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Continuous monitoring of blood pressure (BP) and multiparametric analysis of cardiac functions are crucial for the early diagnosis and therapy of cardiovascular diseases. However, existing monitoring approaches often suffer from bulky and intrusive apparatus, cumbersome testing procedures, and challenging data processing, hampering their applications in continuous monitoring. Here, a heterogeneously hierarchical piezoelectric composite is introduced for wearable continuous BP and cardiac function monitoring, overcoming the rigidity of ceramic and the insensitivity of polymer. By optimizing the hierarchical structure and components of the composite, the developed piezoelectric sensor delivers impressive performances, ensuring continuous and accurate monitoring of BP at Grade A level. Furthermore, the hemodynamic parameters are extracted from the detected signals, such as local pulse wave velocity, cardiac output, and stroke volume, all of which are in alignment with clinical results. Finally, the all-day tracking of cardiac function parameters validates the reliability and stability of the developed sensor, highlighting its potential for personalized healthcare systems, particularly in early diagnosis and timely intervention of cardiovascular disease.

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William Harvey discovered that the cardiovascular system is a closed hydraulic circle. Since that discovery, many haemodynamic models have strayed by dividing the circulation into segments, which can be misleading. An alternative model is presented that both preserves circular hydraulics and provides a comprehensive picture of overall cardiovascular function using a novel cardiovascular vector graphic. The practical value of this approach resides in its ease of visualising critical physiological variables and ease of predicting and communicating how changes in those variables affect function.

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Autonomic dysreflexia (AD) is a common autonomic complication of spinal cord injury (SCI) characterized by a sudden increase is blood pressure triggered by peripheral stimulation, such as bladder distention. Iatrogenic AD events often occur during various medical procedures including urodynamic assessments (UDSs) used to evaluate lower urinary tract (LUT) function in individuals with SCI. To date, there are no established clinical practices that would allow early detection of the development of episodes of AD. Heart rate variability (HRV) is a reliable and non-invasive metric for evaluating autonomic regulation of the cardiovascular system, with demonstrated utility in people with SCI during UDSs. We aim to provide a comprehensive evaluation of cardiovascular function during UDS-induced AD using ultra-short-term HRV analysis and identify changes in cardiovascular dynamics to predict the onset of AD. We assessed cardiovascular data in a total of 24 participants with sensorimotor complete SCI above T6 (17 males, 7 females, median age = 43 [36-50] years) who experienced AD during UDS. We used continuous electrocardiographic recordings to evaluate HRV in 60 sec overlapping windows during filling cystometry. The mean of "normal-to-normal" heartbeats (meanNN), its standard deviation (SDNN), and the root mean square of successive differences (RMSSD) were calculated and used in all subsequent analyses. We found that SDNN and RMSSD diminished during the early phase of bladder filling and sharply increased during AD. Using the lowest point of statistical variability in heart rate (i.e., SDNN), we were able to predict AD events within 240 sec (percentile 25-percentile 75: 172-339 sec) before the first systolic blood pressure peak after AD onset (sensitivity = 0.667; specificity = 0.875). Our results indicated a temporary increase in sympathetic activity during the early phase of bladder filling, which is followed by an increase in parasympathetic outflow to the heart when AD occurs. These findings have significant clinical implications that extend beyond the context of UDS and demonstrate the importance of identifying early changes in HRV in order to accurately predict AD episodes in people living with SCI.

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Sleep is critical to maintaining physical and mental health. Measuring physiological parameters to quantify sleep quality without uncomfortable user experience remains highly desired but a challenge. Here, this work develops a soft bioelectronic patch to perform simultaneous respiration and cardiovascular monitoring during sleep in a wearable and non-invasive manner. The soft bioelectronic patch system is mainly composed of a pressure sensor, a flexible printed circuit for signal processing, and a soft thermoplastic urethane mold for assembling different functional modules. The soft bioelectronic patch holds a sensitivity of >0.12 V kPa-1 and a remarkable low-frequency response from 0.5 to 15 Hz. It is demonstrated to continuously monitor respiration and heartbeat during the whole night, which could be harnessed for sleep monitoring and obstructive sleep apnea-hypopnea syndrome diagnosis. The reported soft bioelectronic patch represents a simple and convenient platform technology for sleep study.

