Arterial Blood Pressure Waveform Research Articles

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method

Background and objectiveDetection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. MethodsThe algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm against marked DN detection, while box plots and Bland-Altman plots were used to compare its performance with both marked DN detection and an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy Python package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. ResultsThe correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87,343) =0.99, p<.001) and PPG (R2(86,764) =0.98, p<.001) waveforms. The algorithm had a lower mean error of DN detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high rate of detectability of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. ConclusionOur proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform (‘DN-less signals’). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.

Wave Separation Analysis to Assess Cardiovascular Alterations Induced by Sepsis.

Sepsis induces a severe decompensation of arterial and cardiac functional properties, leading to important modifications of arterial blood pressure (ABP) waveform, not resolved by recommended therapy, as shown by previous works. The aim of this study is to quantify the changes in ABP waveform morphology and wave reflections during a long-term swine experiment of polymicrobial sepsis and resuscitation, to deepen the understanding of the cardiovascular response to standard resuscitation therapy. We analyzed 14 pigs: polymicrobial sepsis was induced in 9 pigs followed by standard resuscitation and 5 pigs were treated as sham controls. Septic animals were studied at baseline (T1), after sepsis development (T2), and after 24 h (T3) and 48 h (T4) of therapy administration, and sham controls at the same time points. ABP and arterial blood flow were measured in the left and right carotid artery, respectively. Pulse wave analysis and wave separation techniques were used to estimate arterial input impedance, carotid characteristic impedance, forward and backward waves, indices of wave reflections such as reflection magnitude and reflection index, and augmentation index. Sepsis led to an acute alteration of ABP waveform passing from type A to type B or C; consistently, the reflection phenomena were significantly reduced. The resuscitation was successful in reaching targeted hemodynamic stability, but it failed in restoring a physiological blood propagation and reflection. Septic pigs persistently showed altered reflected waves even after 48 hours of successful therapy according to guidelines, suggesting a persistent hidden cardiovascular disorder. The proposed indices may be useful to unravel the complex cardiovascular response to therapy administration in septic patients and could potentially be used for risk stratification of patient deterioration. Whether alterations of blood propagation and reflection contribute to persisting organ dysfunction after hemodynamic stabilization should be further investigated.

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit

Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized that ICP waveforms can be computed noninvasively from three extracranial physiological waveforms routinely acquired in the Intensive Care Unit (ICU): arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG). We evaluated over 600 h of high-frequency (125 Hz) simultaneously acquired ICP, ABP, ECG, and PPG waveform data in 10 patients admitted to the ICU with critical brain disorders. The data were segmented in non-overlapping 10-s windows, and ABP, ECG, and PPG waveforms were used to train deep learning (DL) models to re-create concurrent ICP. The predictive performance of six different DL models was evaluated in single- and multi-patient iterations. The mean average error (MAE) ± SD of the best-performing models was 1.34 ± 0.59 mmHg in the single-patient and 5.10 ± 0.11 mmHg in the multi-patient analysis. Ablation analysis was conducted to compare contributions from single physiologic sources and demonstrated statistically indistinguishable performances across the top DL models for each waveform (MAE±SD 6.33 ± 0.73, 6.65 ± 0.96, and 7.30 ± 1.28 mmHg, respectively, for ECG, PPG, and ABP; p = 0.42). Results support the preliminary feasibility and accuracy of DL-enabled continuous noninvasive ICP waveform computation using extracranial physiological waveforms. With refinement and further validation, this method could represent a safer and more accessible alternative to invasive ICP, enabling assessment and treatment in low-resource settings.

