Monitoring Of Cardiovascular System Research Articles

CPSS: Fusing consistency regularization and pseudo-labeling techniques for semi-supervised deep cardiovascular disease detection using all unlabeled electrocardiograms

Background and objective:Deep learning usually achieves good performance in the supervised way, which requires a large amount of labeled data. However, manual labeling of electrocardiograms (ECGs) is laborious that requires much medical knowledge. Semi-supervised learning (SSL) provides an effective way of leveraging unlabeled data to improve model performance, providing insight for solving this problem. The objective of this study is to improve the performance of cardiovascular disease (CVD) detection by fully utilizing unlabeled ECG. Methods:A novel SSL algorithm fusing consistency regularization and pseudo-labeling techniques (CPSS) is proposed. CPSS consists of supervised learning and unsupervised learning. For supervised learning, the labeled ECGs are mapped into prediction vectors by the classifier. The cross-entropy loss function is used to optimize the classifier. For unsupervised learning, the unlabeled ECGs are weakly and strongly augmented, and a consistency loss is used to minimize the difference between the classifier’s predictions for the two augmentations. Pseudo-labeling techniques include positive pseudo-labeling (PL) and ranking-based negative pseudo-labeling (RNL). PL introduces pseudo-labels for data with high prediction confidence. RNL assigns negative pseudo-labels to the lower-ranked categories in the prediction vectors to leverage data with low prediction confidence. In this study, VGGNet and ResNet are used as classifiers, which are jointly optimized by labeled and unlabeled ECGs. Results:CPSS has been validated on several databases. With the same number of labeled ECGs (10%), it improves the accuracies over pure supervised learning by 13.59%, 4.60%, and 5.38% in the CPSC2018, PTB-XL, and Chapman databases, respectively. CPSS achieves comparable results to the fully supervised method with only 10% of labeled ECGs, which reduces the labeling workload by 90%. In addition, to verify the practicality of CPSS, a cardiovascular disease monitoring system is designed by heterogeneously deploying the trained classifiers on an SoC (system-on-a-chip), which can detect CVD in real time. Conclusion:The results of this study indicate that the proposed CPSS can significantly improve the performance of CVD detection using unlabeled ECG, which reduces the burden of ECG labeling in deep learning. In addition, the designed monitoring system makes the proposed CPSS promising for real-world applications.

Deep learning-assisted intelligent wearable precise cardiovascular monitoring system

Deep learning-assisted intelligent wearable precise cardiovascular monitoring system

Recent Progress in Wearable Self‐Powered Biomechanical Sensors: Mechanisms and Applications

AbstractBiomechanical signals, such as strain variations of the skin, vibrations of the chest and throat, as well as motions of the limbs, hold immense significance in healthcare monitoring, disease diagnosis, and human‐machine interface. Examples span from monitoring blood pressure and pulse waves for atherosclerosis diagnosis, to distinguishing between metatarsalgia patients and healthy individuals by tracking their walking postures, and to voiceprint recognition and hearing aid technology based on vibration sensing. Wearable biomechanical sensors play a crucial role in providing valuable insights into one's health condition and physiological features. However, the development of high‐performance sensors capable of prolonged monitoring poses challenges. Traditional batteries have limited lifespan and pose difficulty in replacement. Using self‐powered devices for the measurement of biomechanical signals represents an attractive solution to tackle the issues caused by batteries. This review focuses on the mechanisms of wearable self‐powered biomechanical sensors, and delves into recent advancements in their applications, covering areas of cardiovascular system monitoring, acoustic signals detection, human motion tracking, and many others associated with biomechanics. A concluding section outlines the potential future prospects in this evolving field of materials and biomedical research.

