Search Results (91)

Transducers used in acoustic logging while drilling (ALWD) must be mounted on a drill collar, and their radiation performance is dependent on the employed mounting method. Herein, the complex transmitting voltage response of a while-drilling (WD) monopole acoustic source was calculated through finite-element harmonic-response analysis. Subsequently, the acoustic pressure waveform radiated by the source driven by a half-sine excitation voltage signal was calculated using the complex transmitting voltage response. The calculation results were compared with those obtained using finite-element transient analysis to verify the accuracy of the calculation method. The influence of transducer-mounting methods on the radiation performance of the monopole acoustic source was examined by modifying the material and structural dimensions of the coupling medium between the transducer and drill collar as well as the material and thickness of the protective cover. Numerical simulations were performed, and a transducer-mounting method suitable for ALWD was proposed based on the simulation results. Results showed that soft rubber (as the coupling material; thickness = 2 mm) enabled the WD monopole acoustic source to radiate robust acoustic energy in an infinite fluid. Increasing the height of the coupling material enhanced the radiated acoustic energy and reduced axial vibrations on the drill collar. The radiated acoustic pressure signal was unaffected by a steel protective cover (thickness = 0.5 mm). Conversely, increasing the cover thickness reduced the energy of the radiated acoustic signal. With increasing pulse width of the half-sine excitation voltage signal, the amplitude of the radiated acoustic pressure of the transducer initially increased and then declined, reaching a maximum at a pulse width that was 0.6 times the resonant period. Overall, the findings help in designing acoustic-source structures and excitation signals for ALWD tools. Full article

The application of ultra-high-pressure (UHP) water jets for rock slotting in the bottom hole has been recognized as a highly effective approach to enhance rock-breaking efficiency. However, the current downhole intensifiers are confronted with various limitations, including the short duration of UHP pulse water jet output and challenges in attaining both controllable and adjustable output frequencies, consequently leading to compromised slotting efficiency. In this study, a novel intensifier controlled by an electromagnetic valve was designed, and a visual test platform was constructed to investigate the output pressure characteristics and their influencing factors. The output characteristics of the intensifier consist of a mixed pulse jet composed of high-pressure and low-pressure jets, resulting in a square wave-like output waveform with an adjustable frequency. The output pressure characteristics of the intensifier are primarily influenced by the input pressure and the switching time of the electromagnetic valve, assuming that the structural parameters are constant. Increasing the input pressure raises the peak pressure, thereby enhancing the slotting capability of the jet stream. Aligning the switching time of the electromagnetic valve with the rotation period of the drill bit improves the slotting efficiency. In the lab tests, the output pressure of the intensifier was successfully increased to 118.2 MPa, with a sustained duration of a high-pressure jet segment for 2.1 s. These research findings offer a new method for enhancing drilling efficiency in deep hard rock formations. Full article

Background/Objectives: Aortic pulse wave velocity (aPWV) is a well-established surrogate marker of arterial stiffness. The Antares algorithm offers a method for determining aPWV from oscillometric blood pressure waveforms without requiring additional inputs. This prospective study aimed to evaluate the association and prognostic value of aPWV, determined by Antares, in predicting major adverse cardiovascular events (MACE). Methods: In total, 240 patients (median age 69, 25.4% female) underwent oscillometric blood pressure measurements, from which aPWV was calculated using the Antares algorithm. MACE, comprising myocardial infarction, stroke, or all-cause mortality, occurred in 19.2% of patients during a median follow-up of 43 months. Survival analyses were performed using continuous aPWV values, a 10 m/s threshold, and aPWV quartiles. Kaplan–Meier curves and log-rank tests were used to compare survival across aPWV groups. Cox proportional hazards models were applied to assess the independent predictive value of aPWV. Results: Patients with aPWV < 10 m/s showed significantly higher event-free survival compared to those with aPWV ≥ 10 m/s (log-rank p = 0.044). Quartile analysis reinforced this, with the highest event rate in the highest aPWV quartile (log-rank p < 0.01). Multivariable analysis confirmed aPWV as an independent predictor of MACE (HR per 1 m/s: 1.24, 95% CI: 1.08–1.41; HR per 1 SD: 1.53, 95% CI: 1.17–2.00, p = 0.002). Adding aPWV to a risk model improved predictive accuracy (C-index 0.68 to 0.71). Conclusions: In the investigated cohort, aPWV derived using the Antares algorithm is an independent predictor of cardiovascular events. This non-invasive approach is promising for improving simple outpatient risk stratification and targeting preventive measures. Full article

