Open-source controller for dynamic cardiovascular models.

Cardiovascular pressure sensors require dedicated, reliable, and customisable performance testing equipment. Devices available on the market, such as pulsatile pumps and pulse multipliers, offer limited adaptability to the needs of pressure sensor testing or are highly complex tools designed for other purposes. Therefore, there is a strong need to provide an adaptable and versatile device for characterisation during prototype development, prior to animal model testing. Early development requires detailed characterisation of a sensor performance in a realistic environmental scenario. To address this need, we adapted an off-the-shelf pressure chamber with a custom Arduino-based controller to achieve a rapid change in pressure that simulates the pulsatile profile of human blood pressure. The system is a highly customisable tool, and we have experimentally shown that it works successfully in a wide range of pressures from 30 mmHg to 400 mmHg with a resolution of 2 mmHg. By adjusting the chamber volume using a water balloon, we achieved a cycle rate of up to 120 beats per minute. The device can be operated directly from the Arduino IDE or with a customised graphical user interface developed by our research group. The proposed system is intended to assist other researchers in the development of industrial and biomedical pressure sensors.

Performance of wearable watch-type home blood pressure measurement devices in a real-world clinical sample.

BACKGROUND: Independent testing of home blood pressure (BP) measurement (HBPM) devices is often lacking, particularly among older and multi-morbid patients. METHODS: We studied the Bpro G2 (using tonometry), Omron HeartGuide (using occlusive oscillometric technology), and Heartisans (using photoplethysmography) wrist watch HBPM devices against a gold standard brachial sphygmomanometer. To test device performance, we used the ISO81060-2 protocol (though this protocol cannot formally validate cuffless devices). We also used linear mixed models to compare adjusted longitudinal BP measurements between devices. Finally, as a surrogate for usability, we recorded instances of device failure where no BP measurement was returned. RESULTS: We enrolled 128 participants (median [Q1-Q3] age 53 [40-65] years, 51% male, 46% on antihypertensive drugs), of whom 100 were suitable for the primary analysis. All three devices had mean BP values within 5 mmHg of sphygmomanometry. However, due to insufficient reliability (e.g., wider than accepted standard deviations of mean BP), none of the three devices passed all criteria required by the ISO81060-2 protocol. In adjusted longitudinal analyses, the Omron device also systematically underestimated systolic and diastolic BP (- 8.46 mmHg; 95% CI 6.07, 10.86; p < 0.001; and - 2.53 mmHg; 95% CI - 4.03, - 1.03; p = 0.001; respectively). Nevertheless, compared to the Omron device, BPro and Heartisans devices had increased odds of failure (BPro: odds ratio [OR] 5.24; p < 0.0001; Heartisans: OR 5.61; p < 0.001). CONCLUSIONS: While we could not formally validate the cuffless devices, our results show that wearable technologies will require improvements to offer reliable BP assessment. This study also highlights the need for validation protocols specifically designed for cuffless BP measurement technologies.

Development and assessment of the performance of a shared ventilatory system that uses clinically available components to individualize tidal volumes.

OBJECTIVES: To develop and assess a system for shared ventilation using clinically available components to individualize tidal volumes. DESIGN: Evaluation and in vitro validation study SETTING: Ventilator shortage during the SARS-CoV-2 pandemic. PARTICIPANTS: The team consisted of physicians, bioengineers, computer programmers, and medical technology professionals. METHODS: Using clinically available components, a system of ventilation consisting of two ventilatory limbs was assembled and connected to a ventilator. Monitors for each limb were developed using open-source software. Firstly, the effect of altering ventilator settings on tidal volumes delivered to each limb was determined. Secondly, the impact of altering the compliance and resistance of one limb on the tidal volumes delivered to both limbs was analysed. Experiments were repeated three times to determine system variability. RESULTS: The system permitted accurate and reproducible titration of tidal volumes to each limb over a range of ventilator settings and simulated lung conditions. Alteration of ventilator inspiratory pressures, of respiratory rates, and I:E ratio resulted in very similar tidal volumes delivered to each limb. Alteration of compliance and resistance in one limb resulted in reproducible alterations in tidal volume to that test lung, with little change to tidal volumes in the other lung. All tidal volumes delivered were reproducible. CONCLUSIONS: We demonstrate the reliability of a shared ventilation system assembled using commonly available clinical components that allows titration of individual tidal volumes. This system may be useful as a strategy of last resort for Covid-19, or other mass casualty situations, where the need for ventilators exceeds supply.