Tablet-Based Wearable Patch Sensor Design for Continuous Cardiovascular System Monitoring in Postoperative Settings

Meticulous monitoring for cardiovascular systems is important for postoperative patients in postanesthesia or the intensive care unit. The continuous auscultation of heart and lung sounds can provide a valuable information for patient safety. Although numerous research projects have proposed the design of continuous cardiopulmonary monitoring devices, they primarily focused on the auscultation of heart and lung sounds and mostly served as screening tools. However, there is a lack of devices that could continuously display and monitor the derived cardiopulmonary parameters. This study presents a novel approach to address this need by proposing a bedside monitoring system that utilizes a lightweight and wearable patch sensor for continuous cardiovascular system monitoring. The heart and lung sounds were collected using a chest stethoscope and microphones, and a developed adaptive noise cancellation algorithm was implemented to remove the background noise corrupted with those sounds. Additionally, a short-distance ECG signal was acquired using electrodes and a high precision analog front end. A high-speed processing microcontroller was used to allow real-time data acquisition, processing, and display. A dedicated tablet-based software was developed to display the acquired signal waveforms and the processed cardiovascular parameters. A significant contribution of this work is the seamless integration of continuous auscultation and ECG signal acquisition, thereby enabling the real-time monitoring of cardiovascular parameters. The wearability and lightweight design of the system were achieved through the use of rigid–flex PCBs, which ensured patient comfort and ease of use. The system provides a high-quality signal acquisition and real-time monitoring of the cardiovascular parameters, thus proving its potential as a health monitoring tool.

Developing a Low-Cost IoT based Remote Cardiovascular Patient Monitoring System in Pakistan

Several issues with conventional cardiovascular monitoring devices are intended to be addressed by a low-cost IoT-based remote cardiovascular patient monitoring system. These difficulties include the high cost of conventional devices, poor accessibility, and the requirement for repeated trips to the doctor. The system enables patients to remotely monitor their vital signs and receive real-time notifications in case of any irregularities, making it a more affordable and accessible option than traditional monitoring equipment. As a result, patients will require fewer trips to the hospital, increasing accessibility and convenience of healthcare. In case of any irregularities or trends that call for medical attention, the system also provides continuous monitoring, ensuring prompt care. The low-cost IoT-based remote cardiovascular patient monitoring system has the potential to revolutionize the way healthcare is provided, particularly in situations with limited resources. It does this by offering patients with cardiovascular problems constant, affordable, and accessible monitoring. Additionally, it can lessen the strain on medical facilities, freeing up resources and enabling healthcare professionals to concentrate on cases that are more urgent. This system is therefore necessary due to the drawbacks of conventional monitoring devices, and it can address the issues of limited accessibility and cost-effectiveness by giving patients a more practical and effective way to monitor their vital signs and receive prompt medical care.

Developing a Low-Cost IoT-Based Remote Cardiovascular Patient Monitoring System in Cameroon.

(1) Background: Cameroonians are exposed to poor health services, more so citizens with cardiovascular-related diseases. The global high cost of acquiring healthcare-related technologies has prompted the government and individuals to promote the need for local research and the development of the health system. (2) Objectives: The main goal of this study is to design and develop a low-cost cardiovascular patient monitoring system (RPM) with wireless capabilities that could be used in any region of Cameroon, accessible, and very inexpensive, that are able to capture important factors, well reflecting the patient's condition and provide alerting mechanisms. (3) Method: Using the lean UX process from the Gothelf and Seiden framework, the implemented IoT-based application measures the patients' systolic, diastolic, and heart rates using various sensors, that are automated to record directly to the application database for analysis. The validity of the heuristic evaluation was examined in an ethnographic study of paramedics using a prototype of the system in their work environment. (4) Results: We obtained a system that was pre-tested on demo patients and later deployed and tested on seven real human test subjects. The users' task performances partially verified the heuristic evaluation results. (5) Conclusions: The data acquired by the sensors have a high level of accuracy and effectively help specialists to properly monitor their patients at a low cost. The proposed system maintains a user-friendliness as no expertise is required for its effective utilization.

Dependence of Skin-Electrode Contact Impedance on Material and Skin Hydration.

