Search Results (103)

Background: In cardiology, vasoregulation is one of the most important targets of pharmacotherapy. SOMNOtouch™-NIBP (SOMNOmedics AG, Randersacker, Germany) is a cuffless device designed for continuous, non-invasive blood pressure measurements, and it appears to be ready for use in infants and children with congenital heart disease. For infants, minor methodological modifications are required due to their small body size. Methods: Using this device, we demonstrate fluctuations in diastolic blood pressure in three patients: an infant with hypoplastic left heart syndrome after Norwood stage 1 and 2 operations; an infant with Tetralogy of Fallot with heart failure due to pulmonary overcirculation after an aorto-pulmonary shunt implantation; and a 13-year-old girl with chronic cyanosis due to a congenitally corrected transposition of the great arteries (ccTGA) with a ventricular septal defect and pulmonary stenosis. The measurement procedures are completely non-invasive and feasible in an outpatient setting. Results: The results demonstrate strong correlations between blood pressure and oxygen saturation levels as well as heart rate variability. We discuss our results in relation to current concepts of hypoxic pulmonary/systemic vasoconstriction and hypoxemia-related pathways. Conclusions: The cuffless device for continuous, non-invasive blood pressure measurement seems to be useful for infants with and without congenital heart defects who receive pharmacotherapies that modulate vasoregulation. These patients should also be non-invasively monitored for safety reasons and for a better understanding of their pathophysiology. Full article

Blood pressure (BP) monitoring is essential for cardiovascular health management, yet traditional cuff-based methods face limitations including patient discomfort and inapplicability for certain populations. This study presents a deep learning framework for cuffless BP estimation using phonocardiogram (PCG) signals. The proposed model integrates convolutional neural networks (CNNs) with Squeeze-and-Excitation (SE) blocks and demographic information to enhance prediction accuracy. Mel-Frequency Cepstral Coefficients (MFCCs), along with their delta and delta–delta coefficients, were employed to capture comprehensive acoustic characteristics of heart sounds. The results demonstrated that the proposed model achieved high predictive accuracy and strong consistency with reference BP measurements. Component analysis confirmed that the inclusion of SE blocks provided substantial performance gains, while demographic information further improved prediction stability. Clinical validation also verified that the model maintained close agreement with true BP values across the tested population, showing significant improvement over the baseline CNN implementation. These findings suggest potential for accessible, non-invasive BP monitoring systems suitable for continuous health tracking. Full article

Accurate and continuous, non-invasive blood pressure (BP) monitoring plays a vital role in the long-term management of cardiovascular diseases. Advances in wearable and flexible sensing technologies have facilitated the transition of non-invasive BP monitoring from clinical settings to ambulatory home environments. However, the measurement consistency and algorithm adaptability of existing devices have not yet reached the level required for routine clinical practice. To address these limitations, comprehensive innovations have been made in material development, sensor design, and algorithm optimization. This review examines the evolution of non-invasive continuous BP measurement, highlighting cutting-edge advances in flexible electronic devices and BP estimation algorithms. First, we introduce measurement principles, sensing devices and limitations of traditional non-invasive BP measurement, including arterial tonometry, arterial volume clamp, and ultrasound-based methods. Subsequently, we review the pulse wave analysis-based BP estimation methods from two perspectives: flexible sensors based on optical, mechanical, and electrical principles, and estimation models that use physiological features or raw waveforms as input. Finally, we conclude the existing challenges and future development directions of flexible electronic technology and intelligent estimation algorithms for non-invasive continuous BP measurement. Full article

The real-time, precise monitoring of physiological signals such as intracranial pressure (ICP) and arterial blood pressure (BP) holds significant clinical importance. However, traditional methods like invasive ICP monitoring and invasive arterial blood pressure measurement present challenges including complex procedures, high infection risks, and difficulties in continuous measurement. Consequently, learning-based prediction utilizing observable signals (e.g., BP/pulse waves) has emerged as a crucial alternative approach. Existing models struggle to simultaneously capture multi-scale local features and long-range temporal dependencies, while their computational complexity remains prohibitively high for meeting real-time clinical demands. To address this, this paper proposes a physiological signal prediction method combining composite feature preprocessing with multiscale modeling. First, a seven-dimensional feature matrix is constructed based on physiological prior knowledge to enhance feature discriminative power and mitigate phase mismatch issues. Second, a network architecture CNN-LSTM-Attention (CBAnet), integrating multiscale convolutions, long short-term memory (LSTM), and attention mechanisms is designed to effectively capture both local waveform details and long-range temporal dependencies, thereby improving waveform prediction accuracy and temporal consistency. Experiments on GBIT-ABP, CHARIS, and our self-built PPG-HAF dataset show that CBAnet achieves competitive performance relative to bidirectional long short-term Memory (BiLSTM), convolutional neural network-long short-term memory network (CNN-LSTM), Transformer, and Wave-U-Net baselines across Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). This study provides a promising, efficient approach for non-invasive, continuous physiological parameter prediction. Full article

