A novel dry foam-based electrode for long-term EEG measurement was proposed in this study. In general, the conventional wet electrodes are most frequently used for EEG measurement. However, they require skin preparation and conduction gels to reduce the skin-electrode contact impedance. The aforementioned procedures when wet electrodes were used usually make trouble to users easily. In order to overcome the aforesaid issues, a novel dry foam electrode, fabricated by electrically conductive polymer foam covered by a conductive fabric, was proposed. By using conductive fabric, which provides partly polarizable electric characteristic, our dry foam electrode exhibits both polarization and conductivity, and can be used to measure biopotentials without skin preparation and conduction gel. In addition, the foam substrate of our dry electrode allows a high geometric conformity between the electrode and irregular scalp surface to maintain low skin-electrode interface impedance, even under motion. The experimental results presented that the dry foam electrode performs better for long-term EEG measurement, and is practicable for daily life applications.

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.

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Background Although acute cardiac injury (ACI) is a known COVID-19 complication, whether ACI acquired during COVID-19 recovers is unknown. This study investigated the incidence of persistent ACI and identified clinical predictors of ACI recovery in hospitalized patients with COVID-19 2.5 months post-discharge. Methods This retrospective study consisted of 10,696 hospitalized COVID-19 patients from March 11, 2020 to June 3, 2021. Demographics, comorbidities, and laboratory tests were collected at ACI onset, hospital discharge, and 2.5 months post-discharge. ACI was defined as serum troponin-T (TNT) level >99th-percentile upper reference limit (0.014ng/mL) during hospitalization, and recovery was defined as TNT below this threshold 2.5 months post-discharge. Four models were used to predict ACI recovery status. Results There were 4,248 (39.7%) COVID-19 patients with ACI, with most (93%) developed ACI on or within a day after admission. In-hospital mortality odds ratio of ACI patients was 4.45 [95%CI: 3.92, 5.05, p<0.001] compared to non-ACI patients. Of the 2,880 ACI survivors, 1,114 (38.7%) returned to our hospitals 2.5 months on average post-discharge, of which only 302 (44.9%) out of 673 patients recovered from ACI. There were no significant differences in demographics, race, ethnicity, major commodities, and length of hospital stay between groups. Prediction of ACI recovery post-discharge using the top predictors (troponin, creatinine, lymphocyte, sodium, lactate dehydrogenase, lymphocytes and hematocrit) at discharge yielded 63.73%-75.73% accuracy. Interpretation Persistent cardiac injury is common among COVID-19 survivors. Readily available patient data accurately predict ACI recovery post-discharge. Early identification of at-risk patients could help prevent long-term cardiovascular complications. Funding None

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Skin-like electronics that can provide comprehensively tactile sensing is required for applications such as soft robotics, health monitoring, medical treatment, and human−machine interfaces. In particular, the capacity to monitor the contact parameters such as the magnitude, direction, and contact location of external forces is crucial for skin-like tactile sensing devices. Herein, a flexible electronic skin which can measure and discriminate the contact parameters in real time is designed. It is fabricated by integrating the three-dimensional (3D) hollow MXene spheres/Ag NW hybrid nanocomposite-based embedded stretchable electrodes and T-ZnOw/PDMS film-based capacitive pressure sensors. To the best of our knowledge, it is the first stretchable electrode to utilize the 3D hollow MXene spheres with the essential characteristic, which can effectively avoid the drawbacks of stress concentration and shedding of the conductive layer. The strain-resistance module and the pressurecapacitance module show the excellent sensing performance in stability and response time, respectively. Moreover, a 6 × 6 sensor array is used as a demonstration to prove that it can realize the multiplex detection of random external force stimuli without mutual interference, illustrating its potential applications in biomimetic soft wearable devices, object recognition, and robotic manipulation.

This paper presents a highly sensitive closed loop enclosed split ring biosensor operating in microwave frequencies for measuring blood glucose levels in the human body. The proposed microwave glucose biosensor, working on the principle of high field confinement and concentrated energy, has been tested using both in-vitro and in-vivo methods. This principle allows the sensor to concentrate energy at the surface which results in improved accuracy of measurements. For in-vitro measurements, the biosensor has been tested using de-ionized water glucose solutions of different concentrations. The miniaturized micrometer scale biosensor is fabricated over a thin Si-substrate using photolithographic technique. The biosensor has been designed in a way to operate at desired microwave frequencies. Highly confined fields and concentrated energy inside the closed loop line containing the split ring resonators are responsible for the sensitivity enhancement. This new biosensor has obtained a high sensitivity of 82 MHz/mgmL −1 within the clinical diabetic range during in-vivo testing over the human body. In addition, the subjects (undergoing experiments) steady state has been continuously monitored throughout the experiment which helps in improving the accuracy of the results. The proposed biosensor has further obtained a low detection limit of <0.05 wt.% and can be useful for continuous non-invasive blood glucose monitoring.