Monitoring Of Patient Vital Signs Research Articles

Through the wall human heart beat detection using single channel CW radar.

Single-channel continuous wave (CW) radar is widely used and has gained popularity due to its simple architecture despite its inability to measure the range and angular location of the target. Its popularity arises in the industry due to the simplicity of the required components, the low demands on the sampling rate, and their low costs. Through-the-wall life signs detection using microwave Doppler Radar is an active area of research and investigation. Most of the work in the literature focused on utilizing multi-channel frequency modulated continuous wave (FMCW), CW, and ultra-wideband (UWB) radar for their capability of range and direction of arrival (DOA) estimation. In this paper, through-the-wall single-subject and two-subject concurrent heart rate detection using single-channel 24-GHz CW radar leveraged with maximal overlap discrete wavelet transform (MODWT) is proposed. Experimental results demonstrated that the repetitive measurement of seven different subjects at a distance of 20cm up to 100cm through two different barriers (wood and brick wall) showed an average accuracy of heart rate extraction of 95.27% for varied distances (20-100cm) in comparison with the Biopac ECG acquisition signal. Additionally, the MODWT method can also isolate the independent heartbeat waveforms from the two subjects' concurrent measurements through the wall. This involved four trials with eight different subjects, achieving an accuracy of 97.04% for a fixed distance of 40cm from the Radar without estimating the angular location of the subjects. Notably, it also superseded the performance of the direct FFT method for the single subject after 40cm distance measurements. The proposed simpler architecture of single-channel CW radar leveraged with MODWT has several potential applications, including post-disaster search and rescue scenarios for finding the trapped, injured people under the debris, emergency evacuation, security, surveillance, and patient vital signs monitoring.

IoT-based real-time patients vital physiological parameters monitoring system using smart wearable sensors.

Health care is one of the least funded sectors in Bangladesh and many other similar developing countries. People living in rural and remote areas do not have access to proper health care, and when they do, it is too expensive. This research aimed to develop a real-time health monitoring system that is cheap, easy to use, and accessible by doctors and patients. The system consists of several Internet of Things (IoT)-based sensors connected to an Arduino microprocessor, which thus measures the vital body signs of the patients. The measured readings are then transmitted to an Android application on a smartphone via a Bluetooth module. The sensors are connected to analog inputs. These sensors measure analog data, which is amplified by the microprocessor after being sorted. Doctors can also carry out the diagnosis of ailments using the data collected remotely from the patient. An Android-based mobile application that interfaces with a web-based application is implemented for efficient patients-doctors dual real-time communication. The Android application, which is connected to a MySQL database, updates the said database, which updates and displays the readings on a website accessible by both doctors and patients. Initially, the health monitor was tested using an Arduino Integrated Development Environment (IDE) monitor and a single user. Once initial simulations were successful, the proposed system was tested on five different real-human test subjects. The testing of the wireless health monitor produced successful results that measured patient vitals with a high level of accuracy. The proposed IoT-based system monitors vital signs such as the patient’s body temperature, heart rate, ECG, SpO2 levels, blood pressure, and glucose levels. This system also includes a medical treatment plan by the doctors. The proposed system is novel as it integrates the IoT-based patient monitoring system with telemedicine. This proposed system has different sensors for real-time measuring the vital signs of the human body. A mobile and web application have also been integrated with this system for real-time remote patient monitoring and treatment plan. There are now systems available that only offer a telemedicine facility, where patients and doctors can have discussions, but do not have an IoT-based patient vital sign monitoring system integrated with telemedicine. The proposed system in this paper has the facility of IoT-based patient vital signs monitoring integrated with telemedicine, which makes this research work novel. The proposed system will increase the life expectancy of people throughout the world.