Rapid Changes in Cerebrovascular Compliance During Vasovagal Syncope

Background: The rapid reduction in arterial blood pressure (ABP) during vasovagal syncope (VVS) is associated with a characteristic decline in diastolic cerebral blood velocity (CBV) and maintained systolic CBV as measured by transcranial Doppler. This increased pulsatility was originally considered to be indicative of a “dysfunctional” increase in cerebrovascular resistance (CVR). Conversely, it may be due to increased cerebrovascular compliance (Ci) that partially attenuates the cerebral effects of rapid hypotension. We tested the hypothesis that Ci supports CBV during pre-syncope in VVS. Method: Finger ABP and right middle cerebral artery blood velocity (MCAv) were recorded from 15 otherwise healthy patients (n = 11 female, 40 ± 16 years, 24.9 ± 4.0 kg/m2) at rest and during head-up tilt (80°, 5 to 28 min). Individual ABP and CBV waveforms of every second heartbeat during VVS were input into a modified Windkessel model to calculate Ci and CVR. MCAv pulsatility was determined as pulse amplitude (systolic MCAv-diastolic MCAv) and Pulsatility Index (Pi; pulse amplitude/mean MCAv). Linear mixed models assessed the contribution of Ci to changes in CBV and Pi. Within-subject mediation analysis investigated the possibility of Ci mediating the relation between blood pressure (i.e., mean ABP and pulse pressure, PP) and cerebrovascular pulsatility. Results: Diastolic and systolic ABP decreased progressively during presyncope, resulting in a nadir reduction of mean ABP of Δ24 ± 9 mmHg at end-tilt (all p ≤ 0.04 vs mid-tilt). Mean MCAv decreased by 22 ± 12% prior to end-tilt (p &lt; 0.01 vs mid-tilt). CVR decreased progressively for 10s prior to end-tilt (p ≤ 0.04 vs mid-tilt). Pi increased significantly, with peak increases occurring at 4 s prior to end-tilt (77 ± 50%, p &lt; 0.01 vs mid-tilt). The increase in Ci (307 ± 223%, p &lt; 0.01 vs mid-tilt) significantly contributed to changes in MCAv (χ2 =10.6, p &lt; 0.01) and Pi (χ2 = 4.9, p = 0.03). Correlations of PP and ABP with Pi were not significant. However, PP was related to MCAv pulse amplitude (total effect: b = 1.57) and Ci ( b = -0.01, both p &lt; 0.01). Ci partially mediated (indirect effect: b = -0.78, p &lt; 0.01) the direct relation between PP and MCAv pulse amplitude ( b = 0.79, p = 0.01). Conclusion: These data provide evidence that Ci attenuates the fall in CBV under conditions of falling PP. Cerebrovascular regulation in VVS appears to be functioning as in healthy populations. However, this regulation is not suffcient to preserve flow in the presence of such extreme and rapid reductions in ABP. USRI — Western University, Natural Sciences and Engineering Research Council of Canada (NSERC, Grant No. RGPIN-2018-06255). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Reconstruction of arterial blood pressure waveforms based on squeeze-and-excitation network models using electrocardiography and photoplethysmography signals

Arterial blood pressure (ABP) waveforms indicate the efficiency with which a patient’s blood responds to changes in the arterial flow. ABP waveforms can be used to analyze the cardiovascular health of patients, including heart rate analysis, stress analysis, and cardiovascular disease diagnosis. However, obtaining the ABP waveform is more difficult than recording the electrocardiography (ECG) and photoplethysmography (PPG) signals. Several studies have focused on the reconstruction of the ABP waveform by recombining bio-signals. Reconstructing ABP waveforms is considered a major challenge because of signals being distorted by waveform destruction, owing to the normalization and standardization of the input ABP waveform. This paper presents a model that can simultaneously output blood pressure estimation and ABP waveforms using the raw ABP signal as the input. Herein, the squeeze-and-excitation network (SE-Net) is coupled with the multi-task learning architecture. Three signals, namely, the ECG, PPG, and ECG–PPG, were trained as feature vectors using different variants of the U-Net model, and the results are compared. Additionally, we introduce a novel method for incorporating white Gaussian noise at different signal-to-noise ratios (SNRs) to augment the training data. The objective of the proposed approach is to evaluate the ABP signal reconstruction capability of the SE-Net at various SNR levels and analyze the overall performance and robustness of the model. In the case of systolic blood pressure, the root mean square error (RMSE) of the PPG-based values is 2.58 mmHg, and the Pearson correlation coefficient is r = 0.92 (p≤ 0.001). In the case of diastolic blood pressure(DBP), the RMSE was 3.35 mmHg, and the Pearson correlation coefficient was r = 0.89 (p ≤ 0.001). The results indicate the potential for replacing ECG signals with PPG signals to overcome the constraints of optimizing the non-invasive reconstruction of ABP waveforms using SE-Net. Furthermore, we demonstrate that the SE-Net-combined multi-task learning architecture model can simultaneously perform blood pressure estimation and output ABP waveforms without damaging the raw signal.

