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Abstract Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.

The image's quality is a crucial consideration in medical imaging, therefore selecting a fiber that offers more benefits is important. The primary objective of this work is to explore the imperative applications of Photonic Crystal Fiber (PCF) in medical imaging. Numerical studies utilizing the finite element method and a normal mesh have been performed on the proposed PCF with several different types of fiber materials in the core and background. More broadly in terms of wavelength, from 0.5 μm to 1.6 μm, some of the optical parameters investigated, including confinement loss, nonlinearity, effective area, and numerical aperture (NA). Changing PCF’s background material from BK7, fused silica, or ZBLAN has resulted in a shift in the parameters. Here, BK7 is discovered to be a promising possibility in medical imaging, and a thorough comparative analysis of PCF parameters has been performed. The comparison reveals that at an optical wavelength of 0.5 μm, the proposed PCF with BK7 exhibits strong nonlinearity of 119 W−1Km−1, low confinement loss of 2.97 × 10−12 dB/m, a numerical aperture of 0.15, along with a very low effective area of 4.18 × 10−12 m2. It is therefore anticipated that the suggested PCF with BK7 will be more suitable for the imaging application.