Objective: We present a review of wireless medical devices that are placed inside the human body to realize many and different sensing and/or stimulating functionalities. Methods: A critical ...literature review analysis is conducted focusing on three types of in-body medical devices, i.e., 1) devices that are implanted inside the human body (implantables), 2) devices that are ingested like regular pills (ingestibles), and 3) devices that are injected into the human body via needles (injectables). Design considerations, current status, and future directions related to the aforementioned in-body devices are discussed. Results: A number of design challenges are associated with in-body devices, including selection of operation frequency, antenna design, powering, and biocompatibility. Nevertheless, in-body devices are opening up new opportunities for medical prevention, prognosis, and treatment that quickly outweigh any design challenges and/or concerns on their invasive nature. Conclusion: In-body devices are already in use for several medical applications, ranging from pacemakers and capsule endoscopes to injectable microstimulators. As technology continues to evolve, in-body devices are promising several new and hitherto unexplored opportunities in the healthcare. Significance: Unobtrusive in-body devices are envisioned to collect a multitude of physiological data from the early years of each individual. This big-data approach aims to enable a shift from symptom-based medicine to a proactive healthcare model.
Happy New Year! I am delighted to be writing this editorial to present the January 2024 issue of the IEEE transactions on Antennas and Propagation (TAP). More than one year has passed since I took ...over as the journal's Editor-in-Chief and time has come to look back at the previous year and reflect on the journal's position and the actions taken toward further elevating its status <xref ref-type="bibr" rid="ref1">1 . I am pleased to report that throughout 2023, TAP has maintained and strengthened its role as the flagship journal in the vibrant field of antennas and propagation, by remaining committed to the publication of timely and rigorous research.
We study the design and radiation performance of novel miniature antennas for integration in head-implanted medical devices operating in the MICS (402.0-405.0 MHz) and ISM (433.1-434.8, 868.0-868.6 ...and 902.8-928.0 MHz) bands. A parametric model of a skin-implantable antenna is proposed, and a prototype is fabricated and tested. To speed-up antenna design, a two-step methodology is suggested. This involves approximate antenna design inside a simplified geometry and further Quasi-Newton optimization inside a canonical model of the intended implantation site. Antennas are further analyzed inside an anatomical human head model. Results indicate strong dependence of the exhibited radiation performance (radiation pattern, gain, specific absorption rate and quality of communication with exterior equipment) on design parameters and operation frequency. The study provides valuable insight into the design of implantable antennas, addressing the suitability of canonical against anatomical tissue models for design purposes, and assessing patient safety and link budget at various frequencies. Finite Element and Finite Difference Time Domain numerical solvers are used at different stages of the antenna design and analysis procedures to suit specific needs. The proposed design methodology can be applied to optimize antennas for several implantation scenarios and biotelemetry applications.
<p><b>A must-have compendium on biomedical telemetry for all biomedical professional engineers, researchers, and graduate students in the field</b></p> ...<p><i>Handbook of Biomedical Telemetry</i> describes the main components of a typical biomedical telemetry system, as well as its technical challenges. Written by a diverse group of experts in the field, it is filled with overviews, highly-detailed scientific analyses, and example applications of biomedical telemetry. The book also addresses technologies for biomedical sensing and design of biomedical telemetry devices with special emphasis on powering/integration issues and materials for biomedical telemetry applications.</p> <p><i>Handbook of Biomedical Telemetry:</i></p> <ul> <li>Describes the main components of a typical biomedical telemetry system, along with the technical challenges</li> <li>Discusses issues of spectrum regulations, standards, and interoperability&#8212;while major technical challenges related to advanced materials, miniaturization, and biocompatibility issues are also included</li> <li>Covers body area electromagnetics, inductive coupling, antennas for biomedical telemetry, intra-body communications, non-RF communication links for biomedical telemetry (optical biotelemetry), as well as safety issues, human phantoms, and exposure assessment to high-frequency biotelemetry fields</li> <li>Presents biosensor network topologies and standards; context-aware sensing and multi-sensor fusion; security and privacy issues in biomedical telemetry; and the connection between biomedical telemetry and telemedicine</li> <li>Introduces clinical applications of Body Sensor Networks (BSNs) in addition to selected examples of wearable, implantable, ingestible devices, stimulator and integrated mobile healthcare system paradigms for monitoring and therapeutic intervention</li> </ul> <p>Covering biomedical telemetry devices, biosensor network topologies and standards, clinical applications, wearable and implantable devices, and the effects on the mobile healthcare system, this compendium is a must-have for professional engineers, researchers, and graduate students.</p>
It is with great excitement and honor that I am taking over the role of Editor-in-Chief of the IEEE Transactions on Antennas and Propagation (TAP). I am grateful to the Administrative Committee of ...the IEEE Antennas and Propagation Society for having entrusted me with the leadership of its flagship journal. Breakthroughs in antennas and propagation and their convergence with cutting-edge technologies are a key enabler for next-generation applications which can advance science, knowledge, and, ultimately, society. TAP provides the ideal forum to accomplish this by facilitating the communication of the most interesting and impactful studies across all sub-fields in antennas and propagation. I am excited for the opportunity to support this mission.
