Showing papers in "Journal of Medical Engineering & Technology in 2019"
TL;DR: The HRV artefact correction algorithm presented in this study provided comparable specificity and better sensitivity to detect ectopic beats as compared to state-of-the-art algorithms.
Abstract: Purpose: Heart rate variability is a commonly used measurement to evaluate functioning of autonomic nervous system, psychophysiological stress, and exercise intensity and recovery. HRV measurements contain artefacts such as extra, missed or misaligned beat detections, which can produce significant distortion on HRV parameters. In this paper, a robust automatic method for artefact detection from HRV time series is proposed. Methods: The proposed detection method is based on time-varying thresholds estimated from distribution of successive RR-interval differences combined with a novel beat classification scheme. The method is validated using simulated extra, missed and misaligned beat detections as well as real artefacts such as atrial and ventricular ectopic beats. Results: The sensitivity of the algorithm to detect simulated missed/extra beats was 100%. The sensitivity to detect real atrial and ventricular ectopic beats was 96.96%, the corresponding specificity being 99.94%. The mean error in HRV parameters after correction was <2% for missed and extra beats as well as for misaligned beats generated with large displacement factors. Misaligned beats with smallest displacement factor were the most difficult to detect and resulted in largest HRV parameter errors after correction, largest errors being <8%. Conclusions: The HRV artefact correction algorithm presented in this study provided comparable specificity and better sensitivity to detect ectopic beats as compared to state-of-the-art algorithms. The proposed algorithm detects abnormal beats with high accuracy, is relatively easy to implement, and secures reliable HRV analysis by reducing the effect of possible artefacts to tolerable level.
111 citations
TL;DR: The sEMG methodology followed by researchers assessed the issues of noise control, wave frequency, cross talk, low signal reception, muscle co-contraction, electrode placement protocol and procedure as well as EMG signal timing, intensity and normalisation so as to collect accurate, adequate and meaningful data.
Abstract: Gait analysis is a significant diagnostic procedure for the clinicians who manage musculoskeletal disorders. Surface electromyography (sEMG) combined with kinematic and kinetic data is a useful tool for decision making of the appropriate method needed to treat such patients. sEMG has been used for decades to evaluate neuromuscular responses during a range of activities and develop rehabilitation protocols. The sEMG methodology followed by researchers assessed the issues of noise control, wave frequency, cross talk, low signal reception, muscle co-contraction, electrode placement protocol and procedure as well as EMG signal timing, intensity and normalisation so as to collect accurate, adequate and meaningful data. Further research should be done to provide more information related to the muscle activity recorded by sEMG and the force produced by the corresponding muscle during gait analysis.
44 citations
TL;DR: A new method for heart sound features extraction for the classification of non-segmented signals using instantaneous frequency was proposed and the overall accuracy, sensitivity, specificity, and precision were all above 95% using both random forest and KNN classifiers.
Abstract: Heart sound and its recorded signal which is known as phonocardiograph (PCG) are one of the most important biosignals that can be used to diagnose cardiac diseases alongside electrocardiogram (ECG). Over the past few years, the use of PCG signals has become more widespread and researchers pay their attention to it and aim to provide an automated heart sound analysis and classification system that supports medical professionals in their decision. In this paper, a new method for heart sound features extraction for the classification of non-segmented signals using instantaneous frequency was proposed. The method has two major phases: the first phase is to estimate the instantaneous frequency of the recorded signal; the second phase is to extract a set of eleven features from the estimated instantaneous frequency. The method was tested into two different datasets, one for binary classification (Normal and Abnormal) and the other for multi-classification (Five Classes) to ensure the robustness of the extracted features. The overall accuracy, sensitivity, specificity, and precision for binary classification and multi-classification were all above 95% using both random forest and KNN classifiers.
37 citations
TL;DR: In this review, the properties of nanofibrous wound dresses are studied and possible approaches to load biological components into them for wound healing improvement are studied.