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Objective: Ballistocardiogram (BCG) features are of interest in wearable cardiovascular monitoring of cardiac performance. We assess feasibility of wrist acceleration BCG during exercise for estimating pulse transit time (PTT), enabling broader cardiovascular response studies during acute exercise and improved monitoring in individuals at risk for cardiovascular disease (CVD). We also examine the relationship between PTT, blood pressure (BP), and stroke volume (SV) during exercise and posture interventions. Methods: 25 participants underwent a bike exercise protocol with four incremental workloads (0 W, 50 W, 100 W, and 150 W) in supine and semirecumbent postures. BCG, invasive radial artery BP, tonometry, photoplethysmography (PPG) and echocardiography were recorded. Ensemble averages of BCG signals determined aortic valve opening (AVO) timings, combined with peripheral pulse wave arrival times to calculate PTT. We tested for significance using Wilcoxon signed-rank test. Results: BCG was successfully recorded at the wrist during exercise. PTT exhibited a moderate negative correlation with systolic BP (ρSup = -0.65, ρSR = -0.57, ρAll = -0.54). PTT differences between supine and semirecumbent conditions were significant at 0 W and 50 W (p < 0.001), less at 100 W (p = 0.0135) and 150 W (p = 0.031). SBP and DBP were lower in semirecumbent posture (p < 0.01), while HR was slightly higher. Echocardiography confirmed association of BCG features with AVO and indicated a positive relationship between BCG amplitude and SV (ρ = 0.74). Significance: Wrist BCG may allow convenient PTT and possibly SV tracking during exercise, enabling studies of cardiovascular response to acute exercise and convenient monitoring of cardiovascular performance.

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Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient's cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.

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Cardiovascular sensing and monitoring is a widely used function in cardiovascular devices. Nowadays, achieving desired flexibility, wearability and implantability becomes a major design goal for the advancement of this family of devices. As an emerging technology, nanogenerator (NG) offers an intriguing promise for replacing the battery, an essential obstacle toward tissue-like soft electronics. This article reviews most recent advancements in NG technology for advanced cardiovascular sensing and monitoring. Based on the application targets, the discuss covers implantable NGs on hearts, implantable NGs for blood vessel grafts and patches, and wearable NGs with various sensing functions. The applications of NGs as a power source and as an electromechanical sensing element are both discussed. At the end, current challenges in this direction and future research perspectives are elaborated. This emerging and impactful application direction reviewed in this article is expected to inspire many new research and commercialization opportunities in the field of NG technology.

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Background and Aims: Post-spinal anaesthesia hypotension (PSH) is common and can lead to significant morbidity and mortality. The inferior vena cava collapsibility index (IVCCI) and carotid artery peak systolic velocity variations (CAPVV) are two widely used parameters for assessing the volume status of critically ill patients which have also been investigated as predictors of PSH and hypotension after induction of general anaesthesia. In this study, we evaluated the diagnostic accuracy of IVCCI and CAPVV as predictors of PSH. Methods: A total of 50 patients aged between 18 and 65 years undergoing elective lower abdominal surgeries under spinal anaesthesia were included. The IVCCI and CAPVV were measured using ultrasound pre-operatively. After administering spinal anaesthesia, haemodynamic data were collected till 15 min. Our primary objective was to evaluate the role of IVCCI and CAPVV to predict PSH. The secondary objectives were to compare the predictive efficacy of these two parameters and to detect other parameters for predicting PSH. We constructed the receiver operator characteristic (ROC) curves for IVCCI and CAPVV and obtained the best cut-off values. Results: The PSH occurred in 34% of the patients. IVCCI >21.15 could predict PSH with 58.8% sensitivity and 69.7% specificity. CAPVV >18.33 predicted PSH with 70.6% sensitivity and 54.6% specificity and IVC max/IVCCI >60 could predict PSH with 58.8% sensitivity and 54.5% specificity. A composite model comprising IVCmax (maximum IVC diameter), CAPVV, and baseline mean blood pressure was able to predict PSH. Conclusion: Both IVCCI and CAPVV have poor diagnostic accuracy in predicting PSH in adult patients undergoing elective infra-umbilical surgery.

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Arrhythmias are one of the leading causes of death in the United States, and their early detection is essential for patient wellness. However, traditional arrhythmia diagnosis by expert evaluation from intermittent clinical examinations is time-consuming and often lacks quantitative data. Modern wearable sensors and machine learning algorithms have attempted to alleviate this problem by providing continuous monitoring and real-time arrhythmia detection. However, current devices are still largely limited by the fundamental mismatch between skin and sensor, giving way to motion artifacts. Additionally, the desirable qualities of flexibility, robustness, breathability, adhesiveness, stretchability, and durability cannot all be met at once. Flexible sensors have improved upon the current clinical arrhythmia detection methods by following the topography of skin and reducing the natural interface mismatch between cardiac monitoring sensors and human skin. Flexible bioelectric, optoelectronic, ultrasonic, and mechanoelectrical sensors have been demonstrated to provide essential information about heart-rate variability, which is crucial in detecting and classifying arrhythmias. In this review, we analyze the current trends in flexible wearable sensors for cardiac monitoring and the efficacy of these devices for arrhythmia detection.