Effect of continuous wireless vital sign monitoring on unplanned ICU admissions and rapid response team calls: a before-and-after study

BackgroundContinuous vital sign monitoring may potentially be improved through the use of wearable monitors linked wirelessly to hospital electronic patient records. By improving early detection of physiological deterioration this approach may save lives. MethodsWe performed a single-centre before-and-after study including surgical and medical patients at a university hospital in The Netherlands. The study intervention was continuous vital sign monitoring using wearable monitors linked wirelessly to hospital systems. The co-primary outcomes were unplanned ICU admission and rapid response team calls. Secondary outcomes were length of hospital stay and in-patient death. ResultsOur baseline cohort included 2466 admissions and our intervention cohort included 2303 admissions recruited from August 2017 to July 2019. Patients in the intervention cohort experienced fewer unplanned ICU admissions (84 [3.4%] vs 54 [2.3%]; P=0.03) and fewer rapid response team calls (107 [4.3%] vs 71 [3.1%]; P=0.02). The number of rapid response team calls that did not result in ICU admission also declined (70 [2.8%] vs 45 [2.0%]; P=0.05). The number of rapid response team calls that did result in ICU admission was not significantly different (52 [2.1%] vs 36 [1.6%]; P=0.16). There were no differences in hospital stay or in-patient deaths between the two study periods. ConclusionsContinuous monitoring of patient vital signs using wearable monitoring technology linked wirelessly to hospital systems was associated with a reduction in unplanned ICU admissions and rapid response team calls. Further research is necessary to confirm the impact of this approach on patient survival.

An IoT-Based Smart System with an MQTT Broker for Individual Patient Vital Sign Monitoring in Potential Emergency or Prehospital Applications.

Emergency care is a critical area of medicine whose outcomes are influenced by the time, availability, and accuracy of contextual information. The success of critical or emergency care is determined by the quality and accuracy of the information received during the emergency call and the data collected during emergency transportation. The Internet of Things (IoT) consists of many smart devices and components that communicate via their connection to the Internet, which is used to collect data with sensors that obtain personal health parameters. In the past, most health measurement systems were based on a single dedicated orientation, and few systems had multiple devices on the same platform. In addition to traditional health measurement technologies, most such systems use centralized data transmission, which means that health measurement data have become the exclusive intellectual asset of the system developer. Therefore, this study develops an IoT-based message-broker system that is deployed and demonstrated for five health devices: blood oxygen, blood pressure, forehead temperature, body temperature, and body weight sensors. A central controller accessed by radio-frequency identification (RFID) collects clients' health profiles on the cloud platform. All collected data can be quickly shared, analyzed, and visualized, and the health devices can be changed, added to, and removed reliably when the requirements change. Additionally, following the message queuing telemetry transport (MQTT) protocol, all devices can communicate with each other and be integrated into a higher-level health measurement standard (such as blood pressure plus weight or body temperature plus blood oxygen). We implement a smart healthcare monitoring system (SHMS) and verify its reliability. We use MQTT to establish an open communication format that other organizations can follow to perform individual patient vital sign monitoring in potential applications. The robustness and flexibility of this research can be verified through the addition of other systems. Through this structure, more large-scale health detection devices can be integrated into the method proposed in this research in the future. Personal RFID or health insurance cards can be used for personal services or in medical institutions, and the data can easily be shared through the mechanism of this research. Such information sharing will enable the utilization of medical resources to be maximized.

Mobile Health Applications to Support COVID-19 Self-management: Review and Evaluation

Aim: During the epidemic and with an increase in coronavirus (COVID-19) disease prevalence, emergency care is essential to help people stay informed and undertake self-management measures to protect their health. One of these self-management procedures is the use of mobile apps in health. Mobile health (mHealth) applications include mobile devices in collecting clinical health data, sharing healthcare information for practitioners and patients, real-time monitoring of patient vital signs, and the direct provision of care (via mobile telemedicine). Mobile apps are increasing to improve health, but before healthcare providers can recommend these applications to patients, they need to be sure the apps will help change patients' lifestyles.
 Method: A search was conducted systematically using the keywords "Covid-19," "Coronavirus," "Covid-19, and Self-management" at the "Apple App Store". Then we evaluated the apps according to MARS criteria in May 2020.
 Results: A total of 145 apps for COVID-19 self-management were identified, but only 32 apps met our inclusion criteria after being assessed. The overall mean MARS score was 2.9 out of 5, and more than half of the apps had a minimum acceptability score (range 2.5-3.9). The "who academy" app received the highest functionality score. Who Academy, Corona-Care and First Responder COVID-19 Guide had the highest scores for behavior change.
 Conclusion: Our findings showed that few apps meet the quality, content, and functionality criteria for Covid-19 self-management. Therefore, developers should use evidence-based medical guidelines in creating mobile health applications so that, they can provide comprehensive and complete information to both patients and healthcare provider.