The time constant of the cerebral arterial bed: exploring age-related implications.

The time constant of the cerebral arterial bed (τ) represents an estimation of the transit time of flow from the point of insonation at the level of the middle cerebral artery to the arteriolar-capillary boundary, during a cardiac cycle. This study assessed differences in τ among healthy volunteers across different age groups.Simultaneous recordings of transcranial Doppler cerebral blood flow velocity (CBFV) and arterial blood pressure (ABP) were performed on two groups: young volunteers (below 30years of age), and older volunteers (above 40years of age). τ was estimated using mathematical transformation of ABP and CBFV pulse waveforms. 77 healthy volunteers [52 in the young group, and 25 in the old group] were included. Pulse amplitude of ABP was higher [16.7 (14.6-19.4) mmHg] in older volunteers as compared to younger ones [12.5 (10.9-14.4) mm Hg; p < 0.001]. CBFV was lower in older volunteers [59 (50-66) cm/s] as compared to younger ones [72 (63-78) cm/s p < 0.001]. τ was longer in the younger volunteers [217 (168-237) ms] as compared to the older volunteers [183 (149-211) ms; p = 0.004]. τ significantly decreased with age (rS = - 0.27; p = 0.018).τ is potentially an integrative marker of the changes occurring in cerebral vasculature, as it encompasses the interplay between changes in compliance and resistance that occur with age.

Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure

Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management. Notwithstanding its potential, the invasive nature of ABP measurements confines their utility primarily to clinical environments, limiting their applicability for continuous monitoring beyond medical facilities. The conversion of photoplethysmography (PPG) signals into ABP equivalents has garnered significant attention due to its potential in revolutionizing cardiovascular disease management. Recent strides in PPG-to-ABP prediction encompass the integration of generative and discriminative models. Despite these advances, the efficacy of these models is curtailed by the latent space shift predicament, stemming from alterations in PPG data distribution across disparate hardware and individuals, potentially leading to distorted ABP waveforms. To tackle this problem, we present an innovative solution named the Latent Space Constraint Transformer (LSCT), leveraging a quantized codebook to yield robust latent spaces by employing multiple discretizing bases. To facilitate improved reconstruction, the Correlation-boosted Attention Module (CAM) is introduced to systematically query pertinent bases on a global scale. Furthermore, to enhance expressive capacity, we propose the Multi-Spectrum Enhancement Knowledge (MSEK), which fosters local information flow within the channels of latent code and provides additional embedding for reconstruction. Through comprehensive experimentation on both publicly available datasets and a private downstream task dataset, the proposed approach demonstrates noteworthy performance enhancements compared to existing methods. Extensive ablation studies further substantiate the effectiveness of each introduced module.

Detecting aortic valve stenosis based on the non-invasive blood pressurewaveform-a proof of concept study.

The incidence of aortic valve stenosis (AoS) increases with age, and once diagnosed, symptomatic severe AoS has a yearly mortality rate of 25%. AoS is diagnosed with transthoracic echocardiography (TTE), however, this gold standard is time consuming and operator and acoustic window dependent. As AoS affects the arterial blood pressure waveform, AoS-specific waveform features might serve as a diagnostic tool. Aim of the present study was to develop a novel, non-invasive, AoS detection model based on blood pressures waveforms. This cross-sectional study included patients with AoS undergoing elective transcatheter or surgical aortic valve replacement. AoS was determined using TTE, and patients with no or mild AoS were labelled as patients without AoS, while patients with moderate or severe AoS were labelled as patients with AoS. Non-invasive blood pressure measurements were performed in awake patients. Ten minutes of consecutive data was collected. Several blood pressure-based features were derived, and the median, interquartile range, variance, and the 1st and 9th decile of the change of these features were calculated. The primary outcome was the development of a machine-learning model for AoS detection, investigating multiple classifiers and training on the area under the receiver-operating curve (AUROC). In total, 101 patients with AoS and 48 patients without AoS were included. Patients with AoS showed an increase in left ventricular ejection time (0.02s, p = 0.001), a delayed maximum upstroke in the systolic phase (0.015s, p < 0.001), and a delayed maximal systolic pressure (0.03s, p < 0.001) compared to patients without AoS. With the logistic regression model, a sensitivity of 0.81, specificity of 0.67, and AUROC of 0.79 were found. The majority of the population without AoS was male (85%), whereas in the population with AoS this was evenly distributed (54% males). Age was significantly (5years, p < 0.001) higher in the population with AoS. In the present study, we developed a novel model able to distinguish no to mild AoS from moderate to severe AoS, based on blood pressure features with high accuracy. Clinical registration number: The study entailing patients with TAVR treatment was registered at ClinicalTrials.gov (NCT03088787, https://clinicaltrials.gov/ct2/show/NCT03088787 ). The study with elective cardiac surgery patients was registered with the Netherland Trial Register (NL7810, https://trialsearch.who.int/Trial2.aspx?TrialID=NL7810 ).

