Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels.

Purpose: Calls have been made to integrate concepts and practices derived from Muslim culture into medical professionalism in Muslim societies. Little is known about how these religious cultural concepts (RCCs) influence medical practice and education. This study explored the influence of RCCs on medical professionalism in Saudi Arabia. Methods: This was a qualitative study that implemented a constructivist, grounded theory approach. Semi-structured interviews about RCCs and medical professionalism were conducted with 15 Saudi physicians at a single academic medical center. Purposive sampling was used to recruit participants of different genders, generations, and specialties. Data collection and analysis were iterative. A theoretical framework was formulated. Results: Key findings: (i) the role of RCCs in medical professionalism is perceived to be constantly evolving due to the evolution of societal interpretations of RCCs; (ii) participants described applying two standards to judge what is professional: a medical standard and a religious cultural standard. Participants shifted between these two standards variably and non-linearly. This variable shifting altered the values shaping medical professionalism, at times unpredictably. Discussion: Academic Saudi physicians argued against assuming a stable traditional interpretation of RCCs, emphasized the evolving contribution of RCCs to medical professionalism, and indicated that the process of merging religious cultural and medical standards in medical practice is variable and may alter medical practice values. Therefore, these physicians perceived RCCs to be useful as supplements to but not as a backbone for medical professionalism. Careful consideration of the potential impact of RCCs on the values of medical professionalism is warranted.

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients’ lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimized deep learning models for differentiating between MCI and normal control samples. In previous studies, the hippocampus region located in the brain is used extensively to diagnose MCI. The entorhinal cortex is a promising area for diagnosing MCI since severe atrophy is observed when diagnosing the disease before the shrinkage of the hippocampus. Due to the small size of the entorhinal cortex area relative to the hippocampus, limited research has been conducted on the entorhinal cortex brain region for predicting MCI. This study involves the construction of a dataset containing only the entorhinal cortex area to implement the classification system. To extract the features of the entorhinal cortex area, three different neural network architectures are optimized independently: VGG16, Inception-V3, and ResNet50. The best outcomes were achieved utilizing the convolution neural network classifier and the Inception-V3 architecture for feature extraction, with accuracy, sensitivity, specificity, and area under the curve scores of 70%, 90%, 54%, and 69%, respectively. Furthermore, the model has an acceptable balance between precision and recall, achieving an F1 score of 73%. The results of this study validate the effectiveness of our approach in predicting MCI and may contribute to diagnosing MCI through MRI.