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BACKGROUND: Acute heart failure and cardiogenic shock remain highly morbid conditions despite prompt medical therapy in critical care settings. Mechanical circulatory support (MCS) is a promising therapy for these patients, yet remains managed with open-loop control. Continuous measure of cardiac function would support and optimize MCS deployment and weaning. The nature of indwelling MCS provides a platform for attaining this information. This study investigates how hysteresis modeling derived from MCS device signals can be used to assess contractility changes to provide continuous indication of changing cardiac state. Load-dependent MCS devices vary their operation with cardiac state to yield a device-heart hysteretic interaction. Predicting and examining this hysteric relation provides insight into cardiac state and can be separated by cardiac cycle phases. Here, we demonstrate this by predicting hysteresis and using the systolic portion of the hysteresis loop to estimate changes in native contractility. This study quantified this measurement as the enclosed area of the systolic portion of the hysteresis loop and correlated it with other widely accepted contractility metrics in animal studies (n = 4) using acute interventions that alter inotropy, including a heart failure model. Clinical validation was performed in patients (n = 8) undergoing Impella support. RESULTS: Hysteresis is well estimated from device signals alone (r = 0.92, limits of agreement: - 0.18 to 0.18). Quantified systolic area was well correlated in animal studies with end-systolic pressure-volume relationship (r = 0.84), preload recruitable stroke work index (r = 0.77), and maximum slope of left ventricular pressure (dP/dtmax) (r = 0.95) across a range of inotropic conditions. Comparable results were seen in patients with dP/dtmax (r = 0.88). Diagnostic capability from ROC analysis yielded AUC measurements of 0.92 and 0.90 in animal and patients, respectively. CONCLUSIONS: Mechanical circulatory support hysteretic behavior can be well modeled using device signals and used to estimate contractility changes. Contractility estimate is correlated with other accepted metrics, captures temporal trends that elucidate changing cardiac state, and is able to accurately indicate changes in inotropy. Inherently available during MCS deployment, this measure will guide titration and inform need for further intervention.

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Continuous hemodynamic monitoring is important for long-term cardiovascular healthcare, especially in hypertension. The impedance plethysmography (IPG) based carotid pulse sensing is a non-invasive diagnosis technique for measuring pulse signals and further evaluating the arterial conditions of the patient such as continuous blood pressure (BP) monitoring. To reach the high-resolution IPG-based carotid pulse detection for cardiovascular applications, this study provides an optimized measurement parameter in response to obvious pulsation from the carotid artery. The influence of the frequency of excitation current, electrode cross-sectional area, electrode arrangements, and physiological site of carotid arteries on IPG measurement resolution was thoroughly investigated for optimized parameters. In this study, the IPG system was implemented and installed on the subject's neck above the carotid artery to evaluate the measurement parameters. The measurement results within 6 subjects obtained the arterial impedance variation of 2137 mΩ using the optimized measurement conditions, including excitation frequency of 50 kHz, a smaller area of 2 cm2, electrode spacing of 4 cm and 1.7 cm for excitation and sensing functions, and location on the left side of the neck. The significance of this study demonstrates an optimized measurement methodology of IPG-based carotid pulse sensing that greatly improves the measurement quality in cardiovascular monitoring.

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Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters. Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure. Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement. Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.

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Non-cardiac surgery in a high-risk patient with severe mitral stenosis (MS) and severe pulmonary hypertension (PH) presents a significant anesthetic challenge. Guidelines recommend using advanced hemodynamic monitors for specific cardiovascular goals. The gold standard for intraoperative monitoring in these cases is the pulmonary artery catheter (PAC) and transesophageal echocardiography (TEE). This case discusses the successful management of a severe MS patient undergoing cystoprostatectomy using a minimally invasive cardiovascular monitor (MICM) incorporating several hemodynamic parameters.

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The capability of sensor systems to efficiently scavenge their operational power from stray, weak environmental energies through sustainable pathways could enable viable schemes for self-powered health diagnostics and therapeutics. Triboelectric nanogenerators (TENG) can effectively transform the otherwise wasted environmental, mechanical energy into electrical power. Recent advances in TENGs have resulted in a significant boost in output performance. However, obstacles hindering the development of efficient triboelectric devices based on biocompatible materials continue to prevail. Being one of the most widely used polymers for biomedical applications, polyvinyl alcohol (PVA) presents exciting opportunities for biocompatible, wearable TENGs. Here, the holistic engineering and systematic characterization of the impact of molecular and ionic fillers on PVA blends' triboelectric performance is presented for the first time. Triboelectric devices built with optimized PVA-gelatin composite films exhibit stable and robust triboelectricity outputs. Such wearable devices can detect the imperceptible skin deformation induced by the human pulse and capture the cardiovascular information encoded in the pulse signals with high fidelity. The gained fundamental understanding and demonstrated capabilities enable the rational design and holistic engineering of novel materials for more capable biocompatible triboelectric devices that can continuously monitor vital physiological signals for self-powered health diagnostics and therapeutics.

