.85% of HRmax with an RPE of 12.22. Our results suggest that the rhythmik exercise program did not directly improve cardiopulmonary functions; however, considering the unstructured nature of the rhythmik exercise program, anticipated positive effects on cardiopulmonary fitness are achievable through changes in the program contents and exercise time according to the goals of the participants.

Artificial Intelligence Enhanced Epileptic Seizure Detection from Wearables.

Wrist or ankle-worn devices are less intrusive than the widely used electroencephalogram systems for monitoring epileptic seizures. Using custom-developed deep-learning seizure detection models, we demonstrate the detection of a broad range of seizure types by wearable signals. Patients admitted to the epilepsy monitoring unit were enrolled and asked to wear wearable sensors on either wrists or ankles. We collected patients' electrodermal activity, accelerometry (ACC), and photoplethysmography from which blood volume pulse (BVP) is derived. Board-certified epileptologists determined seizure onset, offset, and seizure types using video and EEG recordings per ILAE 2017 classification. We applied three neural network models: a CNN and a CNN-LSTM-based generalized detection model and an autoencoder-based personalized detection model, to the raw time-series sensor data to detect seizures and utilized performance measures, including sensitivity, false positive rate (the number of false alarms divided by the total number of non-seizure segments), number of false alarms per day, and detection delay. We applied a 10-fold patient-wise cross-validation scheme to the multi-signal biosensor data and evaluated model performance on 28 seizure types. We analyzed 166 patients (47.6% female, median age: 10.0 years) and 900 seizures (13254 hours of sensor data) for 28 seizure types. With a CNN-LSTM-based seizure detection model, ACC, BVP, and their fusions' performances performed better than chance; ACC and BVP data fusion reached the best detection performance of 83.9% sensitivity and 35.3% false positive rate (FPR). Nineteen out of 28 seizure types could be detected by at least one data modality with AUC-ROC > 0.8 performance. Results from this in-hospital study contribute to a paradigm shift in epilepsy care that entails non-invasive seizure detection, provides time-sensitive and accurate data on additional clinical seizure types, and proposes a novel combination of an out-of-the-box monitoring algorithm with an individualized person-oriented seizure detection approach.

The Investigation of Bio-impedance Analysis at a Wrist Phantom with Two Pulsatile Arteries.

Bio-impedance analysis (BIA) has been widely investigated for hemodynamic monitoring. However, previous works rarely modelled two synchronously pulsatile arteries (representing the radial and ulnar arteries) in the wrist/forearm model. This work aims to clarify and quantify the influences of two pulsatile arteries on BIA. First, two blood-filled arteries were structured in a 3D wrist segment using the finite element method (FEM). Afterwards, an easy-to-produce two-arteries artificial wrist was fabricated with two components: gelatine-based surrounding tissue phantom and saline blood phantom. A syringe driver was utilised to constrict the arteries, and the impedance signals were measured using a Multi-frequency Impedance Analyser (MFIA). Both simulation and experimental results demonstrated the non-negligible influences of the ulnar artery on the overall BIA, inducing unwanted resistance changes to the acquired signals from the radial artery. The phantom experiments revealed the summation of the individual resistance changes caused by a single pulsatile artery was approximately equal to the measured resistance change caused by two synchronously pulsatile arteries, confirming the measured impedance signal at the wrist contains the pulsatile information from both arteries. This work is the first simulation and phantom investigation into two synchronously pulsatile arteries under BIA in the distal forearm, providing a better insight and understanding in the morphology of measured impedance signals. Future research can accordingly select either a small spacing 4-spot electrode configuration for a single artery sensing or a band electrode configuration for overall pulsatile arteries sensing. A more accurate estimation of blood volume change and pulse wave analysis (PWA) could help to develop cuffless blood pressure measurement (BPM).

Effects of 3-month Qigong exercise on heart rate variability and respiration in anxious college students.

This longitudinal study aimed to investigate the effects of Qigong on the anxiety state, heart rate variability (HRV), and breathing of anxious college students. A total of 37 individuals (18-25 years old) were randomly allocated to the control (n = 19) and intervention (n = 18) groups. Qigong interventions were conducted five times weekly for 12 weeks, with each session lasting 60 min. Hamilton Anxiety Scale, Fatigue Scale 14, Pittsburgh Sleep Quality Index, and 36-item Short Form Survey, HRV, and respiration data were collected before and after the 3-month intervention. Individuals who participated in the three-month Qigong exercise intervention showed a significant reduction in anxiety, particularly mental anxiety (p < 0.05). Subjects in the intervention group presented a decrease in skin temperature (p < 0.05) and an increase in blood volume pulsation (p < 0.05). Meanwhile, HRV exhibited a significant increase in the standard deviation of interbeat interval before and after comparisons (p < 0.05) and between the two groups (p = 0.039) and a reduction in the normalized low-frequency power after the intervention. Moreover, the intervention group experienced increased abdominal breathing depth and abdominal breathing per minute (p < 0.05). These findings indicate that Qigong is an effective mind-body exercise strategy for relieving anxiety. HRV and breathing were improved accordingly among college students after the completion of the 3-month Qigong program.

Percentage of mean arterial pressure as a marker of atherosclerosis for detecting patients with coronary artery disease

The percentage of mean arterial pressure (%MAP) is the height of the mean arterial waveform divided by the peak amplitude of the waveform of pulse volume recording. The purpose of this study was to determine whether the cutoff value of 45% for %MAP at the ankle, which is recommended for the diagnosis of lower extremity artery disease, in combination with ankle-brachial index (ABI) is useful for detecting patients with clinical coronary artery disease (CAD) and investigate the optimal cutoff value of %MAP to diagnose patients with CAD. We measured ABI and %MAP in 2213 subjects (mean age: 61.2 ± 15.5 years). Multivariate analysis revealed that %MAP ≥ 45% was significantly associated with a higher risk of CAD after adjusting for traditional cardiovascular risk factors (odds ratio [OR], 2.14; 95% confidence interval [CI], 1.43–3.21; p < 0.001). However, the association was no longer significant after adjusting for ABI (OR, 1.39; 95% CI, 0.83–2.33; p = 0.21), whereas ABI was significantly associated with CAD (OR, 0.98; 95% CI, 0.97–0.99; p = 0.005). The optimal cutoff value of %MAP derived from a receiver operating characteristic curve to diagnose CAD was 40.3%. Multivariate analysis revealed that %MAP ≥ 40.3% was significantly associated with a higher risk of CAD (OR, 1.63; 95% CI, 1.19–2.24; p = 0.002) independent of ABI (OR, 0.98; 95% CI, 0.97–0.99; p = 0.002). The cutoff value of 40.3%, but not 45%, for %MAP may be useful for detecting patients with advanced atherosclerosis and for cardiovascular risk assessment independent of ABI.Registration Informationhttp://www.umin.ac.jp (University Hospital Medical Information Network Clinical Trials Registry) (UMIN000039512)

Depression Recognition Using Remote Photoplethysmography From Facial Videos

Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extracts them from facial videos, just based on visual information, removing the need for any contact-based device. Our solution is based on a pipeline that is able to extract complete remote photoplethysmography signals (rPPG) in a fully unsupervised manner. We use these rPPG signals to calculate over 60 statistical, geometrical, and physiological features that are further used to train several machine learning regressors to recognize different levels of depression. Experiments on two benchmark datasets indicate that this approach offers comparable results to other audiovisual modalities based on voice or facial expression, potentially complementing them. In addition, the results achieved for the proposed method show promising and solid performance that outperforms hand-engineered methods and is comparable to deep learning-based approaches.