A dual-system approach for epilepsy diagnosis: integrating mamba-Bi-LSTM architecture with SHAP-based verification

Abstract

This study develops a medical AI-assisted diagnosis system based on deep learning, which provides intelligent diagnostic solutions for epilepsy, a disease that seriously threatens the life and health of patients. Epilepsy has sudden and unpredictable seizures. Traditional diagnostic methods mainly rely on doctors' manual interpretation of EEG, which is time-consuming and dependent by experience. In response to the above challenges, this study designed a dual-system intelligent diagnosis framework, which includes two core components: the main discrimination system and the verification system. The main discrimination system uses a deep learning model that combines the innovative Mamba architecture with the Bi-LSTM structure to integrate and analyze heterogeneous data to achieve extremely high diagnostic accuracy; the verification system provides an explainable diagnostic basis through the SHAP method to enhance the credibility of the results. This system establishes a cross-modal database to realize intelligent analysis of multi-source heterogeneous data-fusion EEG signals and clinical text data for epilepsy. The system outputs results based on diagnostic consistency and confidence levels, and high-confidence predictions can also be used as automatic feedback sources to optimize the model. The experimental results show that the accuracy of the main discriminant model of the intelligent diagnosis system for epilepsy has increased from 92.6% to 98.7% and the F1 score has increased from 0.895 to 0.992, all of which have exceeded the existing optimal methods; the average processing time for verification system feedback integration is only 220 ms, which increases the overall diagnostic accuracy by 5.1%.