The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.
The research presents mutual coupling reduction between UWB-MIMO antenna elements using stub loading technique. The proposed 2 × 2 UWB antenna geometry consists of two circular-shaped monopole radiators with a partial ground for perfect impedance matching. Stubs of 20 mm × 0.2 mm are inserted between the two antenna elements in the ground plane to improve the isolation. The decoupling stub leads to a mutual coupling reduction of less than 20 dB. The farfield measurement at a selected frequency of 10 GHz confirms an omnidirectional radiation pattern. Different MIMO antenna metric such as channel capacity loss (CCL), mean effective gain (MEG), total active reflection coefficient (TARC), envelope correlation coefficient (ECC), and surface current are presented. Details of the design considerations and the simulation and measurement results are presented and discussed. The proposed MIMO antenna array can be well suited for UWB applications.
Surgical site infections (SSIs) in developing countries have been linked to inadequate availability of sterilising equipment. Existing autoclaves are mostly unaffordable by rural healthcare practitioners, and when they managed to procure them, the electricity supply to power the autoclaves is epileptic. The solar-powered autoclave alternatives are too bulky with a very high initial cost. Hence, low-cost biofuel-powered autoclave becomes an attractive option, and this study sought to present the design, development and clinical evaluation of the device performance. With the global drive for the adoption of green energy, biofuel will not only reduce greenhouse gas emission but also provide revenue for local producers and reduce biomass associated health complications. The theoretical energy requirement for the sterilisation process was calculated. The standard pressure and temperature needed for sterilisation were tested to be 121 C and 15 psi. The device was also clinically tested with Staphylococcus aureus bacteria obtained from the
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