Abstract: The diversity of wound types has gathered momentum to develop a wide range of wound dressings to improve different aspects of the wound healing process. Wound healing is a dynamic, complex and highly regulated mechanism of tissue repair and regeneration. The wound dressing should encourage regeneration and prevent possible infection or scaring. Wound dressings are different from the bandages as they come in direct contact with the wound and are used to absorb exudates and accelerate healing. Wound dressings can have a variety of functions, depending upon the type, severity, and position of the wound. In this review, we have studied the properties of nanofibrous wound dresses and possible approaches to load biological components into them for wound healing improvement.
30 citations
TL;DR: This work aims to review the last breakthroughs about the three above-mentioned techniques describing the benefits of mixing several computational skills to obtain a better global performance, and provides a comparison between several machine learning techniques applied to breast cancer diagnosis.
Abstract: Breast cancer is a disease that threat many women's life, thus, the early and accurate detection play a key role in reducing the mortality rate. Mammography stands as the reference technique for breast cancer screening; nevertheless, many countries still lack access to mammograms due to economic, social and cultural issues. Last advances in computational tools, infra-red cameras and devices for bio-impedance quantification allowed the development of parallel techniques like, thermography, infra-red imaging and electrical impedance tomography, these being faster, reliable and cheaper. In the last decades, these have been considered as complement procedures for breast cancer diagnosis, where many studies concluded that false positive and false negative rates are greatly reduced. This work aims to review the last breakthroughs about the three above-mentioned techniques describing the benefits of mixing several computational skills to obtain a better global performance. In addition, we provide a comparison between several machine learning techniques applied to breast cancer diagnosis going from logistic regression, decision trees and random forest to artificial, deep and convolutional neural networks. Finally, it is mentioned several recommendations for 3D breast simulations, pre-processing techniques, biomedical devices in the research field, prediction of tumour location and size.
28 citations
TL;DR: This paper proposes a routing protocol for wireless body area networks, to transfer data in the network with minimum energy consumption, and longer network lifetime through multi-hop communication, and results show that the routing protocol ensures a robust optimisation of the energy consumption which helps to increase the lifetime of the network and its stability.
Abstract: A Wireless Sensor Networks (WSNs) consists basically of a group of nodes, that communicate with each other through a wireless transmission, and does not need any existing infrastructure. The recent...
18 citations
TL;DR: The enhanced output parameters of the sensor were responsible for smooth, faster and intuitive actuation of the prosthetic hand fingers.
Abstract: This paper proposes a low-cost and sensitive surface electromyography (sEMG) sensor for the myoelectric prosthesis. The sensor consists of a skin interface, signal conditioning circuitry and power supply unit all encased in a single package. The tuned RC parameters based envelope detection scheme employed in the sensor enables faster as well as reliable recognition of EMG signal patterns regardless of its strength and subject variability. The output performance of the developed sensor was compared with a commercial EMG sensor regarding signal-to-noise ratio (SNR), amplitude sensitivity and response time. EMG signals with both the devices were acquired for 10 subjects (three amputees and seven healthy subjects), to perform this comparative analysis. The results showed 4× greater SNR values and 50% higher sensitivity of the developed sensor than the commercial EMG sensor. Also, the proposed sensor was 57% faster than the commercial sensor in producing the output response. The sensor was successfully tested on amputees for controlling a 3D printed hand prototype utilising a proportional control strategy. The enhanced output parameters of the sensor were responsible for smooth, faster and intuitive actuation of the prosthetic hand fingers.
18 citations
TL;DR: This research objective is to synthesise a PDP model for SMEs in the specific medical sector, by incorporating the best practices of the engineering area and particularities of the medical area, using an extensive bibliographic analysis and field research conducted towards SME's in the MD industry.
Abstract: The development process of medical devices (MDs) implies the integration of knowledge and skills from the fields of medicine and engineering. Such an integration is difficult because of lack of com...
15 citations
TL;DR: An interdisciplinary team blending expertise from engineering, medicine, and nursing is believed to be essential in translating wearable technology into the field, necessitating accurate, reliable, and timely transmission of acquired bio-metric and bio-vital data.