Background and Aims: Decreased cerebrovascular reactivity (CVR) in patients with significant internal carotid artery stenosis (ICAS ≥ 70%) is an independent risk factor for cerebral infarction. To evaluate CVR, changes in cerebral perfusion pressure and blood flow velocity (BFV) of the middle cerebral artery (MCA) can be estimated by CO₂- (hyperventilation—HV and breath-holding—BH) and pressure–flow-based (Common Carotid Artery Compression—CCC and Valsalva Maneuver—VM) stimuli. We used a multimodal approach to characterize CVR in patients before carotid endarterectomy (CEA). Methods: HV, BH, CCC, and VM tests were performed on 31, 26, and 34 patients. BFV of MCAs was monitored by transcranial Doppler, and continuous arterial blood pressure was registered non-invasively. CVR was compared between the operated significantly stenotic and the contralateral sides. Results: The extent of HV- and BH-induced CVR was similar, but the time to the lowest HV-induced BFV was shorter on the side with significant ICAS. The response to CCC was sensitive to hemodynamic asymmetry in the transient hyperemic response ratio and in the cumulative change in the (mean arterial blood pressure)/(mean BFV) ratio. In VM, the slope of BFV increased in the ascending (2b) phase, and the time to overshoot correlated with the side of the stenosis. Conclusions: These results suggest that in patients with significant ICAS, in addition to CO₂ reactivity measurements, a more complex estimation of CVR, by using hemodynamic tests (CCC and VM), should also be used to better quantify cerebral ischemic risk. Full article

Hemodynamic monitoring is essential in the postoperative management of neonates undergoing cardiac surgery, enabling early identification of circulatory failure and its underlying cause, optimization of oxygen delivery to tissues, and evaluation of treatment response. Despite its significant role, there is still no consensus and there remains substantial heterogeneity in bedside hemodynamic monitoring practices. Pediatric intensivists typically rely on macro- and microcirculatory indicators, including arterial blood pressure, urine output, capillary refill time, mixed venous oxygen saturation, lactate concentration, and serial echocardiographic evaluations. However, most of these are indirect hemodynamic indicators and provide only intermittent snapshots of the hemodynamic status, which can be very fluctuating following cardiac surgery. Technological advancements have driven a shift toward continuous, noninvasive monitoring techniques, such as near-infrared spectroscopy (NIRS), electrical biosensing technology, and microcirculatory assessment tools. Real-time, simultaneous tracking of multiple physiological variables through a multimodal hemodynamic monitoring protocol facilitates the understanding of systemic and regional perfusion and oxygenation. This narrative review aims to summarize current techniques and innovations in neonatal hemodynamic monitoring following cardiac surgery, combining clinical evaluation with both intermittent and continuous noninvasive techniques. Full article

The phase shift (PS) between spontaneous slow oscillations of cerebral and systemic hemodynamics reliably reflects the state of cerebral autoregulation (CA). However, CA measurements are performed retrospectively after studying the signals from the analysis sensors. At the same time, CA-oriented therapy is becoming increasingly important with the receipt of data on the state of CA in real time, especially in intensive care units. We offer a hardware and software complex for transcranial Dopplerography, which uses a non-invasive method and allows for continuous measurement of cerebral blood flow to assess the rate of CA in real time. The hardware and software complex uses sensors to measure the PS between spontaneous slow oscillations of blood flow velocity (BFV) in the middle cerebral arteries (MCAs) and systemic arterial pressure (BP) in the Mayer wave range and performs wavelet analysis of sensor signals. An examination of 30 volunteers, with an average age of 28 ± 8 years, and 15 patients, with an average age of 57 ± 16 years, with various neurovascular pathologies confirms the feasibility of using the developed hardware and software complex for continuous monitoring of PS in real time to study the mechanisms of cerebral blood flow regulation. Full article