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Reconstructing a standard 12-lead electrocardiogram (ECG) from signals received from electrodes packed into a patch-type device is a challenging task in the field of medical instrumentation. All attempts to obtain a clinically valid 12-lead ECG using a patch-type device were not satisfactory. In this study, we designed the hardware for a three-lead patch-type ECG device and employed a long short-term memory (LSTM) network that can overcome the limitations of the linear regression algorithm used for ECG reconstruction. The LSTM network can overcome the issue of reduced horizontal components of the vector in the electric signal obtained from the patch-type device attached to the anterior chest. The reconstructed 12-lead ECG that uses the LSTM network was tested against a standard 12-lead ECG in 30 healthy subjects and ECGs of 30 patients with pathologic findings. The average correlation coefficient of the LSTM network was found to be 0.95. The ability of the reconstructed ECG to detect pathologic abnormalities was identical to that of the standard ECG. In conclusion, the reconstruction of a standard 12-lead ECG using a three-lead patch-type device is feasible, and such an ECG is an equivalent alternative to a standard 12-lead ECG.

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BACKGROUND: To estimate oxygen uptake (VO2) from cardiopulmonary exercise testing (CPX) using simultaneously recorded seismocardiogram (SCG) and electrocardiogram (ECG) signals captured with a small wearable patch. CPX is an important risk stratification tool for patients with heart failure (HF) owing to the prognostic value of the features derived from the gas exchange variables such as VO2. However, CPX requires specialized equipment, as well as trained professionals to conduct the study. METHODS AND RESULTS: We have conducted a total of 68 CPX tests on 59 patients with HF with reduced ejection fraction (31% women, mean age 55 ± 13 years, ejection fraction 0.27 ± 0.11, 79% stage C). The patients were fitted with a wearable sensing patch and underwent treadmill CPX. We divided the dataset into a training-testing set (n = 44) and a separate validation set (n = 24). We developed globalized (population) regression models to estimate VO2 from the SCG and ECG signals measured continuously with the patch. We further classified the patients as stage D or C using the SCG and ECG features to assess the ability to detect clinical state from the wearable patch measurements alone. We developed the regression and classification model with cross-validation on the training-testing set and validated the models on the validation set. The regression model to estimate VO2 from the wearable features yielded a moderate correlation (R2 of 0.64) with a root mean square error of 2.51 ± 1.12 mL · kg-1 · min-1 on the training-testing set, whereas R2 and root mean square error on the validation set were 0.76 and 2.28 ± 0.93 mL · kg-1 · min-1, respectively. Furthermore, the classification of clinical state yielded accuracy, sensitivity, specificity, and an area under the receiver operating characteristic curve values of 0.84, 0.91, 0.64, and 0.74, respectively, for the training-testing set, and 0.83, 0.86, 0.67, and 0.92, respectively, for the validation set. CONCLUSIONS: Wearable SCG and ECG can assess CPX VO2 and thereby classify clinical status for patients with HF. These methods may provide value in the risk stratification of patients with HF by tracking cardiopulmonary parameters and clinical status outside of specialized settings, potentially allowing for more frequent assessments to be performed during longitudinal monitoring and treatment.

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PURPOSE: Carotid artery corrected flow time (cFT) derived from Doppler USG is a known predictor of volume responsiveness. However, it can't be obtained continuously, and is operator dependent. In this prospective study, correlation between Doppler derived carotid artery cFT and pressure transducer derived radial artery cFT was evaluated in adult patients undergoing surgery under general anaesthesia. METHODS: Doppler derived carotid artery cFT were obtained from n = 51 adult patients at n = 125 time points. Simultaneously, pressure transducer waveforms were saved at the time of measurement of carotid artery cFT. Later, images were analyzed by an image processing computer software; both pulse pressure variation and cFT were estimated from pressure transducer waveform. RESULTS: Radial artery flow times measured by two independent observers, were significantly correlated (r2 = 0.99, p < 0.00001). Bland-Altman analysis found limits of agreement - 8.3 to 6.3 ms [mean difference (95% CI) - 0.98 (- 1.63, - 0.32)]. Doppler derived carotid artery cFT & pressure transducer derived radial artery cFT were also significantly correlated [r2 = 0.78, p < 0.0001]. However, radial artery cFT was significantly higher than carotid artery cFT [p < 0.0001, paired sample t test]. Radial artery cFT > 404.4 ms had an sensitivity and specificity of 87.34% and 85% respectively with a grey zone was between 393.7 and 417 ms to predict PPV ≥ 12%. CONCLUSION: Pressure transducer derived radial artery cFT correlated with Doppler derived carotid artery cFT and may be a reasonable predictor of volume responsiveness. Further studies are required to confirm its role in various clinical scenario for prediction of volume responsiveness.

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