Interpretable Deep Learning for Epileptic Seizure Prediction using EEG: A Novel Approach

Abstract

This research investigates the development of an interpretable deep learning classifier for predicting epileptic seizures using electroencephalography (EEG) data. The primary objective is to improve the accuracy and interpretability of seizure prediction models, addressing the limitations of existing black-box methods. We propose a novel architecture that combines a deep learning model with an explainability technique to provide insights into the model's decision-making process. The results demonstrate improved prediction accuracy compared to traditional methods, while also offering clinically relevant explanations, enhancing trust and facilitating clinical adoption.

Introduction

Epileptic seizures significantly impact the lives of millions worldwide. Accurate seizure prediction can dramatically improve patient management and quality of life, allowing for preventative measures. While deep learning offers potential for improved accuracy in seizure prediction from EEG data, the inherent "black-box" nature of many deep learning models hinders clinical adoption. Clinicians require interpretability to understand the model's predictions and trust its output. This lack of transparency is a critical barrier in the translation of deep learning models into clinical practice. This research addresses this challenge by developing an interpretable deep learning model for improved seizure prediction.

Technical Details

• Uses EEG signal data for seizure prediction
• Incorporates deep learning model with interpretability mechanism (e.g., Grad-CAM, LIME)
• Achieves improved accuracy and transparency over traditional models
• Designed to support clinical decision-making with explainable outputs