The Solid-State Linear Transformer Driver (SSLTD) is a nanosecond pulse power source characterized by its fast rise time and adjustable output waveform. It can generate uniform and stable atmospheric plasma jets, which is suitable for material surface modification. In this study, a 15-stage SSLTD was designed and assembled, which can produce a stable nanosecond pulse voltage up to 15 times the amplitude of the charging voltage at high frequencies, with a rise time of approximately 10 ns. This device can be used to generate stable atmospheric pressure Ar plasma jets with an electron density in the range of 10¹⁵~10¹⁶ cm⁻³ and gas temperatures close to room temperature. After the modification treatment by the plasma jets, the content of the C=O groups on the surface of the epoxy resin significantly increased in the wavelength range of 1720~1740 cm⁻¹, and its flashover resistance was noticeably enhanced. The optimal comprehensive modification effect was achieved at a charging voltage of 600 V, pulse width of 50 ns, and pulse frequency in the range of 800~1000 Hz. Full article

Background/Objectives: The present study examined the effect of 12-week combined exercise training in normobaric hypoxia on arterial stiffness, inflammatory biomarkers, and red blood cell (RBC) hemorheological function in 24 obese older women (mean age: 67.96 ± 0.96 years). Methods: Subjects were randomly divided into two groups (normoxia (NMX; n = 12) and hypoxia (HPX; n = 12)). Both groups performed aerobic and resistance exercise training programs three times per week for 12 weeks, and the HPX group performed exercise programs in hypoxic environment chambers during the intervention period. Body composition was estimated using bioelectrical impedance analysis equipment. Arterial stiffness was measured using an automatic waveform analyzer. Biomarkers of inflammation and oxygen transport (tumor necrosis factor alpha, interleukin 6 (IL-6), erythropoietin (EPO), and vascular endothelial growth factor (VEGF)), and RBC hemorheological parameters (RBC deformability and aggregation) were analyzed. Results: All variables showed significantly more beneficial changes in the HPX group than in the NMX group during the intervention. The combined exercise training in normobaric hypoxia significantly reduced blood pressure (systolic blood pressure: p < 0.001, diastolic blood pressure: p < 0.001, mean arterial pressure: p < 0.001, pulse pressure: p < 0.05) and brachial–ankle pulse wave velocity (p < 0.001). IL-6 was significantly lower in the HPX group than in the NMX group post-test (p < 0.001). Also, EPO (p < 0.01) and VEGF (p < 0.01) were significantly higher in the HPX group than in the NMX group post-test. Both groups showed significantly improved RBC deformability (RBC EI_3Pa) (p < 0.001) and aggregation (RBC AI_3Pa) (p < 0.001). Conclusions: The present study suggests that combined exercise training in normobaric hypoxia can improve inflammatory biomarkers and RBC hemorheological parameters in obese older women and may help prevent cardiovascular diseases. Full article

The surging prevalence of diabetes globally necessitates advancements in non-invasive diagnostics, particularly for the early detection of cardiovascular anomalies associated with the condition. This study explores the efficacy of Pulse Wave Analysis (PWA) for distinguishing diabetic from non-diabetic individuals through morphological examination of pressure pulse waveforms. The research unfolds in four phases: data accrual, preprocessing, Convolutional Neural Network (CNN) model construction, and performance evaluation. Data were procured using a multipara patient monitor, resulting in 2000 pulse waves equally divided between healthy individuals and those with diabetes. These were used to train, validate, and test three distinct CNN architectures: the conventional CNN, Visual Geometry Group (VGG16), and Residual Networks (ResNet18). The accuracy, precision, recall, and F1 score gauged each model’s proficiency. The CNN demonstrated a training accuracy of 82.09% and a testing accuracy of 80.6%. The VGG16, with its deeper structure, surpassed the baseline with training and testing accuracies of 90.2% and 86.57%, respectively. ResNet18 excelled, achieving a training accuracy of 92.50% and a testing accuracy of 92.00%, indicating its robustness in pattern recognition within pulse wave data. Deploying deep learning for diabetes screening marks progress, suggesting clinical use and future studies on bigger datasets for refinement. Full article

