Search Results (48)

Background and Objectives: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide. Regular physical activity (PA) represents a key modifiable factor in CVD prevention. Methods: Fifty-two healthy adult males participated in this study, divided into two groups: aged up to 45 years and over 45 years. The subjects performed a bicycle ergometer exercise and a standardised back muscle workload protocol. ECG, arterial blood pressure (ABP), and muscle oxygen saturation (StO₂) measurements were obtained during workload and recovery. Results: During bicycle ergometer workload, heart rate (HR) at minute 2 was significantly lower in participants over 45 years of age compared to the younger group (126.8–109.8 bpm), while diastolic blood pressure (dBP) was significantly lower in the under-45 group during maximal workload (65.4–71.9 mmHg) and the first minute of recovery (54.6–69.3 mmHg). During workload for back muscles, the over-45 group showed significantly lower dBP at the third rest period (87–74.7 mmHg), while StO₂ was significantly lower in the over-45 group compared to the under-45 group (54.4–77.8%). Conclusions: The findings of this study demonstrate that both bicycle ergometer exercise and standardised back muscle workload had a significant influence on cardiovascular system (CVS) responses, particularly when stratified by age. Participants over the age of 45 exhibited a higher incidence of functional myocardial ischaemia, reduced StO₂ and more pronounced increases in HR during and following exertion. Full article

Background: Interventional electrophysiology is a rapidly advancing field, with sedation essential for patient comfort and immobility during complex electrophysiological procedures (EPS). However, sedatives and analgesics can cause respiratory depression and hypotension. Gender-specific differences (GDs) are often overlooked in medical research, as protocols and dosages are typically based on male subjects, potentially compromising treatment safety and efficacy for women. This study examines GDs in CO₂ levels, respiratory rate, arterial blood pressure (ABP), and anesthetic requirements during deep sedation for EPS. Methods: This prospective study at Ulm University Heart Center included 702 patients (405 men and 297 women) treated under deep sedation between August 2019 and October 2023. Standard monitoring included an electrocardiogram (ECG) with heart rate, non-invasive ABP, oxygen saturation (SpO₂), and a frequent venous blood gas analysis (vBGA). The primary composite endpoint was GDs in SpO₂ dips below 90% and pathological vBGA changes. Results: The primary composite endpoint was reached by 177 women (59.6%) and 213 men (52.6%), showing no significant difference (p = 0.102). Women had a 1,6-fold higher risk of experiencing SpO₂ dips below 90% (p = 0.001). Additionally, women had 1.7 times higher hypoxia rates (p < 0.001) and were 1.5 times more likely to have a mean ABP below 65 mmHg (p < 0.001). On average, they received 65.3 mg less total propofol than men (p = 0.005) and a higher midazolam dose per kilogram of body weight (p < 0.001). Conclusions: Although the primary composite endpoint showed no significant GDs, secondary outcomes highlight the need to consider gender-specific sedation adjustments, particularly for women. This study underscores the need for personalized sedation management and patient monitoring regarding GDs. Full article

Background: Hemodynamic instability is common during adrenalectomy for pheochromocytoma and paraganglioma (PPGL). Most analyses focus on the risk factors for intraoperative hypertension, but hypotension is a frequent and undesirable phenomenon during PPGL surgery. This study aimed to analyze the risk factors for hypotensive episodes during the removal of PPGL, and whether these episodes are always associated with concomitant intraoperative hypertensive events. Methods: A consecutive series of 121 patients (91.7% receiving preoperative alpha-blockade) treated with transperitoneal endoscopic adrenalectomy at a university hospital were analyzed, and pre- and intraoperative risk factors for intraoperative hypotension with or without intraoperative hypertension were analyzed using univariable and multivariable logistic regression analyses. Results: In total, 58 (56.2%) patients presented with intraoperative hypotension. Of these, 25 (20.7%) patients showed only hypotensive episodes but no hypertensive episodes (group 1), and 43 (35.5%) patients had both intraoperative hypotension and hypertension (group 2). The remaining 53 patients did not present with hypotension at all (group 3). When comparing group 1 (hypotension only) to all other patients with incidental diagnosis, higher age and lower preoperative diastolic arterial blood pressure (ABP) were significant risk factors for intraoperative hypotension; only the latter two were still significant in multivariate analysis. The significant risk factors for hypotension independent of hypertension (group 1 + 2 vs. group 3) were age and incidental diagnosis, pre-existing diabetes mellitus, and intraoperative use of remifentanil. Incidental diagnosis and use of remifentanil reached the level of significance in multivariate analysis. Conclusions: Since older age, incidental diagnosis of PPGL, lower preoperative ABP, and diabetes mellitus are risk factors for intraoperative hypotension, preoperative alpha-blocker treatment should be individualized for those at risk for hypotension. In addition, remifentanil should be used cautiously in the risk group. Full article