Abstract: The digital health field has seen a surge in product development over the last decade, with product introductions ranging from wrist monitors, epidermal electronics, electronic pills and smart garments, much of these precipitated through the commercialisation and commoditisation of sensor technology. The emergence of wearable technology has recently garnered heightened interest by physicians and the general public. The convenient use of wireless technology to track and monitor physiological parameters, such as heart rate, distance, sleep and stress, has emerged to become relevant to patient care and human performance assessment. However, collecting data is not enough to inform clinical decision-making. It is essential to translate the acquired data into information relevant to clinicians. Our experiences tell us that team competencies must mirror the interdisciplinary technology itself. Thus, an interdisciplinary team blending expertise from engineering, medicine, and nursing is believed to be essential in translating wearable technology into the field. This review discusses the application of wearable sensors to monitor human performance assessment in domains necessitating accurate, reliable, and timely transmission of acquired bio-metric and bio-vital data. A key result disseminating from our investigations is the need to develop predictive models based off of the data acquired from wearable devices to necessitate the development of athlete-centred treatment plans to expedite the return-to-play time and to maximise performance.
13 citations
TL;DR: The future trends and strategies such as combining different methods, e.g. spectral cameras and bioimpedance spectroscopy that could represent a unique multimodality in vivo tool for providing complementary information on the health status of the oral mucosa are discussed.
Abstract: Oral mucosal diseases are common health problems that reduce overall wellbeing and increase the risk for several systemic diseases. Due to the limitations of present diagnostics, new non-invasive methods are needed for reliable, affordable, real-time screening and follow-up of oral mucosal lesions. Bioimpedance spectroscopy, spectral camera imaging and other optical methods are promising novel techniques to detect abnormal changes in oral mucosa. In this review, the current status of bioimpedance spectroscopy and autofluorescence utilising spectral camera techniques in the assessment of oral mucosal health is critically evaluated. Scientific publications related to bioimpedance spectroscopy were surveyed using PubMed and Scopus databases. Search was done using a combination of terms "oral mucosa", "oral cancer", "squamous cell cancer", "tissue", "electrical impedance measurement" and "bioimpedance spectroscopy". Publications related to spectral cameras were searched from PubMed with a focus on autofluorescence utilising spectral camera techniques. Search was done using terms "autofluorescence", "oral disease" and "VELscope" publication date restricted from 2008 to date. In this review, we also discuss the future trends and strategies such as combining different methods, e.g. spectral cameras and bioimpedance spectroscopy that could represent a unique multimodality in vivo tool for providing complementary information on the health status of the oral mucosa.
12 citations
TL;DR: This work aimed to achieve accurate detection and reading of the seven-segment digits displayed on these medical devices using an image taken in a realistic scenario by a smartphone camera.
Abstract: There is an increasing need for fast and accurate transfer of readings from blood glucose metres and blood pressure monitors to a smartphone mHealth application, without a dependency on Bluetooth technology. Most of the medical devices recommended for home monitoring use a seven-segment display to show the recorded measurement to the patient. We aimed to achieve accurate detection and reading of the seven-segment digits displayed on these medical devices using an image taken in a realistic scenario by a smartphone camera. A synthetic dataset of seven-segment digits was developed in order to train and test a digit classifier. A dataset containing realistic images of blood glucose metres and blood pressure monitors using a variety of smartphone cameras was also created. The digit classifier was evaluated on a dataset of seven-segment digits manually extracted from the medical device images. These datasets along with the code for its development have been made public. The developed algorithm first preprocessed the input image using retinex with two bilateral filters and adaptive histogram equalisation. Subsequently, the digit segments were automatically located within the image by two techniques operating in parallel: Maximally Stable Extremal Regions (MSER) and connected components of a binarised image. A filtering and clustering algorithm was then designed to combine digit segments to form seven-segment digits. The resulting digits were classified using a Histogram of Orientated Gradients (HOG) feature set and a neural network trained on the synthetic digits. The model achieved 93% accuracy on digits found on the medical devices. The digit location algorithm achieved a F1 score of 0.87 and 0.80 on images of blood glucose metres and blood pressure monitors respectively. Very few assumptions were made of the locations of the digits on the devices so that the proposed algorithm can be easily implemented on new devices.
TL;DR: The ANN models were able to accurately classify the gait type of each stride using a single wearable IMU, and the accuracy of the method should improve further as more data is added to the ANN training.
Abstract: With the rising popularity of activity tracking, there is a desire to not only count the number of steps a person takes, but also identify the type of step (e.g., walking or running) they are takin...