Design and Implementation of Real-Time Health Vital Sign Monitoring Device with Wireless Sensor-based on Arduino Mega

This paper discusses the realization of vital sign monitoring devices and knowing their performance. This device is expected to assist medical personnel in measuring and monitoring vital signs without having to have physical contact with patients. The design phase of the patient's vital sign monitoring device with an Arduino Mega-based wireless sensor starts from the identification, needs analysis, design, manufacture, troubleshoot, and testing. Arduino Mega and ESP8266 as the main components that function for the controller and upload data to the server. Then MAX30100, DS18B20, and MPX5700AP sensors which function to detect the vital sign. Android smartphones are used to display measurement results in real-time, save and display vital sign data records. Based on the test results, the patient's vital signs monitoring device has an average difference of 0.67% for temperature checks, 1.19% for pulse checks, 0.77% for SPO2 examinations, 4.78%, and 8.91% for systolic and diastolic blood pressure examinations.

The Use of Head-Worn Displays for Vital Sign Monitoring in Critical and Acute Care: Systematic Review.

BackgroundContinuous monitoring of patient vital signs may improve patient outcomes. Head-worn displays (HWDs) can provide hands-free access to continuous vital sign information of patients in critical and acute care contexts and thus may reduce instances of unrecognized patient deterioration. ObjectiveThe purpose of the study is to conduct a systematic review of the literature to evaluate clinical, surrogate, and process outcomes when clinicians use HWDs for continuous patient vital sign monitoring. MethodsThe review was registered with PROSPERO (CRD42019119875) and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A literature search was conducted for articles published between January 1995 and June 2020 using the following databases: PubMed, Embase, CINAHL, PsycINFO, and Web of Science. Overall, 2 reviewers independently screened titles and abstracts and then assessed the full text of the articles. Original research articles that evaluated the clinical, surrogate, or process outcomes of head-mounted displays for continuous vital sign monitoring in critical care or acute care contexts were included. ResultsOf the 214 records obtained, 15 (7%) articles met the predefined criteria and were included in this review. Of the 15 studies, 7 (47%) took place in a clinical context, whereas the remainder took place in a simulation environment. In 100% (7/7) of the studies that evaluated gaze behavior, changes were found in gaze direction with HWDs. Change detection improvements were found in 67% (2/3) of the studies evaluating changes in the participants’ ability to detect changes in vital signs. Of the 10 studies assessing the ease of use of the HWD, most participants of 7 (70%) studies reported that the HWD was easy to use. In all 6 studies in which participants were asked if they would consider using the HWD in their practice, most participants responded positively, but they often suggested improvements on the HWD hardware or display design. Of the 7 studies conducted in clinical contexts, none reported any clinical outcomes. ConclusionsAlthough there is limited and sometimes conflicting evidence about the benefits of HWDs from certain surrogate and process outcomes, evidence for clinical outcomes is lacking. Recommendations are to employ user-centered design when developing HWDs, perform longitudinal studies, and seek clinical outcomes. Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42019119875; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=119875

Development of a new additive manufacturing platform for direct freeform 3D printing of intrinsically curved flexible membranes

