Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted Unleashing the Power of AI for Intraoperative Neuromonitoring During Carotid Endarterectomy(lawrence technological university, 2024-07-10) Roaa, Hindi; george, pappasThis research investigates the use of a 1D Convolutional Neural Network (CNN) to classify electroencephalography (EEG) signals into four categories of ischemia severity: normal, mild, moderate, and severe. The model’s accuracy was lower in moderate instances (75%) and severe cases (65%) compared to normal cases (95%) and mild cases (85%). The preprocessing pipeline now incorporates Power Spectral Density (PSD) analysis, and segment lengths of 32, 64, and 128 s are thoroughly examined. The work highlights the potential of the model to identify ischemia in real time during carotid endarterectomy (CEA) to prevent perioperative stroke. The 1D-CNN effectively captures both temporal and spatial EEG signals, providing a combination of processing efficiency and accuracy when compared to existing approaches. In order to enhance the identification of moderate and severe instances of ischemia, future studies should prioritize the integration of more complex datasets, specifically for severe ischemia, as well as increasing the current dataset. Our contributions in this study are implementing a novel 1D-CNN model to achieve a classification accuracy of over 93%, improving feature extraction by utilizing Power Spectral Density (PSD), automating the ischemia detection procedure, and enhancing model performance using a well-balanced dataset.23 0Item Restricted DEEP LEARNING-ASSISTED EPILEPSY DETECTION AND PREDICTION(Florida Atlantic University, 2024) Saem Aldahr, Raghdah; Ilyas, MohammadEpilepsy is a multifaceted neurological disorder characterized by superfluous and recurrent seizure activity. Electroencephalogram (EEG) signals are indispensable tools for epilepsy diagnosis that reflect real-time insights of brain activity. Recently, epilepsy researchers have increasingly utilized Deep Learning (DL) architectures for early and timely diagnosis. This research focuses on resolving the challenges, such as data diversity, scarcity, limited labels, and privacy, by proposing potential contributions for epilepsy detection, prediction, and forecasting tasks without impacting the accuracy of the outcome. The proposed design of diversity-enhanced data augmentation initially averts data scarcity and inter-patient variability constraints for multiclass epilepsy detection. The potential features are extracted using a graph theory-based approach by analyzing the inherently dynamic characteristics of augmented EEG data. It utilizes a novel temporal weight fluctuation method to recognize the drastic temporal fluctuations and data patterns realized in EEG signals. Designing the Siamese neural network-based few-shot learning strategy offers a robust framework for multiclass epilepsy detection. Subsequently, Federated Learning (FL) architecture enables epileptic seizure prediction and enhances the generalization capability by utilizing numerous seizure patterns across diversified and globally distributed epileptic patients. By capturing the potential patterns, the hybrid model design potentially offers superior prediction accuracy by integrating a spiking encoder with graph convolutional neural networks. The preictal probability of each local model then aggregates the weights of the local medical centers with the global FL. Furthermore, applying the adaptive neuro-fuzzy inference system ensures a patient-specific preictal probability by combining the local model with patientspecific clinical features. Finally, epileptic seizure forecasting utilizes Self-Supervised Learning (SSL) capabilities to overcome the limitations of annotated EEG data. This selfsupervised transfer learning improves the training efficiency in massively arriving EEG data streams. The dual-feature embedding enhances the learning ability while a lightweight prediction utilizes the embeddings from the pretext task for epilepsy forecasting in the downstream task. The performance testing on the benchmark datasets reveals the accuracy of epilepsy detection, prediction, and forecasting by addressing the limitations of the existing approaches for effective patient management. The research outcomes ultimately enable real-time, transparent, and personalized solutions to ensure commitment towards the quality of life.21 0Item Restricted Flight Crew’s Cognitive States Detection Using Psychophysiological Measurements and Machine Learning Techniques(Cranfield University, 2024-02-29) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl W.In the ever-evolving landscape of aviation safety, the accurate assessment of pilots' mental states is of paramount significance. This thesis elucidates the critical role of Electroencephalogram (EEG) data in comprehending pilots' cognitive conditions. The dataset, sourced from attention-related human performance limiting