Validation of deep learning models for cuffless blood pressure estimation on a large benchmarking dataset

Objective: This study aims to evaluate the effectiveness of deep learning techniques in estimating cuffless blood pressure (BP) across a diverse patient population in intensive care units (ICUs). Methods: A comprehensive ICU benchmarking dataset encompassing 2,154 patients with a wide demographic range (18-97 years old) and varied cardiovascular status was employed to validate several deep learning models in predicting continuous BP waveforms. Three methods were developed to enhance the model's generalizability to this heterogeneous dataset. Ten-fold subject-independent cross-validation was performed and the model performance was assessed through mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and compared with significance analysis. Results: The UTransBPNet_Demo_In model, which incorporated demographic and physiological signals as inputs, achieved a PCC of 0.89 and a MAE of 10.38 mmHg in predicting arterial BP waveforms, demonstrating the highest tracking capability among all models. Notably, the performance of UTransBPNet_Demo_In remained robust across variations in demographic and cardiovascular status. Conclusion: The UTransBPNet_Demo_In model demonstrates robust predictive capabilities across a broad spectrum of demographics and cardiovascular conditions. Although the performance still needs further improvement, this study offers a benchmark in the field of cuffless BP monitoring in critical care settings for future studies.

Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data

Fluid bolus therapy (FBT) is fundamental to the management of circulatory shock in critical care but balancing the benefits and toxicities of FBT has proven challenging in individual patients. Improved predictors of the hemodynamic response to a fluid bolus, commonly referred to as a fluid challenge, are needed to limit non-beneficial fluid administration and to enable automated clinical decision support and patient-specific precision critical care management. In this study we retrospectively analyzed data from 394 fluid boluses from 58 pigs subjected to either hemorrhagic or distributive shock. All animals had continuous blood pressure and cardiac output monitored throughout the study. Using this data, we developed a machine learning (ML) model to predict the hemodynamic response to a fluid challenge using only arterial blood pressure waveform data as the input. A Random Forest binary classifier referred to as the ML fluid responsiveness algorithm (MLFRA) was trained to detect fluid responsiveness (FR), defined as a ≥ 15% change in cardiac stroke volume after a fluid challenge. We then compared its performance to pulse pressure variation, a commonly used metric of FR. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), confusion matrix metrics, and calibration curves plotting predicted probabilities against observed outcomes. Across multiple train/test splits and feature selection methods designed to assess performance in the setting of small sample size conditions typical of large animal experiments, the MLFRA achieved an average AUROC, recall (sensitivity), specificity, and precision of 0.82, 0.86, 0.62. and 0.76, respectively. In the same datasets, pulse pressure variation had an AUROC, recall, specificity, and precision of 0.73, 0.91, 0.49, and 0.71, respectively. The MLFRA was generally well-calibrated across its range of predicted probabilities and appeared to perform equally well across physiologic conditions. These results suggest that ML, using only inputs from arterial blood pressure monitoring, may substantially improve the accuracy of predicting FR compared to the use of pulse pressure variation. If generalizable, these methods may enable more effective, automated precision management of critically ill patients with circulatory shock.

