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Multi-Model analysis for Epilepsy Detection

  • Unique Paper ID: 161977
  • Volume: 10
  • Issue: 7
  • PageNo: 167-169
  • Abstract:
  • This research initiative introduces a pioneering project that integrates scalp dataset recordings into an epilepsy detection framework, emphasizing the effective recording of data in a specific time frequency. The project significantly expands the epilepsy detection dataset by incorporating these scalp recordings. Various machine learning algorithms, including Support Vector Machines (SVM), Random Forest, XGBoost, and K-Nearest Neighbours, are systematically employed. The project meticulously investigates and compares the accuracies of these models, offering valuable insights into their individual strengths and weaknesses. The comprehensive analysis involves the training and evaluation of each model using the enriched dataset. The predictive prowess of SVM in decoding intricate patterns within EEG data is complemented by the robust ensemble learning of Random Forest, the gradient boosting capabilities of XGBoost, and the proximity-based classification of K-Nearest Neighbours. Through the utilization of these diverse algorithms, the project seeks to identify the most effective approach for epilepsy detection. This multi-model comparison not only enhances the understanding of how the integration of scalp datasets impacts detection accuracy but also illuminates the performance nuances of each machine learning algorithm. The findings contribute to the ongoing discourse in the field of epilepsy detection, providing insights that pave the way for more informed and nuanced approaches to diagnosis and treatment.
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  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 7
  • PageNo: 167-169

Multi-Model analysis for Epilepsy Detection

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8.01 (Year 2024)
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Journal no 47859

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