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Bioavailable testosterone and androgen receptor activation, but not total testosterone, are associated with muscle mass and strength in females
Get PDFAccelerating Evolutionary Multitasking Optimization With a Generalized GPU-Based Framework
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Single-extracellular vesicle (EV) analyses validate the use of L1 Cell Adhesion Molecule (L1CAM) as a reliable biomarker of neuron-derived EVs
Get PDFEvaluating the Use of Web-Based Technologies for Self-Management among Arabic-Speaking Immigrants Living with Type 2 Diabetes Mellitus: A Cross-Sectional Study in Saudi Arabia
Get PDFSome Refinements and Reverses of Callebaut's Inequality for Isotonic Functionals via a Result Due to Cartwright and Field
Get PDFTransforming the experiential learning of sport management graduates into transferable employability signals
Get PDFGENet: A Generic Neural Network for Detecting Various Neurological Disorders from EEG
No full textThe global health burden of neurological disorders (NDs) is vast, and they are recognized as major causes of mortality and disability worldwide. Most existing NDs detection methods are disease-specific, which limits an algorithm’s cross-disease applicability. A single diagnostic platform can save time and money over multiple diagnostic systems. There is currently no unified standard platform for diagnosing different types of NDs utilizing electroencephalogram (EEG) signal data. To address this issue, this study aims to develop a Generic EEG neural Network (GENet) framework based on convolutional neural network that can identify various NDs from EEG. The proposed framework consists of several parts: (1) preparing data using channel reduction, resampling, and segmentation for the GENet model; (2) designing and training the GENet model to carry out important features for the classification task; and (3) assessing the proposed model’s performance using different signal segment lengths, and several training batch sizes. Also cross-validating using seven different EEG datasets of six distinct NDs named schizophrenia, autism spectrum disorder, epilepsy, Parkinson’s disease, mild cognitive impairment, and attention-deficit/hyperactivity disorder. In addition, this study also investigates whether the proposed GENet model can identify multiple NDs from EEG. The proposed model achieved much better performance for both binary and multi-class classification compared to state-of-the-art methods. In addition, the proposed model is validated using several ablation studies and layer-wise feature visualization, which provide consistency and efficiency to the proposed model. The proposed GENet model will help technologists create standard software for detecting any of these NDs from EEG