Visual sphygmomanometer needle oscillation (SNO) can occur before audible return of pulsatile flow (ARPF) when measuring blood pressure by Doppler ultrasound. The aim was to assess the agreement of SNO and ARPF with invasive blood pressure (iABP) in a clinical population of anaesthetised dogs. A total of 35 dogs undergoing surgery in dorsal recumbency necessitating arterial cannulation were included. Paired measurements of iABP and SNO, and iABP and ARPF, were collected. The agreement of non-invasive blood pressure (NIBP) and iABP measurements was analysed with concordance correlation coefficients (CCCs) and Bland–Altman plots. The proportions of SNO and ARPF measurements between 10 and 20 mmHg of iABP were compared. Both SNO and ARPF demonstrated greater agreement with invasive systolic (iSAP) than invasive mean (iMAP) pressures, and SNO demonstrated greater agreement with iSAP than ARPF measurements. The mean differences (95% limits of agreement) for SNO and APRF were −9.7 mmHg (−51.3–31.9) and −13.1 mmHg (−62.2–35.9), respectively. The CCC (95% CI) for SNO was 0.5 (0.36–0.64) and ARPF was 0.4 (0.26–0.54). A significantly greater proportion of SNO measurements were within 20 mmHg of iSAP compared to ARPF. Both NIBP techniques performed more poorly than veterinary consensus recommendations for device validation. Caution should be used clinically when interpreting values obtained by Doppler ultrasound in anaesthetised dogs. Full article

Background: Angiotensin II is a peptide hormone vasopressor that activates angiotensin type 1 (AT1) receptors leading to vasoconstriction, the augmentation of arterial blood pressure (ABP), and organ perfusion. Angiotensin II was found to increase the ABP in catecholamine-refractory vasodilatory shock. Whether this effect improves the chances of survival or not remains inconclusive. Therefore, we conducted a systematic review and meta-analysis to evaluate the efficacy and safety of angiotensin II in vasoplegic shock. Objectives: To evaluate the clinical significance of angiotensin II effects in vasoplegic shock concerning the hemodynamic impact, mortality outcomes, and side effects. Methods: Following PRISMA guidelines, we searched PubMed and EMBASE for experimental and observational studies published in English exploring the clinical outcomes of angiotensin II use in vasodilatory shock till 1 July 2024. Two independent authors assessed the quality and risk of bias of the included studies. A random effect model (Mantel–Haenszel) was used to combine data. The primary outcome was in-hospital mortality associated with angiotensin II use in comparison to standard therapy, while the secondary outcomes were mean arterial pressure (MAP) change, multi-organ failure (MOF), and the incidence of atrial fibrillation (AF). The Q test and I² were used to examine heterogeneity, with I² > 50% indicating marked heterogeneity. Results: A total of eight studies (n = 974) comparing angiotensin II to standard therapy in vasoplegic shock were included in the systematic review, with three studies comprising 461 patients included in the final analysis of the primary outcome. Only one study evaluated the use of angiotensin II as a primary vasopressor, while the rest reported angiotensin II use in catecholamine-refractory vasodilatory shock. Overall, angiotensin II use was associated with similar in-hospital mortality compared to standard therapy (risk ratio [RR] = 0.83; 95% CI, 0.68–1.02, I² = 0%). Likewise, there was no difference in MOF and AF (MOF: RR = 1.01; 95% CI, 0.61–1.65, I² = 0%; AF: RR = 1.27; 95% CI, 0.38–4.23, I² = 5%). However, angiotensin II use demonstrated a significant MAP increase (mean difference = −9.60; 95% CI, −9.71, −9.49, I² = 0%). Conclusions: In vasodilatory shock, angiotensin II use demonstrated comparable in-hospital mortality compared to standard therapy. Nevertheless, it resulted in significant MAP change, which may encourage clinicians to use it in cases of profound hypotension. Full article

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. Full article

Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing ${PAT}_{SPG}$ exhibited comparable or superior performance to ${PAT}_{PPG}$ (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring. Full article

