Epileptic Seizure Risk Analysis Using Eeg Signals

Abstract

Epileptic Seizure Is A Sudden Neurological Event Caused By Abnormal Electrical Activity In The Human Brain. These Events Can Significantly Disrupt Patient Safety And Quality Of Life. Early Prediction Of Such Events Is Essential For Enabling Timely Medical Intervention And Continuous Monitoring. This Paper Proposes An Adaptive Attention-based Deep Learning Framework To Analyze Epileptic Seizure Risks Using Electroencephalogram (EEG) Signals. The Proposed Framework Incorporates A Statistical Signal Validation Module To Ensure That Only Reliable EEG Segments Are Considered For Further Processing. The System Employs An Attention-based Feature Extraction Mechanism That Enables The Model To Automatically Focus On Important Temporal Regions Of The EEG Signal. This Mechanism Helps In Capturing Meaningful Patterns Associated With Seizure Activity While Reducing The Influence Of Irrelevant Or Noisy Data. Unlike Conventional Approaches That Rely On Fixed Thresholds, The Proposed System Introduces An Adaptive Thresholding Mechanism That Dynamically Adjusts Decision Boundaries Based On Input Signal Characteristics, Improving Performance Across Different Patients. In Addition To Performing Seizure Detection And Prediction, The Proposed System Also Includes A Risk Level Classification Module That Categorizes EEG Signals Into Low, Medium, And High Levels Of Seizure Risk. Furthermore, A Visualization Component Is Integrated Into The System To Highlight Important Signal Regions That Influence The Model’s Decisions, Thereby Improving Interpretability. A Simple Decision Support Mechanism Is Also Incorporated To Enhance The Practical Usability Of The Prediction Results. The Experimental Results Demonstrate That The Proposed System Can Effectively Detect And Predict Seizures With Reliable Performance. The Framework Provides A Practical And Efficient Solution For Intelligent Seizure Risk Analysis Using EEG Signals.

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