Estimating intracranial parameters using an inverse mathematical model with viscoelastic elements that closely predicts complex ICP morphologies

The quantitative relationship between arterial blood pressure (ABP) and intracranial pressure (ICP) waveforms has not been adequately explained. We hypothesized that the ICP waveform results from interferences between propagating and reflected pressure waves occurring in the cranium following the initiating arterial waveform. To demonstrate cranial effects on interferences between waves and generation of an ICP waveform morphology, we modified our previously reported mathematical model to include viscoelastic elements that affect propagation velocity. Using patient data, we implemented an inverse model methodology to generate simulated ICP waveforms in response to given ABP waveforms. We used an open database of traumatic brain injury patients and studied 65 pairs of ICP and ABP waveforms from 13 patients (five pairs from each). Incorporating viscoelastic elements into the model resulted in model-generated ICP waveforms that very closely resembled the measured waveforms with a 16-fold increase in similarity index relative to the model with only pure elasticity elements. The mean similarity index for the pure elasticity model was 0.06 ± 0.12 SD, compared to 0.96 ± 0.28 SD for the model with viscoelastic components. The normalized root mean squared error (NRMSE) improved substantially for the model with viscoelastic elements compared to the model with pure elastic elements (NRMSE of 2.09% ± 0.62 vs. 15.2% ± 4.8, respectively). The ability of the model to generate complex ICP waveforms indicates that the model may indeed reflect intracranial dynamics. Our results suggest that the model may allow the estimation of intracranial biomechanical parameters with potential clinical significance. It represents a first step in the estimation of inaccessible intracranial parameters.

Cerebral autoregulation derived blood pressure targets in elective neurosurgery

Poor postoperative outcomes may be associated with cerebral ischaemia or hyperaemia, caused by episodes of arterial blood pressure (ABP) being outside the range of cerebral autoregulation (CA). Monitoring CA using COx (correlation between slow changes in mean ABP and regional cerebral O2 saturation—rSO2) could allow to individualise the management of ABP to preserve CA. We aimed to explore a continuous automated assessment of ABPOPT (ABP where CA is best preserved) and ABP at the lower limit of autoregulation (LLA) in elective neurosurgery patients. Retrospective analysis of prospectively collected data of 85 patients [median age 60 (IQR 51–68)] undergoing elective neurosurgery. ABPBASELINE was the mean of 3 pre-operative non-invasive measurements. ABP and rSO2 waveforms were processed to estimate COx-derived ABPOPT and LLA trend-lines. We assessed: availability (number of patients where ABPOPT/LLA were available); time required to achieve first values; differences between ABPOPT/LLA and ABP. ABPOPT and LLA availability was 86 and 89%. Median (IQR) time to achieve the first value was 97 (80–155) and 93 (78–122) min for ABPOPT and LLA respectively. Median ABPOPT [75 (69–84)] was lower than ABPBASELINE [90 (84–95)] (p < 0.001, Mann-U test). Patients spent 72 (56–86) % of recorded time with ABP above or below ABPOPT ± 5 mmHg. ABPOPT and ABP time trends and variability were not related to each other within patients. 37.6% of patients had at least 1 hypotensive insult (ABP < LLA) during the monitoring time. It seems possible to assess individualised automated ABP targets during elective neurosurgery.

Frequency domain analysis of photoplethysmographic and arterial pressure waveforms for assessing hemodynamics in children with congenital heart surgery.

Time-domain parameters are less reliable in children due to increased arterial and chest wall compliance. We assessed the ability of indices derived from frequency analysis of photoplethysmography (PPG) and arterial blood pressure (ABP) waveforms to predict the hemodynamic state in children undergoing congenital heart surgery. We analyzed waveforms after cardiopulmonary bypass period in 76 children who underwent total repair of congenital heart disease. Amplitude density of baseline and amplitude modulation in PPG and ABP by respiratory frequency were obtained using fast Fourier transform analysis and normalized by cardiac pulse height (representing respiratory modulations in venous blood [PPG-DC%] and in amplitude [PPG-AC%] at respiratory frequency). The ratio of amplitude density of PPG at the cardiac frequency (CF) to ABP-CF was used to assess vascular compliance. We assessed volume replacement (ml/kg) and vasoactive inotropic score (VIS). Children requiring volume replacement > 10 ml/kg (15.8%) showed higher PPG-DC% than those not requiring it (median: 52.4%, 95% CI [24.8, 295.1] vs. 36.7% [10.7, 125.7], P = 0.017). In addition, children with a VIS > 7 (22.4%) showed higher PPG-CF/ABP-CF (3.6 [0.91, 10.8] vs. 1.2 [0.27, 5.5], P = 0.008). On receiver operating characteristic curve analysis, PPG-DC% predicted a higher fluid requirement (area under the curve: 0.71, 95% CI [0.604, 0.816], P = 0.009), while PPG-CF/ABP-CF predicted a higher VIS (0.714, [0.599, 0.812], P = 0.004). Frequency domain analysis of PPG and ABP may assess hemodynamic status requiring fluid or vasoactive inotropic therapy after congenital heart surgery.