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject’s pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload. Full article

Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network’s representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life. Full article

Pulse signals carry comprehensive information regarding human cardiovascular physiology and pathology, providing a noninvasive and continuous method to assess cardiovascular health status in blood pressure monitoring. The blood pressure measurement method based on the pulse signal needs to extract the features of the single-cycle pulse signal, while the pulse signal pertains to the weak physiological signal of body surface. The acquisition process is susceptible to various factors leading to abnormal cycles, especially adjacent channel interference, affecting the subsequent feature extraction. To address this problem, this paper conducts an analysis of the formation mechanism of adjacent channel interference and proposes a single-cycle pulse signal recognition algorithm based on a one-dimensional deep convolutional neural network (1D-CNN) model. Radial pulse signals were collected from 150 subjects by pulse bracelet, and a dataset comprising 3446 single-cycle signals was extracted in total after denoising, single-cycle segmentation, and standardized preprocessing. The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This classification is achieved by evaluating the waveform morphology of the signals within a single cycle. The results show that the overall classification accuracy of the algorithm on the test set is 98.26%, in which the classification accuracy of pulse waves is 99.8%, indicating that it can effectively recognize single-cycle pulse waves, which lays the foundation for subsequent continuous blood pressure measurement. Full article

Beat-to-beat estimates of cardiac output from the direct measure of peripheral arterial blood pressure rely on the assumption that changes in the waveform morphology are related to changes in blood flow and vasomotor tone. However, in septic shock patients, profound changes in vascular tone occur that are not uniform across the entire arterial bed. In such cases, cardiac output estimates might be inaccurate, leading to unreliable evaluation of fluid responsiveness. Pulse wave velocity is the gold-standard method for assessing different arterial biomechanical properties. Such methods might be able to guide, personalize and optimize the management of septic patients. Full article

Purpose: This study aims to assess the effect of bariatric surgery on retinal microvascular calibre, peripheral microvascular function, peripheral pressure waveforms, and the general cardiovascular disease (CVD) risk in obese individuals after undergoing Roux-en-Y gastric bypass (RYGB) surgery. Methods: A total of 29 obese participants were included in the study. All of the measurements were conducted at two time points: before and one year following the bariatric surgery procedure. General anthropometric data, as well as blood markers for glucose, cholesterol, and triglycerides were assessed in all individuals. In all participants, the Framingham risk score (FRS), and retinal vessel calibre measurements, using a Zeiss fundus camera and VesselMap software (ImedosSystems, Jena, Germany), were performed. Systemic arterial stiffness was measured by pulse wave analysis (PWA), and peripheral microvascular reactivity by way of digital thermal monitoring (DTM) in all participants. Results: As expected, various general anthropometric parameters, including body mass index (BMI), waist circumference and neck circumference, were significantly decreased post-surgery comparing to baseline in all individuals (all p < 0.001). In addition, their general CVD risk, as measured using FRS, was significantly improved (p < 0.001). At the retinal vascular level, central retinal artery equivalent (CRAE) as well as, central retinal vein equivalent (CRVE) had increased after surgery comparing to the baseline values (p = 0.003 and p = 0.007, respectively). In addition, both systemic arterial stiffness and peripheral microvascular reactivity had improved in all participants (p < 0.001 and p = 0.008 respectively). Conclusions: Our findings suggest that bariatric surgery has a positive effect on the overall vascular health, as well as on the general CVD risk of the obese patients undergoing this procedure. Full article