Optimizing Cardiac Wireless Implant Communication: A Feasibility Study on Selecting the Frequency and Matching Medium.

Cardiac wireless implantable medical devices (CWIMD) have brought a paradigm shift in monitoring and treating various cardiac conditions, including heart failure, arrhythmias, and hypertension. One of the key elements in CWIMD is the implant antenna which uses radio frequency (RF) technology to wirelessly communicate and transmit data to external devices. However, wireless communication with a deeply implanted antenna using RF can be challenging due to the significant loss of electromagnetic (EM) signal at the air-skin interface, and second, due to the propagation and reflection of EM waves from different tissue boundaries. The air-skin interface loss of the EM wave is pronounced due to the absence of a matching medium. This paper investigates the EM propagation losses in the human body and presents a choice of optimal frequency for the design of the cardiac implant antenna and the dielectric properties of the matching medium. First, the dielectric properties of all tissues present in the human thorax including skin, fat, muscle, cartilage, and heart are analyzed as a function of frequency to study the EM wave absorption at different frequencies. Second, the penetration of EM waves inside the biological tissues is analyzed as a function of frequency. Third, a transmission line (TL) formalism approach is adopted to examine the optimal frequency band for designing a cardiac implant antenna and the matching medium for the air-skin interface. Finally, experimental validation is performed at two ISM frequencies, 433 MHz and 915 MHz, selected from the optimal frequency band (0.4-1.5 GHz) suggested by our analytical investigation. For experimental validation, two off-the-shelf flexible dipole antennas operating at selected ISM frequencies were used. The numerical and experimental findings suggested that for the specific application of a cardiac implant with a penetration depth of 7-17 cm, the most effective frequency range for operation is within 0.4-1.5 GHz. The findings based on the dielectric properties of thorax tissues, the penetration depth of EM waves, and the optimal frequency band have provided valuable information on developing and optimizing CWIMDs for cardiac care applications.

Statistical techniques used in analysing simultaneous continuous glucose monitoring and ambulatory electrocardiography in patients with diabetes: A systematic review.

OBJECTIVES: There has been a steady increase in the number of studies of the complex relationship between glucose and electrical cardiac activity which use simultaneous continuous glucose monitors (CGM) and continuous electrocardiogram (ECG). However, data collected on the same individual tend to be similar (yielding correlated or dependent data) and require analyses that take into account that correlation. Many opt for simplified techniques such as calculating one measure from the data collected and analyse one observation per subject. These simplified methods may yield inconsistent and biased results in some instances. In this systematic review, we aim to examine the adequacy of the statistical analyses performed in such studies and make recommendations for future studies. RESEARCH QUESTIONS: What are the objectives of studies collecting simultaneous CGM and ECG data? Do methods used in analysing CGM and continuous ECG data fully optimise the data collected? DESIGN: Systematic review. DATA SOURCES: PubMed and Web of Science. METHODS: A comprehensive search of the PubMed and Web of Science databases to June 2022 was performed. Studies utilising CGM and continuous ECG simultaneously in people with diabetes were included. We extracted information about study objectives, technologies used to collect data and statistical analysis methods used for analysis. Reporting was done following PRISMA guidelines. RESULTS: Out of 118 publications screened, a total of 31 studies met the inclusion criteria. There was a diverse array of study objectives, with only two studies exploring the same exposure-outcome relationship, allowing only qualitative analysis. Only seven studies (23%) incorporated methods which fully utilised the study data using methods that yield the correct power and minimize type I error rate. The rest (77%) used analyses that summarise the data first before analysis and/or totally ignored data dependency. Of those who applied more advanced methods, one study performed both simple and correct analyses and found that ignoring data structure resulted in no association whilst controlling for repeated measures yielded a significant relationship. CONCLUSION: Most studies underutilised statistical methods suitable for analysis of dynamic continuous data, potentially attenuating their statistical power and overall conclusions. We recommend that aggregated data be used only as exploratory analysis, while primary analysis should use methods applied to the raw data such as mixed models or functional data analyses. These methods are widely available in many free, open source software applications.

Planar Elliptical Inductor Design for Wireless Implantable Medical Devices.