Background/Objectives: The aim of this study was to develop evaluation metrics for lower-limit vasopressor control, a strategy intended to prevent prolonged intraoperative hypotension under noninvasive blood pressure monitoring. Methods: Using general-purpose simulation software, we developed a blood pressure generation model with one-minute intervals and an automated vasopressor administration model with five-minute intervals. The latter delivered drugs according to predefined rules when systolic blood pressure (sBP) fell below a threshold. Four dosing strategies were constructed by combining bolus, repeated low-dose bolus, and continuous infusion approaches. Simulations were performed, and the following evaluation metrics were calculated: (1) proportion of time below threshold (PTBT), (2) mean value below threshold (MVBT), (3) average sBP, and (4) median performance error (MDPE) and median absolute performance error (MDAPE). Results: PTBT and MVBT analyses showed that incorporating continuous infusion reduced both the duration and severity of hypotension. Moreover, adding MVBT to the average sBP after subtracting the threshold quantified the extent to which sBP exceeded the threshold on average. In contrast, MDPE and MDAPE varied substantially with the assumed target pressure, highlighting their limitations in evaluating lower-limit control without a fixed target. Conclusions: For lower-limit control, metrics such as PTBT, MVBT, and average sBP offer useful insights into control stability and hypotension avoidance, whereas MDPE and MDAPE may be unsuitable for quantitative assessment when the primary goal is to exceed a threshold rather than achieve a fixed target pressure. Full article

This study describes the design and validation of an experimental setup for testing photoplethysmographic (PPG) devices intended for the non-invasive monitoring of vascular accesses in hemodialysis patients. Continuous assessment of arteriovenous fistulas is essential to detect pathological conditions such as stenosis, which can compromise patient safety and dialysis efficacy. While PPG-based sensors are capable of detecting such anomalies, their clinical applicability must be supported by controlled in vitro validation. The developed system replicates the anatomical, mechanical, optical, and hemodynamic features of vascular accesses. A 3D fistula model was designed and fabricated via 3D printing and silicone casting. The hydraulic circuit used red India ink and a PWM-controlled pump to simulate physiological blood flow, including stenotic conditions. Quantitative validation confirmed anatomical accuracy within 0.1 mm tolerance. The phantom exhibited an average Shore A hardness of 20.3 ± 1.1, a Young’s modulus of 10.4 ± 0.9 MPa, and a compression modulus of 105 MPa—values consistent with soft tissue behavior. Burst pressure exceeded 2000 mmHg, meeting ISO 7198:2016 standards. Flow rates (400–700 mL/min) showed <1% error. Compliance was 2.4 ± 0.2, and simulated blood viscosity was 3.9 ± 0.3 mPa·s. Systolic and diastolic pressures fell within physiological ranges. Photoplethysmographic signals acquired using a MAX30102 sensor (Analog devices Inc., Wilmington, MA, USA) reproduced key components of in vivo waveforms, confirming the system’s suitability for device testing. Full article

Cerebral autoregulation refers to the ability of cerebral vasculature to maintain stable blood flow by adjusting vascular resistance in response to changes in perfusion pressure. With advancing age, this regulatory capacity gradually declines, and its early, real-time, and dynamic monitoring holds potential as a promising approach for the prevention and treatment of cerebrovascular diseases. Given the absence of an established “gold standard” for assessing cerebral autoregulation, this study aimed to develop a non-invasive, continuous method for assessing cerebral autoregulation based on bioelectrical impedance technology. Using a wearable headband in combination with a Finapres device, blood pressure and cerebral blood flow were continuously monitored. A novel impedance recovery curve method was developed and, together with systemic blood pressure data, used to construct a hierarchical cerebral autoregulation assessment model via system identification. Moreover, the utility of this method in differentiating autoregulatory capacity across age groups (young adult and middle-aged) was assessed. The results demonstrated that the time constant (τ_REG), which characterizes the speed of cerebral blood flow recovery, differed significantly between the young adult and middle-aged groups (p < 0.001). These findings suggest the potential of τ_REG as a quantitative indicator for distinguishing cerebral autoregulatory function between healthy age cohorts. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