The wearable technology market has been expanding from wearable medical devices for non-invasive continuous monitoring of patient vital signs to wearable devices for tracking fitness activities that any person can access. Regardless of their form or function, desirable characteristics of wearable devices are the ability to be flexible, conformal, and easily attachable to the human body. However, as the human body is intrinsically curved and irregular, flat devices often have poor interfacial adhesion with the human body. This often leads to interfacial delamination and eventual detachment of the device. Therefore, a new additive manufacturing (AM) platform, a direct freeform 3D printing process (DF3DP), is proposed to allow direct construction of intrinsically curved 3D surfaces during the material deposition phase without the need for any pre-shaped supporting molds or templates. This 3D freeform printing process involves a supporting matrix made up of calcium alginate microgels, printing material made from silicone ink, and freeform printing paths derived from customized G-codes that conform exactly to the scanned human surface profile. Curved meshes mimicking the human elbow were used as a demonstration. A static contact stability test showed that the printed 3D silicone mesh was highly conformal to the model elbow surface as compared to a 2D flat mesh. A dynamic contact stability test was also conducted by subjecting both meshes to 100 cycles of mechanical flexion and extension, proving that intrinsically curved surfaces can provide better contact stability for complex human body surfaces undergoing motion than can flat surfaces. These results have proven that intrinsically curved membranes or structures fabricated by DF3DP can reduce the interfacial shear stress and occurrence of cracks and delamination while maintaining structural integrity and stability during use without compromising the comfort of the users. Our approach can resolve interfacial issues in flexible substrates and has great potential for epidermal devices or soft robotics via its long-term sustainable performance.

Towards a Remote Monitoring of Patient Vital Signs Based on IoT-Based Blockchain Integrity Management Platforms in Smart Hospitals.

Over the past several years, many healthcare applications have been developed to enhance the healthcare industry. Recent advancements in information technology and blockchain technology have revolutionized electronic healthcare research and industry. The innovation of miniaturized healthcare sensors for monitoring patient vital signs has improved and secured the human healthcare system. The increase in portable health devices has enhanced the quality of health-monitoring status both at an activity/fitness level for self-health tracking and at a medical level, providing more data to clinicians with potential for earlier diagnosis and guidance of treatment. When sharing personal medical information, data security and comfort are essential requirements for interaction with and collection of electronic medical records. However, it is hard for current systems to meet these requirements because they have inconsistent security policies and access control structures. The new solutions should be directed towards improving data access, and should be managed by the government in terms of privacy and security requirements to ensure the reliability of data for medical purposes. Blockchain paves the way for a revolution in the traditional pharmaceutical industry and benefits from unique features such as privacy and transparency of data. In this paper, we propose a novel platform for monitoring patient vital signs using smart contracts based on blockchain. The proposed system is designed and developed using hyperledger fabric, which is an enterprise-distributed ledger framework for developing blockchain-based applications. This approach provides several benefits to the patients, such as an extensive, immutable history log, and global access to medical information from anywhere at any time. The Libelium e-Health toolkit is used to acquire physiological data. The performance of the designed and developed system is evaluated in terms of transaction per second, transaction latency, and resource utilization using a standard benchmark tool known as Hyperledger Caliper. It is found that the proposed system outperforms the traditional health care system for monitoring patient data.

Design of Wireless Vital Signs Monitoring System for Indoor Monitoring Activity

Patient vital signs monitoring is an important activity in medical field, the situation in real medical facility usually does not allow for continuous monitoring. In a medical facility such as hospital or health care center, the patients outnumbered the staffs which makes it difficult to focus on each patient at a time. This problem can decrease chance to efficiently and effectively detecting medical condition. In this paper, we propose a wireless vital signs monitoring system, where multiple patients can be monitored at the same time. The system consists of two parts: Sensor Device to detect and measure patient's vital signs and Monitor Device to present the sensors' measurement result. We use ZigBee protocol for data transmission between devices. We compare our system to previous work to showcase the advantages of our work. We present the performance results related with the ZigBee communication and the display of Graphical User Interface (GUI). The results show that the system is capable of carrying out vital sign monitoring in indoor scenario with interactive measurement display that can help monitor multiple patients simultaneously.