Dry electrodes offer an accessible continuous acquisition of biopotential signals as part of current in-home monitoring systems but often face challenges of high-contact impedance that results in poor signal quality. The performance of dry electrodes could be affected by electrode material and skin hydration. Herein, we investigate these dependencies using a circuit skin-electrode interface model, varying material and hydration in controlled benchtop experiments on a biomimetic skin phantom simulating dry and hydrated skin. Results of the model demonstrate the contribution of the individual components in the circuit to total impedance and assist in understanding the role of electrode material in the mechanistic principle of dry electrodes. Validation was performed by conducting in vivo skin-electrode contact impedance measurements across ten normative human subjects. Further, the impact of the electrode on biopotential signal quality was evaluated by demonstrating an ability to capture clinically relevant electrocardiogram signals by using dry electrodes integrated into a toilet seat cardiovascular monitoring system. Titanium electrodes resulted in better signal quality than stainless steel electrodes. Results suggest that relative permittivity of native oxide of electrode material come into contact with the skin contributes to the interface impedance, and can lead to enhancement in the capacitive coupling of biopotential signals, especially in dry skin individuals.

Mechanical Sensors for Cardiovascular Monitoring: From Battery-Powered to Self-Powered.

Cardiovascular disease is one of the leading causes of death worldwide. Long-term and real-time monitoring of cardiovascular indicators is required to detect abnormalities and conduct early intervention in time. To this end, the development of flexible wearable/implantable sensors for real-time monitoring of various vital signs has aroused extensive interest among researchers. Among the different kinds of sensors, mechanical sensors can reflect the direct information of pressure fluctuations in the cardiovascular system with the advantages of high sensitivity and suitable flexibility. Herein, we first introduce the recent advances of four kinds of mechanical sensors for cardiovascular system monitoring, based on capacitive, piezoresistive, piezoelectric, and triboelectric principles. Then, the physio-mechanical mechanisms in the cardiovascular system and their monitoring are described, including pulse wave, blood pressure, heart rhythm, endocardial pressure, etc. Finally, we emphasize the importance of real-time physiological monitoring in the treatment of cardiovascular disease and discuss its challenges in clinical translation.

Kirigami-Inspired Pressure Sensors for Wearable Dynamic Cardiovascular Monitoring.

Continuously and accurately monitoring pulse-wave signals is critical to prevent and diagnose cardiovascular diseases. However, existing wearable pulse sensors are vulnerable to motion artifacts due to the lack of proper adhesion and conformal interface with human skin during body movement. Here, a highly sensitive and conformal pressure sensor inspired by the kirigami structure is developed to measure the human pulse wave on different body artery sites under various prestressing pressure conditions and even with body movement. COMSOL multiphysical field coupling simulation and experimental testing are used to verify the unique advantages of the kirigami structure. The device shows a superior sensitivity (35.2mV Pa-1 ) and remarkable stability (>84000 cycles). Toward practical applications, a wireless cardiovascular monitoring system is developed for wirelessly transmitting the pulse signals to a mobile phone in real-time, which successfully distinguished the pulse waveforms from different participants. The pulse waveforms measured by the kirigami inspired pressure sensor are as accurate as those provided by the commercial medical device. Given the compelling features, the sensor provides an ascendant way for wearable electronics to overcome motion artifacts when monitoring pulse signals, thus representing a solid advancement toward personalized healthcare in the era of the Internet of Things.

A Low-Cost Non-Invasive Continuous Cardiovascular Monitoring System Based on Oil-Filled Rubber Tubing

Cardiovascular monitoring at home is highly desirable for managing diseases such as heart failure. Existing technologies are either inconvenient, too costly, or incapable of acquiring clinically valuable information. We developed a new low-cost and sensitive monitoring system based on rubber tubing, mineral oil, and an off-the-shelf pressure sensor. The rubber tube can be installed underneath bed mattresses and chair cushions to convert the ballistocardiogram (BCG) signal caused by heartbeat to pressure change, which is then detected by the pressure sensor. The sensitivity is increased by filling the tube with mineral oil based on Boyle’s gas law (PV = Constant). Preliminary tests demonstrated that the new monitoring system can record BCG with high sensitivity from the bed. From the recorded signal, both heart rate and respiration rate can be extracted. Other valuable information such as myocardial strength and respiration strength can be potentially derived as well. The new monitoring system is low-cost, sensitive, robust, unattended, and does not need to attach any device to the patient. This new monitoring system will be beneficial for the monitoring of health conditions such as heart failure, sleep apnea, epilepsy, and respiratory diseases such as COVID-19.