Background and Purpose: Early differentiation between acute ischaemic (AIS) and haemorrhagic stroke (ICH), based on cerebral and peripheral hemodynamic parameters, would be advantageous to allow for pre-hospital interventions. In this preliminary study, we explored the potential of multiple parameters, including dynamic cerebral autoregulation, for phenotyping and differentiating each stroke sub-type. Methods: Eighty patients were included with clinical stroke syndromes confirmed by computed tomography within 48 h of symptom onset. Continuous recordings of bilateral cerebral blood velocity (transcranial Doppler ultrasound), end-tidal CO₂ (capnography), electrocardiogram (ECG), and arterial blood pressure (ABP, Finometer) were used to derive 67 cerebral and peripheral parameters. Results: A total of 68 patients with AIS (mean age 66.8 ± SD 12.4 years) and 12 patients with ICH (67.8 ± 16.2 years) were included. The median ± SD NIHSS of the cohort was 5 ± 4.6. Statistically significant differences between AIS and ICH were observed for (i) an autoregulation index (ARI) that was higher in the unaffected hemisphere (UH) for ICH compared to AIS (5.9 ± 1.7 vs. 4.9 ± 1.8 p = 0.07); (ii) coherence function for both hemispheres in different frequency bands (AH, p < 0.01; UH p < 0.02); (iii) a baroreceptor sensitivity (BRS) for the low-frequency (LF) bands that was higher for AIS (6.7 ± 4.2 vs. 4.10 ± 2.13 ms/mmHg, p = 0.04) compared to ICH, and that the mean gain of the BRS in the LF range was higher in the AIS than in the ICH (5.8 ± 5.3 vs. 2.7 ± 1.8 ms/mmHg, p = 0.0005); (iv) Systolic and diastolic velocities of the affected hemisphere (AH) that were significantly higher in ICH than in AIS (82.5 ± 28.09 vs. 61.9 ± 18.9 cm/s), systolic velocity (p = 0.002), and diastolic velocity (p = 0.05). Conclusion: Further multivariate modelling might improve the ability of multiple parameters to discriminate between AIS and ICH and warrants future prospective studies of ultra-early classification (<4 h post symptom onset) of stroke sub-types. 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

Continuous monitoring plays a crucial role in diagnosing hypertension, characterized by the increase in Arterial Blood Pressure (ABP). The gold-standard method for obtaining ABP involves the uncomfortable and invasive technique of cannulation. Conversely, ABP can be acquired non-invasively by using Photoplethysmography (PPG). This non-invasive approach offers the advantage of continuous BP monitoring outside a hospital setting and can be implemented in cost-effective wearable devices. PPG and ABP signals differ in scale values, which creates a non-linear relationship, opening avenues for the utilization of algorithms capable of detecting non-linear associations. In this study, we introduce Neural Model of Blood Pressure (NeuBP), which estimates systolic and diastolic values from PPG signals. The problem is treated as a binary classification task, distinguishing between Normotensive and Hypertensive categories. Furthermore, our research investigates NeuBP’s performance in classifying different BP categories, including Normotensive, Prehypertensive, Grade 1 Hypertensive, and Grade 2 Hypertensive cases. We evaluate our proposed method by using data from the publicly available MIMIC-III database. The experimental results demonstrate that NeuBP achieves results comparable to more complex models with fewer parameters. The mean absolute errors for systolic and diastolic values are 5.02 mmHg and 3.11 mmHg, respectively. Full article

One of the most significant indicators of heart and cardiovascular health is blood pressure (BP). Blood pressure (BP) has gained great attention in the last decade. Uncontrolled high blood pressure increases the risk of serious health problems, including heart attack and stroke. Recently, machine/deep learning has been leveraged for learning a BP from photoplethysmography (PPG) signals. Hence, continuous BP monitoring can be introduced, based on simple wearable contact sensors or even remotely sensed from a proper camera away from the clinical setup. However, the available training dataset imposes many limitations besides the other difficulties related to the PPG time series as high-dimensional data. This work presents beat-by-beat continuous PPG-based BP monitoring while accounting for the aforementioned limitations. For a better exploration of beats’ features, we propose to use wavelet scattering transform as a better descriptive domain to cope with the limitation of the training dataset and to help the deep learning network accurately learn the relationship between the morphological shapes of PPG beats and the BP. A long short-term memory (LSTM) network is utilized to demonstrate the superiority of the wavelet scattering transform over other domains. The learning scenarios are carried out on a beat basis where the input corresponding PPG beat is used for predicting BP in two scenarios; (1) Beat-by-beat arterial blood pressure (ABP) estimation, and (2) Beat-by-beat estimation of the systolic and diastolic blood pressure values. Different transformations are used to extract the features of the PPG beats in different domains including time, discrete cosine transform (DCT), discrete wavelet transform (DWT), and wavelet scattering transform (WST) domains. The simulation results show that using the WST domain outperforms the other domains in the sense of root mean square error (RMSE) and mean absolute error (MAE) for both of the suggested two scenarios. Full article