Wireless implantable medical devices (WIMDs) have seen unprecedented progress in the past three decades. WIMDs help clinicians in better-understanding diseases and enhance medical treatment by allowing for remote data collection and delivering tailored patient care. The wireless connectivity range between the external reader and the implanted device is considered one of the key design parameters in WIMD technology. One of the common modes of communication in battery-free WIMDs is inductive coupling, where the power and data between the reader and the implanted device are transmitted via magnetically coupled inductors. The design and shape of these inductors depend on the requirements of the application. Several studies have reported models of standard planar inductors such as circular, square, hexagonal, and octagonal in medical applications. However, for applications, constrained by narrow implantable locations, elliptical planar inductors may perform better than standard-shaped planar inductors. The aim of this study is to develop a numerical model for elliptical inductors. This model allows for the calculation of the inductance of the elliptical planar inductor and its parasitic components, which are key design parameters for the development of WIMDs powered by inductive coupling. An area transformation technique is used to transform and derive elliptical inductor formulas from standard circular inductor formulas. The proposed model is validated for various combinations of the number of turns, trace width, trace separation, and different inner and outer diameters of the elliptical planar inductor. For a thorough experimental validation of the proposed numerical model, more than 75 elliptical planar inductors were fabricated, measured, and compared with the numerical output of the proposed model. The mean error between the measured inductor parameters and numerical estimates using the proposed model is <5%, with a standard deviation of <3.18%. The proposed model provides an accurate analytical method for estimating and optimizing elliptical planar inductor parameters using a combination of current sheet expression and area transformation techniques. An elliptical planar inductor integrated with a sensing element can be used as a wireless implant to monitor the physiological signal from narrow implantation sites.

Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset.

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the "Stress-Predict Dataset", created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.

An Ex Vivo Study of Wireless Linkage Distance between Implantable LC Resonance Sensor and External Readout Coil.

The wireless monitoring of key physiological parameters such as heart rate, respiratory rate, temperature, and pressure can aid in preventive healthcare, early diagnosis, and patient-tailored treatment. In wireless implantable sensors, the distance between the sensor and the reader device is prone to be influenced by the operating frequency, as well as by the medium between the sensor and the reader. This manuscript presents an ex vivo investigation of the wireless linkage between an implantable sensor and an external reader for medical applications. The sensor was designed and fabricated using a cost-effective and accessible fabrication process. The sensor is composed of a circular planar inductor (L) and a circular planar capacitor (C) to form an inductor-capacitor (LC) resonance tank circuit. The reader system comprises a readout coil and data acquisition instrumentation. To investigate the effect of biological medium on wireless linkage, the readout distance between the sensor and the readout coil was examined independently for porcine and ovine tissues. In the bench model, to mimic the bio-environment for the investigation, skin, muscle, and fat tissues were used. The relative magnitude of the reflection coefficient (S11) at the readout coil was used as a metric to benchmark wireless linkage. A readable linkage signal was observed on the readout coil when the sensor was held up to 2.5 cm under layers of skin, muscle, and fat tissue. To increase the remote readout distance of the LC sensor, the effect of the repeater coil was also investigated. The experimental results showed that the magnitude of the reflection coefficient signal was increased 3-3.5 times in the presence of the repeater coil, thereby increasing the signal-to-noise ratio of the detected signal. Therefore, the repeater coil between the sensor and the readout coil allows a larger sensing range for a variety of applications in implanted or sealed fields.

Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications.

Purpose: Respiratory rate can provide auxiliary information on the physiological changes within the human body, such as physical and emotional stress. In a clinical setup, the abnormal respiratory rate can be indicative of the deterioration of the patient's condition. Most of the existing algorithms for the estimation of respiratory rate using photoplethysmography (PPG) are sensitive to external noise and may require the selection of certain algorithm-specific parameters, through the trial-and-error method. Methods: This paper proposes a new algorithm to estimate the respiratory rate using a photoplethysmography sensor signal for health monitoring. The algorithm is resistant to signal loss and can handle low-quality signals from the sensor. It combines selective windowing, preprocessing and signal conditioning, modified Welch filtering and postprocessing to achieve high accuracy and robustness to noise. Results: The Mean Absolute Error and the Root Mean Square Error of the proposed algorithm, with the optimal signal window size, are determined to be 2.05 breaths count per minute and 2.47 breaths count per minute, respectively, when tested on a publicly available dataset. These results present a significant improvement in accuracy over previously reported methods. The proposed algorithm achieved comparable results to the existing algorithms in the literature on the BIDMC dataset (containing data of 53 subjects, each recorded for 8 min) for other signal window sizes. Conclusion: The results endorse that integration of the proposed algorithm to a commercially available pulse oximetry device would expand its functionality from the measurement of oxygen saturation level and heart rate to the continuous measurement of the respiratory rate with good efficiency at home and in a clinical setting. Supplementary Information: The online version contains supplementary material available at 10.1007/s40846-022-00700-z.

Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection.

Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. These systems rely on the collection of sensor and reference data during the development phase. One of the most challenging tasks in physiological or pathological stress monitoring is the labeling of the physiological signals collected during an experiment. Commonly, different types of self-reporting questionnaires are used to label the perceived stress instances. These questionnaires only capture stress levels at a specific point in time. Moreover, self-reporting is subjective and prone to inaccuracies. This paper explores the potential feasibility of unsupervised learning clustering classifiers such as Affinity Propagation, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), K-mean, Mini-Batch K-mean, Mean Shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) for implementation in stress monitoring wearable devices. Traditional supervised machine learning (linear, ensembles, trees, and neighboring models) classifiers require hand-crafted features and labels while on the other hand, the unsupervised classifier does not require any labels of perceived stress levels and performs classification based on clustering algorithms. The classification results of unsupervised machine learning classifiers are found comparable to supervised machine learning classifiers on two publicly available datasets. The analysis and results of this comparative study demonstrate the potential of unsupervised learning for the development of non-invasive, continuous, and robust detection and monitoring of physiological and pathological stress.

Bench test and in vivo evaluation of longitudinal stent deformation during proximal optimisation.

BACKGROUND: While radial stent deformation has been thoroughly investigated, data on longitudinal deformation are scarce. AIMS: The aim of the study was to describe longitudinal stent deformation associated with the proximal optimisation technique (POT). METHODS: Longitudinal stent deformation was assessed by bench testing and by clinical evaluation. Bench testing was performed in silicone models using 3.00 (n=15) and 3.50 mm (n=14) stent platforms. After deployment, stents were sequentially post-dilated in the proximal main branch up to 5.50 mm, in increments of 0.50 mm, in order to simulate a spectrum of overexpansion. Stent length was redefined by optical coherence tomography (OCT) after each step. Clinical data were collected retrospectively from OCT-guided bifurcation percutaneous coronary intervention cases. RESULTS: In bench tests, POT has led to significant stent elongation in all cases. The magnitude of elongation was comparable between the 3.00 and the 3.50 mm stent platforms, with 0.86±0.74 mm vs 0.86±0.73 mm, respectively (p=0.71), per 0.5 mm overexpansion. For 3.00 mm stent platforms, maximal elongation was 4.31±1.47 mm after up to 5.5 mm overexpansion. For 3.50 mm platforms, maximal elongation was 2.87±0.94 mm after up to 5.5 mm overexpansion. Thirty-six clinical cases were analysed, of which 22 (61%) were performed in the distal left main. Post-dilation was performed with 0.98±0.36 mm absolute overexpansion, resulting in 2.22±1.35 mm elongation, as compared to nominal stent length. CONCLUSIONS: Overexpansion by POT results in proximal stent elongation. This has to be considered once the stent length is selected and the stent is positioned, especially in the left main stem, where proximal overexpansion is marked and accurate ostial landing is critical.

Angiography-Based 4-Dimensional Superficial Wall Strain and Stress: A New Diagnostic Tool in the Catheterization Laboratory.

A novel method for four-dimensional superficial wall strain and stress (4D-SWS) is derived from the arterial motion as pictured by invasive coronary angiography. Compared with the conventional finite element analysis of cardiovascular biomechanics using the estimated pulsatile pressure, the 4D-SWS approach can calculate the dynamic mechanical state of the superficial wall in vivo, which could be directly linked with plaque rupture or stent fracture. The validation of this approach using in silico models showed that the distribution and maximum values of superficial wall stress were similar to those calculated by conventional finite element analysis. The in vivo deformation was validated on 16 coronary arteries, from the comparison of centerlines predicted by the 4D-SWS approach against the actual centerlines reconstructed from angiograms at a randomly selected time-point, which demonstrated a good agreement of the centerline morphology between both approaches (scaling: 0.995 ± 0.018 and dissimilarity: 0.007 ± 0.014). The in silico vessel models with softer plaque and larger plaque burden presented more variation in mean lumen diameter and resulted in higher superficial wall stress. In more than half of the patients (n = 16), the maximum superficial wall stress was found at the proximal lesion shoulder. Additionally, in three patients who later suffered from acute coronary syndrome, the culprit plaque rupture sites co-localized with the site of highest superficial wall stress on their baseline angiography. These representative cases suggest that angiography-based superficial wall dynamics have the potential to identify coronary segments at high-risk of plaque rupture and fracture sites of implanted stents. Ongoing studies are focusing on identifying weak spots in coronary bypass grafts, and on exploring the biomechanical mechanisms of coronary arterial remodeling and aneurysm formation. Future developments involve integration of fast computational techniques to allow online availability of superficial wall strain and stress in the catheterization laboratory.