Intelligent System for the Classification of Arterial Blood Pressure Waveform Abnormalities Due to Mistiming in Intra-Aortic Balloon Pump

Cardiovascular diseases detection or diagnosis on appropriate time is crucial to avoid health complications. In this study, an advanced procedure for classifying changes in the blood pressure has been used analyzing the wave-forms inside the arterial system where such variation can occur due to improper timing in intra-aortic balloon pump (IABP) control. Inaccurate pressure extends with probable injury can be caused by improper timing in the heart valve in both pumping and compression of the balloon. This investigation focuses on accurately recognizing and classifying any irregularities in the artery wave-forms in IABP in the blood pressure initiated by mistiming. Accumulated blood pressure records are used for the progression of providing information to IABP trainer. The wave-forms require pre-handling employing image digitizing software to acquire automated identifications. Any undesirable image features have been removed using Wavelet in MATLAB software. Afterward, such features can be employed to develop a technique for arrangement depending on neural networks. The artificial neural network technique has used marked data to properly detect irregularities in wave-forms in vascular blood pressure due to improper IABP timing. As a result, the validation has proved to appropriately recognize and classify such anomalies, denoting a considerable prospect to improve patient protection with an efficacy of treatment in the area of cardiovascular prescription.

An end-to-end deep learning framework for accurate estimation of intracranial pressure waveform characteristics

It is clinically significant if intracranial pressure (ICP) waveform could be reliably reconstructed from simple physiological measurements that are commonly used in clinical practice. Some attempts have been made with the focus on estimating intermittent mean ICP (MICP) values, but no investigation has been performed to reconstruct beat-by-beat ICP waveforms from arterial blood pressure (ABP) waveforms. This study aimed to overcome this challenge. A large dataset of 28511 segments (10.8 s for each segment; 540 samples) of simultaneously measured invasive ABP and ICP signals was used to develop and cross-evaluate a Wave-U-Net based deep learning model for reconstructing ICP waveform from ABP signals. From the reconstructed beat-by-beat ICP waveform, systolic ICP, diastolic ICP, and MICP were derived for each cardiac cycle and compared with reference ICPs. After comparing all the sampling points of each segment, the results showed that the reconstructed ICP waveforms were statistically correlated to the reference ICP waveforms, with the average Pearson's correlation coefficient (r) of 0.822 across all the segments. There was no significant difference between the estimated and reference ICP values, with the mean absolute error (MAE) of systolic ICP, diastolic ICP and MICP of 0.75 ± 0.29, 0.48 ± 0.22 and 0.42 ± 0.18 mmHg, respectively, meeting the clinical standards of ICP monitoring for the management of traumatic brain injury. Our proposed method confirmed the hypothesis for the first time that ABP waveform can be used for reliable reconstruction of ICP waveform, enabling continuous monitoring of ICP waveform, and providing the hope possibility to replace the gold standard invasive technique.

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.