TL;DR: The stimulation could produce a better movement even in cases where the muscles were clinically completely paretic, sometimes also in palsies that were several years old, provided that the muscle was not totally denervated.
Abstract: Reanimation of paralysed facial muscles by electrical stimulation has been studied extensively in animal models, but human studies in this field are largely lacking. Twenty-four subjects with a peripheral facial nerve palsy with a median duration of three years were enrolled. We studied activations of four facial muscles with electrical stimulation using surface electrodes. In subjects whose voluntary movement was severely impaired or completely absent, the electrical stimulation produced a movement that was greater in amplitude compared with the voluntary effort in 10 out of 18 subjects in the frontalis muscle, in 5 out of 14 subjects in the zygomaticus major muscle, and in 3 out of 8 subjects in the orbicularis oris muscle. The electrical stimulation produced a stronger blink in 8 subjects out of 22 compared with their spontaneous blinks. The stimulation could produce a better movement even in cases where the muscles were clinically completely paretic, sometimes also in palsies that were several years old, provided that the muscle was not totally denervated. Restoring the function of paralysed facial muscles by electrical stimulation has potential as a therapeutic option in cases where the muscle is clinically paretic but has reinnervation.
TL;DR: Although the absolute measures in the hip and joint angles obtained using MLS differ from MBS, the MLS may be useful for accurately classifying the movement strategy adopted in the FRT.
Abstract: The purpose of this study was to examine the accuracy of classifying the movement strategy in the functional reach test (FRT) using a markerless motion capture system (MLS) on the basis of values a...
TL;DR: This study provides an overview of various methods for obtaining energy from the human body, summarised, compared and analysed and the best results achieved are compared and listed.
Abstract: The purpose of this study is to review available methods of utilising the human body to obtain energy during the course of daily life activities, without interference with an individual's lifestyle. The number of individuals with health issues requiring assistance from external or internal health-aiding devices is rapidly increasing. Battery life associated with these devices is currently a major limitation. Currently, medical devices that depend on batteries (i.e., implantable devices) require constant battery monitoring. Development of implantable devices with rechargeable batteries is, therefore, essential. Technologies that can capture energy from the human body can be developed, with different organs, systems, and activities having the potential to be utilised to generate energy. This energy source can act as an alternative to conventional batteries. This study provides an overview of various methods for obtaining energy from the human body. These methods are summarised, compared and analysed. The best results achieved (in terms of power output) are compared and listed.
TL;DR: Various methods of analysing automatic retinopathy detection and classification of different grading based on the severity levels are discussed, which will be helpful for the technical persons and researchers who want to focus on enhancing the diagnosis of a system that would be more powerful in real life.
Abstract: Diabetic retinopathy is a serious microvascular disorder that might result in loss of vision and blindness. It seriously damages the retinal blood vessels and reduces the light-sensitive inner layer of the eye. Due to the manual inspection of retinal fundus images on diabetic retinopathy to detect the morphological abnormalities in Microaneurysms (MAs), Exudates (EXs), Haemorrhages (HMs), and Inter retinal microvascular abnormalities (IRMA) is very difficult and time consuming process. In order to avoid this, the regular follow-up screening process, and early automatic Diabetic Retinopathy detection are necessary. This paper discusses various methods of analysing automatic retinopathy detection and classification of different grading based on the severity levels. In addition, retinal blood vessel detection techniques are also discussed for the ultimate detection and diagnostic procedure of proliferative diabetic retinopathy. Furthermore, the paper elaborately discussed the systematic review accessed by authors on various publicly available databases collected from different medical sources. In the survey, meta-analysis of several methods for diabetic feature extraction, segmentation and various types of classifiers have been used to evaluate the system performance metrics for the diagnosis of DR. This survey will be helpful for the technical persons and researchers who want to focus on enhancing the diagnosis of a system that would be more powerful in real life.
TL;DR: Using the electric model of the cardiac cell and the mechanism of producing an ECG signal in the heart, the process of producing cardiac electrical potential has been modelled and a sufficient match between the model output and the reported changes of the heart arrhythmia including ischaemic failures is demonstrated.
