Detection of epileptic seizures on the basis of Electroencephalogram (EEG) recordings is a challenging task due to the complex, non-stationary and non-linear nature of these biomedical signals. In the existing literature, a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms. Some characterizing features of epileptic and non-epileptic EEG signals overlap; therefore, it requires that analysis of signals must be performed from diverse perspectives. Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals. To pose the challenge mentioned above, in this paper, a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy classifiers. The proposed work extracts pattern features along with time-domain, frequencydomain, and non-linear analysis of signals. It applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic seizures. The empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG dataset. It shows significant accuracy of 98% to 100% for normal vs. ictal classification cases while for three class classification of normal vs. inter-ictal vs. ictal accuracy reaches to above 97.5%. The obtained results for ten classification cases (including normal, seizure or ictal, and seizure-free
In this article an ultra-wideband rectangular Dielectric Resonator Antenna is designed for millimeter wave 5G frequency band applications. Indoor 5G communications require antenna system with wide bandwidth and high efficiency to enhance the throughput in the channel. To fulfill such requirements a Dielectric Resonator Antenna (DRA) is designed here which has achieved an ultra-wide bandwidth of 20.15% (22.32-27.56 GHz) which is 5.24 GHz of bandwidth centered at 26 GHz as resonating frequency. This covers the complete band 30 (24.3-27.5 GHz) of 5G spectrum. 26 and 28 GHz are considered as most popular frequencies in millimeter wave 5G communications. The aperture fed DRA designed here has also achieved an efficiency of 96 percentage with maximum radiation in the broadside direction (Phi = 0, Theta = 0). The measured gain of the DRA is 6.3 dB. The DRA designed here has dimensions of 0.25 λ 0 ×0.22 λ 0 ×0.12 λ 0. under the characteristic's mode. The DRA is placed over a substrate with dimensions 0.5 λ 0 ×0.5 λ 0 ×0.02 λ 0 . A cross slot aperture has been made on the ground plane which is placed above to the substrate. Here a full ground plane is used to resonate the antenna and is of similar dimension to the substrate. A microstrip line with two concentric rings makes an annular feed structure is used to excite the DRA and is placed below the substrate. The DRA is excited here in characteristics mode TE 1Y1 and is the only mode of excitation. The DRA is linearly polarized, and the characteristic mode of excitation is maintained with 50 Ohm input impedance of the antenna. The DRA also gives here a good difference between the co-pol and cross pol approximately 15 to 20 dB. This antenna is more suitable for 5G indoor applications in millimeter wave frequency band centered at 26 GHz.
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