Piezoelectric Micromachined Ultrasonic Transducer for Arterial Wall Dynamics Monitoring.

In this article, a [Formula: see text] piezoelectric micromachined ultrasonic transducer (PMUT) array is designed and driven with one cycle of a 5-MHz sinusoid at 10 [Formula: see text] for radial artery motion tracking. The transmit and receive performance figure of merit (FOM) of an individual PMUT over operating frequency is modeled and validated using laser Doppler vibrometer (LDV) measurements. Given a fixed cross section, the FOM inversely scales with frequency. The array aperture size is selected to obtain enough pressure and received signal to measure the radial artery wall reflection at a 5-mm depth in tissue. The 2-mm acoustic beamwidth provides enough lateral resolution for radial artery wall motion tracking. Single-line ultrasonic pulse-echo measurements with high time resolution, also called M-mode ultrasound imaging, are demonstrated to reproduce a known target motion profile with a precision of around 0.5 [Formula: see text]. In vivo radial artery dynamics are measured by placing the sensor on the wrist of a volunteer. The measured diameter change waveform of the radial artery is consistent with reports in the literature and captures key arterial pulse waveform features, including systolic upstroke, systolic decline, dicrotic notch, and diastolic runoff. The system has sufficient accuracy and precision to measure both the 50 [Formula: see text] overall diameter change and the 5- [Formula: see text] diameter change due to the dicrotic notch. A heart rate of 70 beats/min is also derived. This demonstrates the great potential of custom PMUT arrays for continuous cardiovascular system monitoring.

Skin-conformable photoplethysmogram sensors for energy-efficient always-on cardiovascular monitoring systems

Skin-conformable photoplethysmogram (PPG) sensors enable continuous and accurate monitoring of physiological states to efficiently prevent cardiovascular-related diseases. Herein, novel PPG sensors consisting of polymer/oxide hybrid phototransistors, mini-light-emitting diodes, and a framework conformable to epidermis are developed. The key element, a heterojunction phototransistor for efficient energy usage, is composed of an indium gallium zinc oxide (IGZO)-based active layer for low-power consumption and a specific diketopyrrolopyrrole (DPP) polymer layer affording high near-infrared (NIR) light absorbability and hydrophobicity. Therefore, the phototransistors with NIR detectivity of 1.00 × 1013 Jones, rapid photoresponse within the human heart rate range, high reliability against perspiration and mechanical stress, and low operating voltages (< 5 V) are achieved. Using the developed PPG sensors, the heart rate and oxygen saturation of human subjects are successfully detected, which is comparable to the commercial PPG sensors. Furthermore, controlling potential barrier energy at the interface between heterojunction layers, PPG sensors that operate separately at low and high heart rates are implemented for continuous monitoring. Consequently, a distinguished configuration of skin-conformable PPG sensors and a novel concept of an always-on cardiovascular monitoring system while consuming less power are suggested. The study contributes to the development of PPG sensors and may become a potential solution for Healthcare 4.0 applications.

Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor (Adv. Mater. 41/2021)

Personalized Healthcare In article number 2104178, Jun Chen and co-workers report a waterproof textile-based wearable cardiovascular monitoring system that is developed via integration of a triboelectric sensor, a signal-processing circuit, a Bluetooth module, and a customized user-friendly app interface. With the assistance of machine-learning algorithms, this system can perform continuous blood-pressure monitoring against body motion and perspiration.

Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach.

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%.