Wireless Sensor Network Based Health Monitoring System for Hypertensive In-Patients

Hypertensive-patient monitoring is a continuous process of observing closely the situation of patient's blood pressure and alerting the appropriate personnel in case of any anomaly. It usually requires the use of non-invasive sensors that are hardwired to bedside monitors. Although, present systems allow continuous monitoring of patient vital signs and limit the patient to the bed, the readings are mostly stored on the system local memory over a period of time before it is assessed for analysis. Hence, the need for a real time hypertensive patients’ monitoring system which can meet up with immediate demands of emergency cases. This paper presents a Wireless Sensor Network (WSN)-based health monitoring system that addressed the aforementioned drawbacks for monitoring hypertensive in-patients. The design of the system comprises of hardware components such as blood pressure sensor, Bluetooth serial communication circuit, sensor node for base station interfaces and software components. Performance evaluation of the designed system gave an accuracy of 89.7% in blood pressure monitoring. The system is also cost effective, reliable and user friendly when compared with existing systems. Keywords— Blood Pressure, Health monitoring, Hypertension, Wireless Sensor Networks

Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring.

Gaussian process regression (GPR) provides a means to generate flexible personalized models of time series of patient vital signs. These models can perform useful clinical inference in ways that population-based models cannot. A challenge for the use of personalized models is that they must be amenable to a wide range of parameterizations, to accommodate the plausible physiology of any patient in the population. Additionally, optimal performance is typically achieved when models are regularized in light of the knowledge of the physiology of the individual patient. In this paper, we describe a method to build GP models with varying complexity (via covariance kernels) and regularization (via fixed priors over hyperparameters) on a patient-specific level, for the purpose of robust vital-sign forecasting. To this end, our results present evidence in support of two main hypotheses: 1) the use of patient-specific models can outperform population-based models for useful clinical tasks, such as vital-sign forecasting; and 2) the optimal values of (hyper)parameters of these models are best determined by sophisticated methods of optimization, due to high correlation between dimensions of the search space. The resulting models are sufficiently robust to inform clinicians of a patient's vital-sign trajectory and warn of imminent deterioration.

Design and Development of Patient Monitoring System

Patient monitoring system allows continuous monitoring of patient vital signs, support decision making among medical personnel and help enhance patient care. This system can consist of devices that measure, display and record human’s vital signs, including body temperature, heart rate, blood pressure and other health-related criteria. This paper proposes a system to monitor the patient’s conditions by monitoring the body temperature and pulse rate. The system consists of a pulse rate monitoring software and a wearable device that can measure a subject’s temperature and pulse rate only by using a fingertip. The device is able to record the measurement data and interface to PC via Arduino microcontroller. The recorded data can be viewed as a historical file or can be archived for further analysis. This work also describes the preliminary experimental results of the selected sensors to show the usefulness of the sensors for the proposed patient monitoring system.

Likelihood-based artefact detection in continuously-acquired patient vital signs.

Robust continuous monitoring of patient vital signs (VS) is limited by artefactual data yielding measurements that are not representative of the patient's physiology. These artefacts are typified by several distinct "archetypes". We present several of these archetypal artefacts for heart rate (HR) monitoring, and propose a light weight, real-time algorithm to remove the majority of these artefacts. Most artefacts are not identifiable by their values in absolute terms, but instead by their values relative to other measurements nearby in time. We model temporally-proximate measurements as independent and identically distributed (i.i.d.) samples from a Gamma distribution. Measurements with low likelihood with respect to the distribution are candidates for artefact removal. This lightweight algorithm is important for real-time deployment on wearable sensors, which are becoming increasingly common in hospital and home care. The clinical applicability of artefact-removal is demonstrated in its ability to enhance patient deterioration detection. A Kalman filter-based patient monitoring algorithm is shown to improve early warning of deterioration when the proposed artefact-removal algorithm is used. We demonstrate this real-time system with patient data from a clinical trial that we have undertaken.