Abstract: One of the most common causes of heart failure is ischaemia. In this disease, the heart muscles die due to the lack or insufficiency of the blood in the cardiac veins. As a result of such a phenome...
TL;DR: A computerised diagnostic method based on morphological characteristics and a supervised classification method to categorise subjects into two groups: normal and affected cases is proposed and shown that the use of a support vector machine (SVM) classifier is an effective way to enhance recognition and detection for rapid and accurate foetal head diagnostic.
Abstract: This paper presents an advanced approach for foetal brain abnormalities diagnostic by integrating significant biometric features in the identification process. In foetal anomaly diagnosis, manual evaluation of foetal behaviour in ultrasound images is a subjective, slow and error-prone task, especially in the preliminary treatment phases. The effectiveness of this appearance is strictly subject to the attention and the experience of gynaecologists. In this case, automatic methods of image analysis offer the possibility of obtaining a homogeneous, objective and above all fast diagnosis of the foetal head in order to identify pregnancy behaviour. Indeed, we propose a computerised diagnostic method based on morphological characteristics and a supervised classification method to categorise subjects into two groups: normal and affected cases. The presented method is validated on a real integrated microcephaly and dolichocephaly cases. The studied database contains the same gestational age of both normal and abnormal foetuses. The results show that the use of a support vector machine (SVM) classifier is an effective way to enhance recognition and detection for rapid and accurate foetal head diagnostic.
TL;DR: It can be concluded that the level of accuracy presented in the new low-cost Kinect Perfect Phorm system is not yet suitable for clinical assessments, but for general tests of performance, and for tracking cases where absolute accuracy is less critical, future versions of this software may have a place.
Abstract: Human motion tracking is widely used for the assessment of movement dysfunction in orthopaedic patients. Currently, most clinical motion analysis centres use marker-based three-dimensional (3D) systems as they are deemed to be the most accurate method. However, due to space, costs and logistics they are not available in many clinical settings. This study compared joint angles measured in functional tests using the novel low-cost Microsoft Kinect Perfect Phorm system with the established marker-based Nexus VICON system. When measuring right and left knee flexion, the average difference between the VICON and Kinect Perfect Phorm measurement was 13.2%, with a SD of 19.6. Both overestimation and underestimation of the joint angle was recorded in different participants. Although the average percentage difference during hip abduction tests was lower at -3.9%, the range of error was far greater (SD = 75). From this, it can be concluded that the level of accuracy presented in the new low-cost Kinect Perfect Phorm system is not yet suitable for clinical assessments. However, for general tests of performance, and for tracking cases where absolute accuracy is less critical, future versions of this software may have a place.
TL;DR: This study presents the design and construction of an inexpensive ($49.6) medication dispenser suitable for point of care applications in low resource settings and intends to deploy this device for wider and independent usage by users in order to prevent unnecessary hospital admission meant to enforce compliance with appropriate medication usage for the users.
Abstract: Neurodegenerative illnesses due to diseases or old age are typical examples of clinical conditions that may affect the proper observation of prescribed medication usage with negative conseq...
TL;DR: A correction technique based on the two-point linear equation derived from the information gathered from different subjects’ skeletal data and data regression analysis to compensate the inaccuracies in joint-to-ground data collection proved to be consistent and more accurate within a distance range of 1.6–2.9 m.
Abstract: The Kinect sensor has been widely used in different applications such as rehabilitation and gait analysis. Whilst Kinect v2 was released with several improvements over its predecessor, it still inc...
TL;DR: The current MCTR in use for the testing of artificial cardiovascular organs are examined, including the necessity of in vitro study, the building of MCTRs, relevant standards, general system structure, and the considerations for the modelling of the physiological impedance of M CTRs are surveyed.
Abstract: In vitro study plays an important role in the experimental study of cardiovascular dynamics. An essential hardware facility that mimics the blood flow changes and provides the required test conditi...
TL;DR: A novel, cost-effective, portable, complex flow phantom is proposed and the design specifications are provided, which employs a piston/cylinder system for vortex ring generation, coupled to an imaging tank full of fluid, for vortex propagation.
Abstract: Cardiovascular fluid dynamics exhibit complex flow patterns, such as recirculation and vortices. Quantitative analysis of these complexities supports diagnosis, leading to early prediction ...