Regional cerebral oxygen saturation (rSO₂), a method of cerebral tissue oxygenation measurement, is recorded using non-invasive near-infrared Spectroscopy (NIRS) devices. A major limitation is that recorded signals often contain artifacts. Manually removing these artifacts is both resource and time consuming. The objective was to evaluate the applicability of using wavelet analysis as an automated method for simple signal loss artifact clearance of rSO₂ signals obtained from commercially available devices. A retrospective observational study using existing populations (healthy control (HC), elective spinal surgery patients (SP), and traumatic brain injury patients (TBI)) was conducted. Arterial blood pressure (ABP) and rSO₂ data were collected in all patients. Wavelet analysis was determined to be successful in removing simple signal loss artifacts using wavelet coefficients and coherence to detect signal loss artifacts in rSO₂ signals. The removal success rates in HC, SP, and TBI populations were 100%, 99.8%, and 99.7%, respectively (though it had limited precision in determining the exact point in time). Thus, wavelet analysis may prove to be useful in a layered approach NIRS signal artifact tool utilizing higher-frequency data; however, future work is needed. Full article

Left ventricular ejection time (LVET) is an important parameter for assessing cardiovascular disorders. In a medical office, it is typically measured using the Tissue Doppler Imaging technique, but new wearable devices have led to a growing interest in integrating this parameter into them, increasing accessibility to personalized healthcare for users and patients. In the cardiovascular domain, photoplethysmography (PPG) is a promising technology that shares two distinctive features with invasive arterial blood pressure (ABP) tracing: the tidal wave (TDW) and the dicrotic wave (DCW). In the early years of cardiovascular research, the duration of the dicrotic point was initially linked to the ending phase of left ventricular ejection. Subsequent studies reported deviations from the initial association, suggesting that the ejection period is related to the tidal wave feature. In this current study, we measured left ventricular ejection time in both ABP and PPG waveforms, considering recent research results. A total of 27,000 cardiac cycles were analyzed for both afore-mentioned signals. The reference value for ejection time was computed based on the T-wave segment duration from the electrocardiogram waveform. In lower blood pressure, which is associated with decreased heart contractility, the results indicated an underestimation of −29 ± 19 ms in ABP and an overestimation of 18 ± 31 ms in PPG. On the other side of the spectrum, during increased contractility, the minimum errors were −3 ± 18 ms and 4 ± 33 ms, respectively. Since the tidal wave feature is strongly affected by arterial tree compliance, the population evaluation results indicate a Pearson’s correlation factor of 0.58 in the ABP case, and 0.53 in PPG. These findings highlight the need for advanced compensation techniques, in particular for PPG assessment, to achieve clinical-grade accuracy. Full article

Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients’ health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN–LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN–LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN–LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices. Full article

This paper presents a neural network model to estimate arterial blood pressure (ABP) waveforms using electrocardiogram (ECG) and photoplethysmography (PPG) signals and its first two order mathematical derivatives (PPG${}^{\prime }$, PPG${}^{″}$). In order to achieve this objective, a lightweight and optimized neural network architecture has been proposed, made of Conv1D and BiLSTM layers. To train the network, the UCI Database “Cuff-Less Blood Pressure Estimation Data Set” has been used, which contains ECG and PPG signals together with the corresponding ABP waveform data; then the first two PPG derivatives have been computed. Four different configurations and parameter sets have been tested to choose the best structure and set of parameters. Additionally, various batch sizes, numbers of BiLSTM layers, and the presence of a maximum pooling layer have been tested. The best performing model achieves a mean absolute error of around 2.97, which is comparable to the state-of-the-art methods. Results prove deep learning techniques can be effectively used for non-invasive cuffless arterial blood pressure estimation. The lightweight and optimized model can be effectively used for continuous monitoring of blood pressure, which has significant clinical implications. Further research can focus on integrating the proposed model with wearable devices for real-time blood pressure monitoring in daily life. Full article