Wearable Doppler Radar with Integrated Antenna for Patient Vital Sign Monitoring

ABSTRACTIn this article, a wearable Doppler radar unit with radio data link in 2.4 GHz for use in portable patient monitoring and emergency response is presented. By using a rotated E-shaped strip in the radiating patch, a new resonance at lower frequencies (2.4 GHz) can be achieved. Also, by embedding cutting a rectangular slot with protruded interdigital strip inside the slot in the feed line a frequency band-stop performance can be achieved. The proposed structure has a major advantage in high harmonic rejection. In order to generate efficient RF to dc conversion, we use a pair of gap distances in the microstrip loop. Unlike portable electrocardiograms (ECG) or photoplethysmography, the near-field Doppler unit enables monitoring of the person’s heart rate without the need for electrical contact or optical access to the patient’s skin. The Doppler unit is designed to be embedded in a clothing garment, such as a shirt or vest, or used by medical emergency personnel in an instrumented blanket or medical stretcher. Since the Doppler unit is placed directly on or behind the patient’s torso, the extraneous signals due to relative motion artifacts are greatly reduced. Low-cost design is achieved by employing pulse wideband microstrip elements for the integrated patch antenna, microwave oscillator, and tuning elements. Also, since the distance between the Doppler unit and the patient is fixed, it was possible to tune the detection phase to enable the use of a single mixer diode and eliminate the need for quadrature detection. This approach is demonstrated using a simple direct conversion radar circuit implemented in a microstrip operating at 2.460 GHz. Heart rate data is shown for detection distances of 0.5 meter. Measured heart data from this technique shows a clear waveform substructure similar to the Personal Questionnaire Rapid Scaling Technique complex features found in captured ECG data.

Case Study: Continuous Monitoring of Patient Vital Signs to Reduce 'Failure-to-Rescue' Events.

Case Study: Continuous Monitoring of Patient Vital Signs to Reduce 'Failure-to-Rescue' Events.

Prévention du risque d’œdème aigu pulmonaire associé à la transfusion

Hydrostatic pulmonary edema is a frequent and severe complication of blood transfusion. Recent epidemiological studies open the way for a better prevention of Transfusion-Associated Circulatory Overload. Preventive measures rely solely on the medical and nursing staff. Mitigation strategies include a careful identification of patients and conditions at-risk, a single-unit transfusion policy in patients with chronic anemia, the use of slow infusion rates, the careful monitoring of patient vital signs (particularly systemic arterial blood pressure). Peritransfusion IV diuretics use is likely to be helpful, although optimal prescribing patterns have not been defined.

Impact of introducing an electronic physiological surveillance system on hospital mortality

BackgroundAvoidable hospital mortality is often attributable to inadequate patient vital signs monitoring, and failure to recognise or respond to clinical deterioration. The processes involved with vital sign collection and charting;...

Probabilistic Novelty Detection With Support Vector Machines

Novelty detection, or one-class classification, is of particular use in the analysis of high-integrity systems, in which examples of failure are rare in comparison with the number of examples of stable behaviour, such that a conventional multi-class classification approach cannot be taken. Support Vector Machines (SVMs) are a popular means of performing novelty detection, and it is conventional practice to use a train-validate-test approach, often involving cross-validation, to train the one-class SVM, and then select appropriate values for its parameters. An alternative method, used with multi-class SVMs, is to calibrate the SVM output into conditional class probabilities. A probabilistic approach offers many advantages over the conventional method, including the facility to select automatically a probabilistic novelty threshold. The contributions of this paper are (i) the development of a probabilistic calibration technique for one-class SVMs, such that on-line novelty detection may be performed in a probabilistic manner; and (ii) the demonstration of the advantages of the proposed method (in comparison to the conventional one-class SVM methodology) using case studies, in which one-class probabilistic SVMs are used to perform condition monitoring of a high-integrity industrial combustion plant, and in detecting deterioration in patient physiological condition during patient vital-sign monitoring.

Anomaly Detection in Medical Wireless Sensor Networks

In this paper, we propose a new framework for anomaly detection in medical wireless sensor networks, which are used for remote monitoring of patient vital signs. The proposed framework performs sequential data analysis on a mini gateway used as a base station to detect abnormal changes and to cope with unreliable measurements in collected data without prior knowledge of anomalous events or normal data patterns. The proposed approach is based on the Mahalanobis distance for spatial analysis, and a kernel density estimator for the identification of abnormal temporal patterns. Our main objective is to distinguish between faulty measurements and clinical emergencies in order to reduce false alarms triggered by faulty measurements or ill-behaved sensors. Our experimental results on both real and synthetic medical datasets show that the proposed approach can achieve good detection accuracy with a low false alarm rate (less than 5.5%).