TL;DR: A hybrid algorithm including empirical mode decomposition (EMD), Hilbert transform and Gaussian function for detecting heart sounds to distinguish first (S1) and second (S2) cardiac sounds by eliminating the effect of cardiac murmurs is proposed.
Abstract: For many years, heart function has been measured by the electrocardiogram (ECG) signal, while sounds produced in the heart can also contain information indicating normal or abnormal heart function. What has caused to restrict the use of the phonocardiography (PCG) signal was the lack of mastery of experts in the interpretation of these sounds, as well as its high potential for noise pollution. PCG is a non-invasive signal for monitoring physiological parameters of cardiac, which can make heart disease diagnostics more efficient. In recent years, attempts have been made to use PCG to detect heart disease independently without a need to match with the ECG. We propose a hybrid algorithm including empirical mode decomposition (EMD), Hilbert transform and Gaussian function for detecting heart sounds to distinguish first (S1) and second (S2) cardiac sounds by eliminating the effect of cardiac murmurs. In this article, 250 normal and 250 abnormal sound signals were examined. The overall positive predictivity of normal and abnormal S1 and S2 is 98.98%, 98.78, 98.78 and 98.37, respectively. Our results showed that the proposed method has a high potential for heart sounds determination, while maintains its simplicity and has a reasonable computational time.
TL;DR: Thoracic bioimpedance may have a role as a non-invasive cardiac output trend monitor in healthy volunteer studies by comparing it against thoracic echocardiography in fifteen healthy volunteers undergoing two physical challenges designed to vary cardiac output.
Abstract: Thoracic electrical bioimpedance (TEB) and transthoracic echocardiography (TTE) are non-invasive methods to estimate stroke volume (SV) and cardiac output (CO). Thoracic electrical bioimpedance is not in widespread clinical use with reports of inaccurate cardiac output estimation compared to invasive monitors, particularly in non-healthy populations. We explore its use as a trend monitor by comparing it against thoracic echocardiography in fifteen healthy volunteers undergoing two physical challenges designed to vary cardiac output. Of all paired values, 54.6% showed gross trend agreement and only 1.9% showed direct disagreement between the two monitors. Our results show thoracic bioimpedance may have a role as a non-invasive cardiac output trend monitor in healthy volunteer studies.
TL;DR: The prototype drill-guide improved the accuracy, reduced the variability, and reduced procedure duration compared to thetraditional free-hand technique, and created a more accurate bone bridge than the traditional free hand method.
Abstract: For surgical reconstruction of the medial patello-femoral ligament (MPFL) a variety of techniques are used for fixation of the graft to the medial border of the patella. The bone bridge or V-shaped tunnel technique utilises two tunnels drilled from the medial aspect of the patella that converge centrally creating a tunnel through which the graft is threaded. This technique has advantages: it avoids hardware (bone anchors) and their associated complications, creates a broad attachment of the ligament approximating normal anatomy and the tunnel does not breach the lateral cortex of the patella reducing the risk of patella fracture. In current practice the bony tunnels are created using freehand techniques. These rely on estimation of the patella centre by the surgeon and is subject to wide variation. Additionally this technique can be inefficient, inaccurate and time consuming. To address these disadvantages a new drill-guide device was developed. A prototype drill-guide was constructed using CAD and 3D printing methods. The device was designed to allow the surgeon to accurately and efficiently drill the required v-shaped bone tunnel. To assess the efficacy of the prototype drill guide, an experiment designed to assess a group of ten surgeons with an average of 4.2 years experience performing the task of creating a v-shaped bone tunnel using a free-hand technique and the drill-guide. To determine the accuracy of the tunnel placement, the angle between drill holes, distance from centre of the patella and the amount of over-drill were measured. Procedure duration was also compared. The results revealed that the prototype drill-guide created a more accurate bone bridge than the traditional free hand method. The root mean square error for the distance from centre was 0.50 mm vs 2.12 mm and the angle between tunnels was 2.6O vs 15.9O for the prototype and traditional methods respectively. There was a mean of 8.9 mm over-drill with the traditional method, which was negated when using the guide. Surgeons using the guide were approximately 25% faster than using the traditional free-hand technique. The prototype drill-guide improved the accuracy, reduced the variability, and reduced procedure duration compared to the traditional free-hand technique.