A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare.

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies.

A feasibility study on microwave imaging of bone for osteoporosis monitoring.

The dielectric properties of bones are found to be influenced by the demineralisation of bones. Therefore, microwave imaging (MWI) can be used to monitor in vivo dielectric properties of human bones and hence aid in the monitoring of osteoporosis. This paper presents the feasibility analysis of the MWI device for monitoring osteoporosis. Firstly, the dielectric properties of tissues present in the human heel are analysed. Secondly, a transmission line (TL) formalism approach is adopted to examine the feasible frequency band and the matching medium for MWI of trabecular bone. Finally, simplified numerical modelling of the human heel was set to monitor the penetration of E-field, the received signal strength, and the power loss in a numerical model of the human heel. Based on the TL formalism approach, 0.6-1.9-GHz frequency band is found to feasible for bone imaging purpose. The relative permittivity of the matching medium can be chosen between 15 and 40. The average percentage difference between the received signal for feasible and inconvenient frequency band was found to be 82%. The findings based on the dielectric contrast of tissues in the heel, the feasible frequency band, and the finite difference time domain simulations support the development of an MWI prototype for monitoring osteoporosis.

Development of a New Detection Algorithm to Identify Acute Coronary Syndrome Using Electrochemical Biosensors for Real-World Long-Term Monitoring.

Electrochemically based technologies are rapidly moving from the laboratory to bedside applications and wearable devices, like in the field of cardiovascular disease. Major efforts have focused on the biosensor component in contrast with those employed in creating more suitable detection algorithms for long-term real-world monitoring solutions. The calibration curve procedure presents major limitations in this context. The objective is to propose a new algorithm, compliant with current clinical guidelines, which can overcome these limitations and contribute to the development of trustworthy wearable or telemonitoring solutions for home-based care. A total of 123 samples of phosphate buffer solution were spiked with different concentrations of troponin, the gold standard method for the diagnosis of the acute coronary syndrome. These were classified as normal or abnormal according to established clinical cut-off values. Off-the-shelf screen-printed electrochemical sensors and cyclic voltammetry measurements (sweep between -1 and 1 V in a 5 mV step) was performed to characterize the changes on the surface of the biosensor and to measure the concentration of troponin in each sample. A logistic regression model was developed to accurately classify these samples as normal or abnormal. The model presents high predictive performance according to specificity (94%), sensitivity (92%), precision (92%), recall (92%), negative predictive value (94%) and F-score (92%). The area under the curve of the precision-recall curve is 97% and the positive and negative likelihood ratios are 16.38 and 0.082, respectively. Moreover, high discriminative power is observed from the discriminate odd ratio (201) and the Youden index (0.866) values. The promising performance of the proposed algorithm suggests its capability to overcome the limitations of the calibration curve procedure and therefore its suitability for the development of trustworthy home-based care solutions.

Microwave Bone Imaging: A Preliminary Investigation on Numerical Bone Phantoms for Bone Health Monitoring.

Microwave tomography (MWT) can be used as an alternative modality for monitoring human bone health. Studies have found a significant dielectric contrast between healthy and diseased human trabecular bones. A set of diverse bone phantoms were developed based on single-pole Debye parameters of osteoporotic and osteoarthritis human trabecular bones. The bone phantoms were designed as a two-layered circular structure, where the outer layer mimics the dielectric properties of the cortical bone and the inner layer mimics the dielectric properties of the trabecular bone. The electromagnetic (EM) inverse scattering problem was solved using a distorted Born iterative method (DBIM). A compressed sensing-based linear inversion approach referred to as iterative method with adaptive thresholding for compressed sensing (IMATCS) has been employed for solving the underdetermined set of linear equations at each DBIM iteration. To overcome the challenges posed by the ill-posedness of the EM inverse scattering problem, the L2-based regularization approach was adopted in the amalgamation of the IMATCS approach. The simulation results showed that osteoporotic and osteoarthritis bones can be differentiated based on the reconstructed dielectric properties even for low values of the signal-to-noise ratio. These results show that the adopted approach can be used to monitor bone health based on the reconstructed dielectric properties.

