Publicação
EPIWEB: A web-based application for detecting and predicting epileptic seizures
| Resumo: | This dissertation explores the major obstacles presented by epilepsy, a neurological disorder that causes frequent seizures. The study presents EPIWEB, a user-friendly web platform devised to improve the identification and forecasting of epileptic seizures. By utilizing web development, data analysis, and machine learning techniques, EPIWEB delivers an easy-to-use interface to create, arrange, and scrutinize EEG data. EPIWEB provides researchers with key tools for visualising raw EEG data, extracting features, and utilising classification algorithms. EPIWEB's architecture, grounded in the Django framework and tailored for scalability, guarantees smooth data processing integration. Importantly, the platform emphasises user authentication and data protection to secure sensitive patient information. This dissertation explores in detail the core concepts that form the basis of predicting epileptic seizures. It encompasses the analysis of EEG signals, feature extraction, and an extensive evaluation of past research, including the important EPILAB project. This work addresses potential risks and implements strategies to mitigate challenges arising from the ambitious project scope and evolving requirements. EPIWEB undergoes thorough validation against functional requirements, demonstrating its capacity and potential in analyzing and predicting epileptic seizures. The platform's proficiency in managing patient data, conducting research, and displaying EEG information is convincingly demonstrated. In conclusion, EPIWEB shows potential for advancing the field of epileptic seizure detection and prediction. Its noteworthy attributes makes it a valuable resource for academics and professionals in this pivotal field. |
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| Autores principais: | Nogueira, Gonçalo Cardoso |
| Assunto: | Epilepsy Web Application EEG Signal Analysis Machine Learning Seizure Detection Epilepsia Aplicação Web Processamento de Sinal EEG Aprendizagem Computacional Deteção de Crises Epiléticas |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Coimbra |
| Idioma: | inglês |
| Origem: | Estudo Geral - Universidade de Coimbra |
| Resumo: | This dissertation explores the major obstacles presented by epilepsy, a neurological disorder that causes frequent seizures. The study presents EPIWEB, a user-friendly web platform devised to improve the identification and forecasting of epileptic seizures. By utilizing web development, data analysis, and machine learning techniques, EPIWEB delivers an easy-to-use interface to create, arrange, and scrutinize EEG data. EPIWEB provides researchers with key tools for visualising raw EEG data, extracting features, and utilising classification algorithms. EPIWEB's architecture, grounded in the Django framework and tailored for scalability, guarantees smooth data processing integration. Importantly, the platform emphasises user authentication and data protection to secure sensitive patient information. This dissertation explores in detail the core concepts that form the basis of predicting epileptic seizures. It encompasses the analysis of EEG signals, feature extraction, and an extensive evaluation of past research, including the important EPILAB project. This work addresses potential risks and implements strategies to mitigate challenges arising from the ambitious project scope and evolving requirements. EPIWEB undergoes thorough validation against functional requirements, demonstrating its capacity and potential in analyzing and predicting epileptic seizures. The platform's proficiency in managing patient data, conducting research, and displaying EEG information is convincingly demonstrated. In conclusion, EPIWEB shows potential for advancing the field of epileptic seizure detection and prediction. Its noteworthy attributes makes it a valuable resource for academics and professionals in this pivotal field. |
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