TL;DR: Using the Hodgkin–Huxley neuron model, an analytical method for the calculation of the electromagnetic power absorption in the brain neurons is proposed based on the complex permittivity of neuron membranes, which reduces the reflectivity and transmissivity of the external stimulus at the membrane of the cell.
Abstract: The effect of the external electromagnetic radiation on the human brain has been investigated by the numerical calculation of the frequency-dependent complex permittivity. Using the Hodgkin-Huxley neuron model, an analytical method for the calculation of the electromagnetic power absorption in the brain neurons is proposed based on the complex permittivity of neuron membranes. The effect of the human head is considered through the calculation of forward and backward travelling electromagnetic waves. The modified time-dependent Hodgkin-Huxley equations have been used to obtain the reflectivity and transmissivity of the external stimulus at the membrane of the cell. The amount of absorbed energy by the human head is presented in the form of the electromagnetic absorption rate.
TL;DR: A non-contact cardiac monitoring system using a 24-GHz medical radar for directly measuring the beat-to-beat heart mechanical activity at a distance from a subject and monitoring dynamic changes in HRV indices induced by the head-up tilt test has significant potential for advancing personal healthcare in both clinical and out-of-hospital settings.
Abstract: Electrocardiography (ECG) is a mandatory standard for monitoring electrical activity of the heart in many clinical settings such as intensive care and emergency units. However, in situations wherein the skin is damaged, such as acute burn injuries, it is impossible to efficiently attach electrodes to the skin. In this study, we developed a non-contact cardiac monitoring system using a 24-GHz medical radar for directly measuring the beat-to-beat heart mechanical activity at a distance from a subject. The heart rate variability (HRV) was analysed using an autoregressive model (AR) from the measured beat-to-beat intervals during a head-up tilt test. To investigate the feasibility of the proposed system, we compared medical radar and ECG recording by using Lin's correlation coefficient and Bland-Altman analysis, which showed a negligible mean difference from the substantial agreement of Lin's correlation coefficient of 0.9 between the radar and ECG. The non-contact radar clearly monitored dynamic changes in HRV indices induced by the head-up tilt test. This type of non-contact HRV-sensing technique as an alternative approach has significant potential for advancing personal healthcare in both clinical and out-of-hospital settings.
TL;DR: Results show that balance perturbations representative of real-life situations, such as standing on public transport, can accurately and repeatedly be produced in a safe and controlled environment with a low-cost and low-maintenance system.
Abstract: Balance recovery mechanisms are of paramount importance in situations like public transport where sudden loss of equilibrium can occur. These mechanisms can be altered by aging or pathologi...
TL;DR: Use of a population mean rather than personalised baselines is probably sufficient for most clinical purposes as between-person variability is not large compared to within- person variability.
Abstract: © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Between-individual variability of body temperature has been little investigated, but is of clinical importance: for example, in detection of neutropenic sepsis during chemotherapy. We studied within-person and between-person variability in temperature in healthy adults and those receiving chemotherapy using a prospective observational design involving 29 healthy participants and 23 patients undergoing chemotherapy. Primary outcome was oral temperature. We calculated each patient’s mean temperature, standard deviation within each patient (within-person variability), and between patients (between-person variability). Secondary analysis explored temperature changes in the three days before admission for neutropenic sepsis. 1,755 temperature readings were returned by healthy participants and 1,765 by chemotherapy patients. Mean participant temperature was 36.16 C (95% CI 36.07–36.26) in healthy participants and 36.32 C (95% CI 36.18–36.46) in chemotherapy patients. Healthy participant within-person variability was 0.40 C (95% CI 0.36–0.44) and between-person variability was 0.26 C (95% CI 0.16–0.35). Chemotherapy patient within-person variability was 0.39 C (95% CI 0.34–0.44) and between-person variability was 0.34 C (95% CI 0.26–0.48). Thus, use of a population mean rather than personalised baselines is probably sufficient for most clinical purposes as between-person variability is not large compared to within-person variability. Standardised guidance and provision of thermometers to patients might help to improve recording and guide management.