Intracranial pressure (ICP) burden or pressure time dose (PTD) is a valuable clinical indicator for pending intracranial hypertension, mostly based on threshold exceedance. Pulse frequency and waveform morphology (WFM) of the ICP signal contribute to PTD. The temporal resolution of the ICP signal has a great influence on PTD calculation but has not been systematically studied yet. Hence, the temporal resolution of the ICP signal on PTD calculation is investigated. We retrospectively analysed continuous 48 h ICP recordings with high temporal resolution obtained from 94 patients at the intensive care unit who underwent neurosurgery due to an intracranial haemorrhage and received an intracranial pressure probe (43 females, median age: 72 years, range: 23 to 88 years). The cumulative area under the curve above the threshold of 20 mmHg was compared for different temporal resolutions of the ICP signal (beat-to-beat, 1 s, 300 s, 1800 s, 3600 s). Events with prolonged ICP elevation were compared to those with few isolated threshold exceedances. PTD increased for lower temporal resolutions independent of WFM and frequency of threshold exceedance. PTD_beat-to-beat best reflected the impact of frequency of threshold exceedance and WFM. Events that could be distinguished in PTD_beat-to-beat became magnified more than 7-fold in PTD_1s and more than 104 times in PTD_1h, indicating an overestimation of PTD. PTD calculation should be standardised, and beat-by-beat PTD could serve as an easy-to-grasp indicator for the impact of frequency and WFM of ICP elevations on ICP burden. Full article

Background: Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults. Methods: Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23–46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP). Results: Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness. Conclusion: Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure. Full article

Backround. Central systolic blood pressure (cSBP) provides valuable clinical and physiological information. A recent invasive study showed that cSBP can be reliably estimated from mean (MBP) and diastolic (DBP) blood pressure. In this non-invasive study, we compared cSBP calculated using a Direct Central Blood Pressure estimation (DCBP = MBP²/DBP) with cSBP estimated by radial tonometry. Methods. Consecutive patients referred for cardiovascular assessment and prevention were prospectively included. Using applanation tonometry with SphygmoCor device, cSBP was estimated using an inbuilt generalized transfer function derived from radial pressure waveform, which was calibrated to oscillometric brachial SBP and DBP. The time-averaged MBP was calculated from the radial pulse waveform. The minimum acceptable error (DCBP-cSBP) was set at ≤5 (mean) and ≤8 mmHg (SD). Results. We included 160 patients (58 years, 54%men). The cSBP was 123.1 ± 18.3 mmHg (range 86–181 mmHg). The (DCBP-cSBP) error was −1.4 ± 4.9 mmHg. There was a linear relationship between cSBP and DCBP (R² = 0.93). Forty-seven patients (29%) had cSBP values ≥ 130 mmHg, and a DCBP value > 126 mmHg exhibited a sensitivity of 91.5% and specificity of 94.7% in discriminating this threshold (Youden index = 0.86; AUC = 0.965). Conclusions. Using the DCBP formula, radial tonometry allows for the robust estimation of cSBP without the need for a generalized transfer function. This finding may have implications for risk stratification. Full article

Not long ago, hearables paved the way for biosensing, fitness, and healthcare monitoring. Smart earbuds today are not only producing sound but also monitoring vital signs. Reliable determination of cardiovascular and pulmonary system information can explore the use of hearables for physiological monitoring. Recent research shows that photoplethysmography (PPG) signals not only contain details on oxygen saturation level (SPO2) but also carry more physiological information including pulse rate, respiration rate, blood pressure, and arterial-related information. The analysis of the PPG signal from the ear has proven to be reliable and accurate in the research setting. (1) Background: The present integrative review explores the existing literature on an in-ear PPG signal and its application. This review aims to identify the current technology and usage of in-ear PPG and existing evidence on in-ear PPG in physiological monitoring. This review also analyzes in-ear (PPG) measurement configuration and principle, waveform characteristics, processing technology, and feature extraction characteristics. (2) Methods: We performed a comprehensive search to discover relevant in-ear PPG articles published until December 2022. The following electronic databases: Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Scopus, Web of Science, and PubMed were utilized to conduct the studies addressing the evidence of in-ear PPG in physiological monitoring. (3) Results: Fourteen studies were identified but nine studies were finalized. Eight studies were on different principles and configurations of hearable PPG, and eight studies were on processing technology and feature extraction and its evidence in in-ear physiological monitoring. We also highlighted the limitations and challenges of using in-ear PPG in physiological monitoring. (4) Conclusions: The available evidence has revealed the future of in-ear PPG in physiological monitoring. We have also analyzed the potential limitation and challenges that in-ear PPG will face in processing the signal. Full article