Thin-Film Flexible Wireless Pressure Sensor for Continuous Pressure Monitoring in Medical Applications.

Physiological pressure measurement is one of the most common applications of sensors in healthcare. Particularly, continuous pressure monitoring provides key information for early diagnosis, patient-specific treatment, and preventive healthcare. This paper presents a thin-film flexible wireless pressure sensor for continuous pressure measurement in a wide range of medical applications but mainly focused on interface pressure monitoring during compression therapy to treat venous insufficiency. The sensor is based on a pressure-dependent capacitor (C) and printed inductive coil (L) that form an inductor-capacitor (LC) resonant circuit. A matched reader coil provides an excellent coupling at the fundamental resonance frequency of the sensor. Considering varying requirements of venous ulceration, two versions of the sensor, with different sizes, were finalized after design parameter optimization and fabricated using a cost-effective and simple etching method. A test setup consisting of a glass pressure chamber and a vacuum pump was developed to test and characterize the response of the sensors. Both sensors were tested for a narrow range (0-100 mmHg) and a wide range (0-300 mmHg) to cover most of the physiological pressure measurement applications. Both sensors showed good linearity with high sensitivity in the lower pressure range <100 mmHg, providing a wireless monitoring platform for compression therapy in venous ulceration.

Benefits of Home-Based Solutions for Diagnosis and Treatment of Acute Coronary Syndromes on Health Care Costs: A Systematic Review.

Diagnosing and treating acute coronary syndromes consumes a significant fraction of the healthcare budget worldwide. The pressure on resources is expected to increase with the continuing rise of cardiovascular disease, other chronic diseases and extended life expectancy, while expenditure is constrained. The objective of this review is to assess if home-based solutions for measuring chemical cardiac biomarkers can mitigate or reduce the continued rise in the costs of ACS treatment. A systematic review was performed considering published literature in several relevant public databases (i.e., PUBMED, Cochrane, Embase and Scopus) focusing on current biomarker practices in high-risk patients, their cost-effectiveness and the clinical evidence and feasibility of implementation. Out of 26,000 references screened, 86 met the inclusion criteria after independent full-text review. Current clinical evidence highlights that home-based solutions implemented in primary and secondary prevention reduce health care costs by earlier diagnosis, improved patient outcomes and quality of life, as well as by avoidance of unnecessary use of resources. Economical evidence suggests their potential to reduce health care costs if the incremental cost-effectiveness ratio or the willingness-to-pay does not surpass £20,000/QALY or €50,000 limit per 20,000 patients, respectively. The cost-effectiveness of these solutions increases when applied to high-risk patients.

Dielectric characterization of diseased human trabecular bones at microwave frequency.

The objective of this study is to determine whether in vitro dielectric properties of human trabecular bones, can distinguish between osteoporotic and osteoarthritis patients' bone samples. Specifically this study enlightens intra-patient variation of trabecular bone microarchitecture and dielectric properties, inter-disease comparison of bone dielectric properties, and finally establishes the correlation to traditional bone histomorphometry parameter (bone volume fraction) for diseased bone tissue. Bone cores were obtained from osteoporotic and osteoarthritis patients (n = 12). These were scanned using microCT to examine bone volume fraction. An open-ended coaxial probe measurement technique was employed to measure dielectric properties over the 0.5 - 8.5 GHz frequency range. The dielectric properties of osteoarthritis patients are significantly higher than osteoporotic patients; with an increase of 41% and 45% for relative permittivity and conductivity respectively. The dielectric properties within each patient vary significantly, variation in relative permittivity and conductivity was found to be greater than 25% and 1.4% respectively. A weak correlation (r  = â€…0.5) is observed between relative permittivity and bone volume fraction. Osteoporotic and osteoarthritis bones can be differentiated based on difference of dielectric properties. Although these do not correlate strongly to bone volume fraction, it should be noted that bone volume fraction is a poor predictor of fracture risk. The dielectric properties of bones are found to be influenced by mineralization levels of bones. Therefore, dielectric properties of bones may have potential as a diagnostic measure of osteoporosis.