Season's Greetings! It is with true delight and great privilege that I am writing this editorial to present the January 2023 issue of the IEEE Transactions on Antennas and Propagation (TAP). Since I ...took over as the Editor-in-Chief in October 2022, I have worked together with the Editorial Board and the IEEE staff to ensure a smooth transition. I am particularly thankful to my predecessor, Prof. Danilo Erricolo, for all the advice and support he has offered me over the past few months.
Patients usually deviate from prescribed medication schedules and show reduced adherence. Even when the adherence is sufficient, there are conditions where the medication schedule should be modified. ...Crucial drug–drug, food–drug, and supplement–drug interactions can lead to treatment failure. We present the development of an internet of medical things (IoMT) platform to improve medication adherence and enable remote treatment modifications. Based on photos of food and supplements provided by the patient, using a camera integrated to a portable 3D-printed low-power pillbox, dangerous interactions with treatment medicines can be detected and prevented. We compare the medication adherence of 14 participants following a complex medication schedule using a functional prototype that automatically receives remote adjustments, to a dummy pillbox where the adjustments are sent with text messages. The system usability scale (SUS) score was 86.79, which denotes excellent user acceptance. Total errors (wrong/no pill) between the functional prototype and the dummy pillbox did not demonstrate any statistically significant difference (p = 0.57), but the total delay of the intake time was higher (p = 0.03) during dummy pillbox use. Thus, the proposed low-cost IoMT pillbox improves medication adherence even with a complex regimen while supporting remote dose adjustment.
The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant ...scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources.
The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases.
The model's input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models' LSTM layers for the latter to feed a dense layer.
The primary model that learned from weather data yielded better forecast accuracy (root mean square error RMSE=0.144; Pearson correlation coefficient PCC=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822).
The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The estimation of long-term diabetes complications risk is essential in the process of medical decision making. Guidelines for the management of Type 2 Diabetes Mellitus (T2DM) advocate calculating ...the Cardiovascular Disease (CVD) risk to initiate appropriate treatment. The objective of this study is to investigate the use of sophisticated machine learning techniques toward the development of personalized models able to predict the risk of fatal or nonfatal CVD incidence in T2DM patients. The important challenge of handling the unbalanced nature of the available dataset is addressed by applying novel ensemble strategies. Hybrid Wavelet Neural Networks (HWNNs) and Self-Organizing Maps (SOMs) constitute the primary models for building ensembles following a subsampling approach. Different methods for combining the decisions of the primary models are applied and comparatively assessed. Data from the 5-year follow up of 560 patients with T2DM are used for development and evaluation purposes. The highest discrimination performance (Area Under the Curve (AUC): 71.48%) is achieved by taking into account both the HWNN- and SOM- based primary models' outputs. The proposed method is superior to the Binomial Linear Regression (BLR) model justifying the need to apply more sophisticated techniques in order to produce reliable CVD risk scores.