Development of continuous cuffless blood pressure prediction platform using enhanced 1-D SENet–LSTM

In this study, we propose a blood pressure estimation that employs a combined 1-D squeeze and excitation network (SENet-LSTM) architecture. Combining photoplethysmography (PPG) and electrocardiogram (ECG) signals can provide more helpful feature information. The original signals were processed by removing the noise and artifacts. By introducing SE block, the ability to learn features is enhanced, and an end-to-end approach is used to realize the automatic learning process. Additionally, the algorithm has the ability to recover arterial blood pressure (ABP) waveforms from blood pressure readings. The results of this study met the Grade A standard for diastolic blood pressure (DBP), systolic blood pressure (SBP), and mean arterial pressure (MAP) set by the British Hypertension Society (BHS) and were also in accordance with the Association for the Advancement of Medical Instrumentation (AAMI) standard. According to the DBP and SBP ranges, blood pressure is divided into the following categories: normal blood pressure, prehypertension, and hypertension. For the classification of Hypertension, Normotension, and Prehypertension using SBP, we achieved accuracy of 94% and F1 scores of 0.92, 0.94, and 0.85. For the results obtained using DBP classification the overall accuracy was 91%, with F1 scores of 0.85, 0.98, and 0.78. When compared to other studies, the classifier results generated by SBP and DBP estimates based on the 1-D SENet-LSTM method improved accuracy by 2% and 11%, respectively.

Left Ventricular Ejection Time Estimation from Blood Pressure and Photoplethysmography Signals Based on Tidal Wave

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.

Pulse wave analysis as a tool for the evaluation of resuscitation therapy in septic shock

Objective. Pulse wave analysis (PWA) can provide insights into cardiovascular biomechanical properties. The use of PWA in critically ill patients, such as septic shock patients, is still limited, but it can provide complementary information on the cardiovascular effects of treatment when compared to standard indices outlined in international guidelines. Previous works have highlighted how sepsis induces severe cardiovascular derangement with altered arterial blood pressure waveform morphology and how resuscitation according to standard haemodynamic targets is not able to restore the physiological functioning of the cardiovascular system. The aim of this work is to test the effectiveness of PWA in characterizing arterial waveforms obtained from a swine experiment involving polymicrobial septic shock and resuscitation with different drugs. Methods. During the experiment, morphological aortic waveform features, such as indices related to the dicrotic notch and inflection point, were extracted by means of PWA techniques. Finally, all the PWA indices were used to compute a clustering classification (mini batch K-means) of the pigs according to the different phases of the experiment. This analysis aimed to test if PWA features alone could be used to distinguish between the different responses to the administered therapies. Results. The PWA indices highlighted different cardiovascular conditions of the pigs in response to different treatments, despite the mean haemodynamic values typically used to guide therapy administration being similar in all animals. The clustering algorithm was able to distinguish between the different phases of the experiment and the different responses of the animals based on the unique information derived from the aortic PWA. Conclusion. Even when used alone, PWA indices were highly informative when assessing therapy responses in cases of septic shock. Significance. A complex pathological condition like septic shock requires extensive monitoring without neglecting important information from commonly measured signals such as arterial blood pressure. Future studies are needed to understand how individual differences in the response to therapy are associated with different cardiovascular conditions that may become specific therapy targets.

Arterial blood pressure waveform in liver transplant surgery possesses variability of morphology reflecting recipients' acuity and predicting short term outcomes.

We investigated clinical information underneath the beat-to-beat fluctuation of the arterial blood pressure (ABP) waveform morphology. We proposed the Dynamical Diffusion Map algorithm (DDMap) to quantify the variability of morphology. The underlying physiology could be the compensatory mechanisms involving complex interactions between various physiological mechanisms to regulate the cardiovascular system. As a liver transplant surgery contains distinct periods, we investigated its clinical behavior in different surgical steps. Our study used DDmap algorithm, based on unsupervised manifold learning, to obtain a quantitative index for the beat-to-beat variability of morphology. We examined the correlation between the variability of ABP morphology and disease acuity as indicated by Model for End-Stage Liver Disease (MELD) scores, the postoperative laboratory data, and 4 early allograft failure (EAF) scores. Among the 85 enrolled patients, the variability of morphology obtained during the presurgical phase was best correlated with MELD-Na scores. The neohepatic phase variability of morphology was associated with EAF scores as well as postoperative bilirubin levels, international normalized ratio, aspartate aminotransferase levels, and platelet count. Furthermore, variability of morphology presents more associations with the above clinical conditions than the common BP measures and their BP variability indices. The variability of morphology obtained during the presurgical phase is indicative of patient acuity, whereas those during the neohepatic phase are indicative of short-term surgical outcomes.