Learning Aided Estimation of Human Emotion using Speech and ECG Signal for Design of a Contactless Cardio-Vascular Monitoring System

Learning Aided Estimation of Human Emotion using Speech and ECG Signal for Design of a Contactless Cardio-Vascular Monitoring System

Big-ECG: Cardiographic Predictive Cyber-Physical System for Stroke Management

Electrocardiogram (ECG) is sensitive to autonomic dysfunction and cardiac complications derived from ischemic or hemorrhage stroke and is supposed to be a potential prognostic tool in stroke identification and post-stroke treatment. ECG data generated cannot be real-time accumulated, processed, and used for enterprise-level healthcare and wellness services with the existing cardiovascular monitoring system used in hospitals. This study aims to assess the feasibility of a cyber-physical cardiac monitoring system to classify stroke patients with altered cardiac activity and healthy adults. Here, we propose Big-ECG, a cyber-physical cardiac monitoring system for stroke management, consisting of a wearable ECG sensor, data storage and data analysis in a big data platform, and health advisory services using data analytics and medical ontology. We investigated our proposed ECG-based patient monitoring system with 45 stroke patients (average age 70.8 years old, 68% men) admitted to the rehabilitation center of the hospital and 40 healthy elderly volunteers (average age 75.4 years old, 38% men). We recorded ECG at resting state using a single-channel ECG patch within three months of diagnosis of ischemic stroke (clinically confirmed). In statistical results, ECG fiducial features, RR-I, QRS, QT, ST, and heart rate variability (HRV) features, SDSD, LF/HF, LF/(LF + HF), and HF/(LF + HF) are observed as significantly distinctive biomarkers for the stroke group relative to the healthy control group. The Random Trees model presented the best classification performance (overall accuracy: 95.6%) utilizing ECG fiducial variables. This system may assist healthcare enterprises in prognosis and rehabilitation management during post-stroke treatment.

Self-powered ultrasensitive pulse sensors for noninvasive multi-indicators cardiovascular monitoring

Abstract Noninvasive cardiovascular monitoring via pulse sensor is highly desirable for diagnosis and prevention of cardiovascular diseases. However, long-term and multi-indicators cardiovascular monitoring through pulse sensor is still great challenge in practical application. Here, a self-powered ultrasensitive pulse sensor (SUPS) based on triboelectric nanogenerator is developed for noninvasive multi-indicators cardiovascular monitoring. Owning to the synergistic engineering of materials surface and device structure, the SUPS holds excellent sensing performances including the ultrasensitivity of 10.29 nA/kPa, fast response time of 30 ms, and low detection limit of 5 mg. The SUPS achieves the precise detection of pulse waves from varies arteries, and the detailed characteristic peaks in pulse waveforms can be distinguished clearly. Furthermore, a novel strategy for noninvasive multi-indicators cardiovascular monitoring is proposed through the integration of SUPSs distributed on different arterial pulse sites. Cardiovascular indicators including heart rate, pulse wave velocity, and blood pressure are monitored based on the peak position differences extracted from detected pulse waveforms, and all monitored indicators show highly consistency with the results from commercial medical instruments. The developed SUPS enables the noninvasive, long-term and accurate monitoring of cardiovascular system, which is expected to be applied in diagnosis and prevention of cardiovascular diseases in the future.

Sex Differences in Vagus Nerve Stimulation Effects on Rat Cardiovascular and Immune Systems.

BackgroundInvestigations into the benefits of vagus nerve stimulation (VNS) through pre-clinical and clinical research have led to promising findings for treating several disorders. Despite proven effectiveness of VNS on conditions such as epilepsy and depression, understanding of off-target effects and contributing factors such as sex differences can be beneficial to optimize therapy design.New MethodsIn this article, we assessed longitudinal effects of VNS on cardiovascular and immune systems, and studied potential sex differences using a rat model of long-term VNS. Rats were implanted with cuff electrodes around the left cervical vagus nerve for VNS, and wireless physiological monitoring devices for continuous monitoring of cardiovascular system using electrocardiogram (ECG) signals. ECG morphology and heart rate variability (HRV) features were extracted to assess cardiovascular changes resulting from VNS in short-term and long-term timescales. We also assessed VNS effects on expression of inflammatory cytokines in blood during the course of the experiment. Statistical analysis was performed to compare results between Treatment and Sham groups, and between male and female animals from Treatment and Sham groups.ResultsConsiderable differences between male and female rats in cardiovascular effects of VNS were observed in multiple cardiovascular features. However, the effects seemed to be transient with approximately 1-h recovery after VNS. While short-term cardiovascular effects were mainly observed in male rats, females in general showed more significant long-term effects even after VNS stopped. We did not observe notable changes or sex differences in systemic cytokine levels resulting from VNS.Comparison With Existing MethodsCompared to existing methods, our study design incorporated wireless physiological monitoring and systemic blood cytokine level analysis, along with long-term VNS experiments in unanesthetized rats to study sex differences.ConclusionThe contribution of sex differences for long-term VNS off-target effects on cardiovascular and immune systems was assessed using awake behaving rats. Although VNS did not change the concentration of inflammatory biomarkers in systemic circulation for male and female rats, we observed significant differences in cardiovascular effects of VNS characterized using ECG morphology and HRV analyses.

Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals

This article introduces an integrated photoplethysmographic (PPG) based cardiovascular monitoring system that consists of an individually portable PPG device for recording photoplethysmographic signals and a software system with a serverless architecture for processing, storing, and analyzing the obtained signals. The portable device uses the optical plethysmography technique for measuring blood volume in blood vessels. The device was tested and validated by a comparative analysis of three photoplethysmographic signals and one Electrocardiographic signal registered simultaneously in the target subject. The comparative analysis of these signals shows insignificant deviations in the obtained results, with the mean squared error between the studied signals being less than 21 ms. This deviation cannot affect the results that were obtained from the analysis of the interval series tested. Based on this result, we assume that the detected signals with the proposed device are realistic. The designed software system processes the registered data, performs preprocessing, determines the pulse rate variability, and performs mathematical analysis of PP intervals. Two groups of subjects were studied: 42 patients with arrhythmia and 40 healthy controls. Mathematical methods for data analysis in time and frequency domain and nonlinear methods (Poincaré plots, Rescaled Range Plot, Detrended Fluctuation Analysis, and MultiFractal Detrended Fluctuation Analysis) are applied. The obtained results are presented in tabular form and some of them in graphical form. The parameters studied in the time and frequency domain, as well as with the nonlinear methods, have statistical significance (p &lt; 0.05) and they can distinguish between the two studied groups. Visual analysis of PP intervals, based on Poincare’s nonlinear method, provides important information on the physiological status of patients, allowing for one to see at a glance the entire PP interval series and quickly detect cardiovascular disorders, if any. The photoplethysmographic data of healthy individuals and patients diagnosed with arrhythmia were recorded, processed, and examined through the system under the guidance of a cardiologist. The results were analyzed and it was concluded that this system could serve to monitor patients with cardiovascular diseases and, when the condition worsens, a signal could be generated and sent to the hospital for undertaking immediate measures to stabilize patient’s health.

Damages of Cardiovascular System at Mucopolysaccharidosis Type I: Clinical Cases

Background. Cardiac involvement in patients with mucopolysaccharidosis (MPS) type I, or Hurler syndrome, has various phenotypes. Clinical Case Description . In the first case symptoms were indicative of acute severe heart failure which was confirmed by laboratory and instrumental diagnostic methods. Left heart chambers dilatation and left ventricular hypocontractility were revealed on echocardiography. Atypical disease course with no improvement on multicomponent therapy of heart failure let us to think about metabolic disease, so we confirmed it with tandem mass spectrometry and molecular genetic testing. Therefore this led to timely enzyme replacement therapy onset and allogeneic bone marrow transplantation that positively affect the disease outcome. The second case showed classic course of MPS I. Its clinical signs such as musculoskeletal and ENT-organ manifestations allowed us to diagnosis and later confirm it by tandem mass spectrometry and molecular genetic testing at the age of 3. The cardiac pathology presented by mitral valve leaflets thickening and 2nd degree regurgitation has been diagnosed later. In our view, early treatment onset should slow down the progression of heart damage. Conclusion. Several clinical variants of cardiac pathology at MPS I are presented. The need of constant cardiovascular system monitoring in children with MPS I is shown. It is also mentioned that cardiac pathology can be the first manifestation of the disease.