Background: Hypotension following epidural labor analgesia (ELA) is its most common complication, affecting approximately 20% of patients and posing risks to both maternal and fetal health. As digital tools and predictive analytics increasingly shape perioperative and obstetric anesthesia practices, real-world implementation data are needed to guide their integration into clinical care. Current monitoring practices rely on intermittent non-invasive blood pressure (NIBP) measurements, which may delay recognition and treatment of hypotension. The Hypotension Prediction Index (HPI) algorithm uses continuous arterial waveform monitoring to predict hypotension for potentially earlier intervention. This clinical trial evaluated the feasibility, acceptability, and efficacy of continuous HPI-guided treatment in reducing time-to-treatment for ELA-associated hypotension and improving maternal hemodynamics. Methods: This was a prospective randomized controlled trial design involving healthy pregnant individuals receiving ELA. Participants were randomized into two groups: Group CM (conventional monitoring with NIBP) and Group HPI (continuous noninvasive blood pressure monitoring). In Group HPI, hypotension treatment was guided by HPI output; in Group CM, treatment was based on NIBP readings. Feasibility, appropriateness, and acceptability outcomes were assessed among subjects and their bedside nurse using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM) instruments. The primary efficacy outcome was time-to-treatment of hypotension, defined as the duration between onset of hypotension and administration of a vasopressor or fluid therapy. This outcome was chosen to evaluate the clinical responsiveness enabled by HPI monitoring. Hypotension is defined as a mean arterial pressure (MAP) < 65 mmHg for more than 1 min in Group CM and an HPI threshold < 75 for more than 1 min in Group HPI. Secondary outcomes included total time in hypotension, vasopressor doses, and hemodynamic parameters. Results: There were 30 patients (Group HPI, n = 16; Group CM, n = 14) included in the final analysis. Subjects and clinicians alike rated the acceptability, appropriateness, and feasibility of the continuous monitoring device highly, with median scores ≥ 4 across all domains, indicating favorable perceptions of the intervention. The cumulative probability of time-to-treatment of hypotension was lower by 75 min after ELA initiation in Group HPI (65%) than Group CM (71%), although this difference was not statistically significant (log-rank p = 0.66). Mixed models indicated trends that Group HPI had higher cardiac output (β = 0.58, 95% confidence interval −0.18 to 1.34, p = 0.13) and lower systemic vascular resistance (β = −97.22, 95% confidence interval −200.84 to 6.40, p = 0.07) throughout the monitoring period. No differences were found in total vasopressor use or intravenous fluid administration. Conclusions: Continuous monitoring and precision hypotension treatment is feasible, appropriate, and acceptable to both patients and clinicians in a labor and delivery setting. These hypothesis-generating results support that HPI-guided treatment may be associated with hemodynamic trends that warrant further investigation to determine definitive efficacy in labor analgesia contexts. Full article

Traditional cuff-based blood pressure (BP) monitoring methods provide only intermittent readings, while invasive alternatives pose clinical risks. Recent studies have demonstrated feasibility of estimating continuous non-invasive cuff-less BP using photoplethysmogram (PPG) signals alone. However, existing approaches rely on complex manual feature engineering and/or multiple model architectures, resulting in inefficient epoch training numbers and limited performance. This research proposes cBP-Tnet, an efficient single-channel and model multi-task Transformer network designed for PPG signal automatic feature extraction. cBP-Tnet employed specialized hyperparameters—integrating adaptive Kalman filtering, outlier elimination, signal synchronization, and data augmentation—leveraging multi-head self-attention and multi-task learning strategies to identify subtle and shared waveform patterns associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP). We used the MIMIC-II public dataset (500 patients with 202,956 samples) for experimentation. Results showed mean absolute errors of 4.32 mmHg for SBP and 2.18 mmHg for DBP. For the first time, both SBP and DBP meet the Association for the Advancement of Medical Instrumentation’s international standard (<5 mmHg, >85 subjects). Furthermore, the network efficiently reduces the epoch training number by 13.67% when compared to other deep learning methods. Thus, this establishes cBP-Tnet’s potential for integration into wearable and home-based healthcare devices with continuous non-invasive cuff-less blood pressure monitoring. Full article

Cardiovascular disease (CVD) is the leading cause of global mortality, with hypertension affecting over one billion people. Current noninvasive blood pressure (BP) systems, like cuffs, suffer from discomfort and placement errors and lack continuous monitoring. Wearable solutions promise improvements, but technologies like photoplethysmography (PPG) and bioimpedance (BIOZ) face usability and clinical accuracy limitations. PPG is sensitive to skin tone and body mass index (BMI) variability, while BIOZ struggles with electrode contact and reusability. We present a novel, strain gauge-based wearable BP device that directly quantifies pressure via a dual transducer system, compensating for tissue deformation and external forces to enable continuous, accurate BP measurement. The reusable, energy-efficient, and compact design suits long-term daily use. A novel leg press protocol across 10 subjects (systolic: 71.04–241.42 mmHg, diastolic: 53.46–123.84 mmHg) validated its performance under dynamic conditions, achieving mean absolute errors of 2.45 ± 3.99 mmHg (systolic) and 1.59 ± 2.08 mmHg (diastolic). The device showed enhanced robustness compared to the Finapres, with less motion-induced noise. This technology significantly advances current methods by delivering continuous, real-time BP monitoring without reliance on electrodes, independent of skin tone, while maintaining a high accuracy and user comfort. Full article