Does a CNN and XAI integrated system accurately detect seizures from EEG signals while providing interpretability?
The integration of CNNs and XAI (SHAP) provides a transparent and interpretable system for detecting epileptic seizures from EEG data.
• This research focuses on developing an advanced system for the detection of epileptic seizures using a combination of convolutional neural networks (CNNs) and explainable artificial intelligence (XAI) techniques. The system utilizes electroencephalogram (EEG) data to train the model, employing deep learning for robust feature extraction. Key components of this research include: • Epileptic Seizure Detection: Utilizing CNNs, this study aims to accurately detect seizures, which are characterized by rapid and uncontrolled bursts of electrical activity in the brain. • Integration of Explainable AI (XAI): The research incorporates XAI techniques, specifically Shapley additive explanations (SHAP), to enhance the interpretability of the model. This allows for a transparent understanding of how and why certain decisions are made by the model, providing insights into the critical features influencing its predictions. • Feature Extraction and Model Visualization: By combining deep learning with effective feature extraction methods, the research facilitates the identification of significant EEG data features. Model visualization and SHAP-based feature importance analysis provide clear insights into the decision-making process of the CNN, enhancing trust and usability. • Performance Evaluation: The proposed system's effectiveness is evaluated using metrics such as specificity and accuracy, ensuring reliable seizure detection. High specificity minimizes false positives, and high accuracy ensures that the system can reliably detect epileptic events. • Objective and Impact: The primary goal is to develop simple yet effective seizure detection systems that enable early identification of epilepsy in patients. This, in turn, supports the creation of individualized treatment plans, improving patient care and outcomes. The research aims to advance the field of epilepsy seizure detection, ultimately contributing to better patient care and management. • By leveraging advanced AI techniques and focusing on interpretability, this research paves the way for more reliable and understandable seizure detection methods, which are crucial for timely and effective epilepsy management. A seizure is a rapid, uncontrolled burst of electrical activity in the brain. Epilepsy is a neurological disorder caused by repeated seizures. Effective management requires early detection. This research aims to create a convolutional neural network (CNN) and explainable artificial intelligence (XAI) integrated system for epileptic seizure detection, using techniques such as Shapley additive explanation (SHAP) for better interpretability. The relevant data is acquired from a variety of electroencephalogram (EEG) dataset to facilitate the development of the model. To identify the EEG data, the proposed system combines deep learning models with feature extraction technique. Model visualization and XAI approaches like feature importance analysis from SHAP values offer clear insights into the model's decision-making process. Evaluation criteria like specificity and accuracy are used to assess the models' performance. This framework's objective is to create simple seizure detection systems that assist early epilepsy patient identification and individualized treatment plans. This research aims in opening the door to improved patient care and treatment by developing the field of epilepsy seizure detection.
Murugan et al. (Fri,) studied this question.