states, was publicly available on the NASA open portal website and encompasses EEG, electrocardiogram, galvanic skin response, and respiration data. The initial analyses delved into the challenges posed by noise within EEG recordings. After rigorous testing, it was observed that prevalent preprocessing techniques, specifically band-pass filtering coupled with Independent Component Analysis, were not always effective. This inefficiency underscored the need for more advanced methodologies to optimize machine learning outcomes. In response, subsequent research stages proposed a hybrid ensemble learning approach. This innovative approach integrated advanced automated EEG preprocessing with Riemannian geometry. Through rigorous experimentation and validation, it was determined that this methodology accentuated the profound advantages of refined preprocessing, significantly enhancing the accuracy and reliability of EEG data interpretation. As the inquiry advanced, a more integrative approach was adopted, amalgamating EEG with other physiological data. A novel methodology, synergizing one-dimensional Convolutional Neural Networks with Long Short-Term Memory architectures, was unveiled. Additionally, the impact of employing methods to handle data imbalance on machine learning performance was thoroughly examined. In the concluding phases, the research placed a heightened emphasis on model interpretability. Through the integration of SHapley Additive exPlanations values, a bridge was constructed between intricate model predictions and nuanced human comprehension, delineating paramount features for distinct cognitive states. To encapsulate, this thesis offers a meticulous dissection of EEG data manipulation, machine learning, and deep learning constructs, positing a blueprint for the augmentation of aviation safety through in-depth cognitive state evaluations.70 0Item Restricted Wearable Technology for Mental Wellness Monitoring and Feedback(Saudi Digital Library, 2023-12-31) Alhejaili, Reham; Alomainy, AkramThis thesis investigates the transformative potential of wearable monitoring devices in empowering individuals to make positive lifestyle changes and enhance mental well-being. The primary objective is to assess the efficacy of these devices in addressing mental health issues, with a specific focus on stress and anxiety biomarkers. The research includes a systematic literature review that uniquely emphasizes integrating wearable technology into mental wellness, spanning diverse domains such as electronics, wearable technology, machine learning, and data analysis. This novel systematic literature review encompasses the period from 2010 to 2023, examining the profound impact of the Internet of Things (IoT) across various sectors, particularly healthcare. The thesis extensively explores wearable technologies capable of identifying a broad spectrum of human biomarkers and stress-related indicators, emphasizing their potential benefits for healthcare professionals. Challenges faced by participants and researchers in the practical implementation of wearable technology are addressed through survey analysis, providing substantial evidence for the potential of wearables in bolstering mental health within professional environments. Meticulous data analysis gathering from biosignals captured by wearables investigates the impact of stress factors and anxiety on individuals' mental well-being. The study concludes with a thorough discussion of the findings and their implications. Additionally, integrating Photoplethysmography (PPG) devices is highlighted as a significant advancement in capturing vital biomarkers associated with stress and mental well-being. Through light-based technology, PPG devices monitor blood volume changes in microvascular tissue, providing real-time information on heart rate variability (HRV). This non-invasive approach enables continuous monitoring, offering a dynamic understanding of physiological responses to stressors. The reliability of wearable devices equipped with PPG and Electroencephalography (EEG) sensors is emphasized in capturing differences in subject biomarkers. EEG devices measure brainwave patterns, providing insights into neural activity associated with stress and emotional states. The combination of PPG and EEG data enhances the precision of stress and mental well-being assessments, offering a holistic approach that captures peripheral physiological responses and central nervous system activity. In conclusion, integrating PPG devices with subjective methods and EEG sensors significantly advances stress and mental well-being assessment. This multidimensional approach improves measurement accuracy, laying the foundation for personalized interventions and innovative solutions in mental health care. The thesis also evaluates body sensors and their correlation with medically established gold references, exploring the potential of wearable devices in advancing mental health and well-being.20 0