Abstract：
The efficient patient-independent and interpretable framework for electroencephalogram (EEG) epileptic seizure detection (ESD) has informative challenges due to the complex pattern of EEG nature. Automated detection of ES is crucial, while Explainable Artificial Intelligence (XAI) is urgently needed to justify the model detection of epileptic seizures in clinical applications. Therefore, this study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules, including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system. To ensure the privacy and security of biomedical EEG data, the blockchain is employed. Initially, the Butterworth filter eliminates various artifacts, and the Dual-Tree Complex Wavelet Transform (DTCWT) decomposes EEG signals, extracting real and imaginary eigenvalue features using frequency domain (FD), time domain (TD) linear feature, and Fractal Dimension (FD) of non-linear features. The best features are selected by using Correlation Coefficients (CC) and Distance Correlation (DC). The selected features are fed into the Stacking Ensemble Classifiers (SEC) for EEG ES detection. Further, the Shapley Additive Explanations (SHAP) method of XAI is implemented to facilitate the interpretation of predictions made by the proposed approach, enabling medical experts to make accurate and understandable decisions. The proposed Stacking Ensemble Classifiers (SEC) in XAI-CAESDs have demonstrated 2% best average accuracy, recall, specificity, and F1-score using the University of California, Irvine, Bonn University, and Boston Children\'s Hospital-MIT EEG data sets. The proposed framework enhances decision-making and the diagnosis process using biomedical EEG signals and ensures data security in smart healthcare systems.
摘要：
脑电图（EEG）癫痫发作检测（ESD）的有效独立于患者且可解释的框架由于EEG性质的复杂模式而具有信息性挑战。ES的自动检测至关重要，迫切需要可解释的人工智能（XAI）来证明临床环境中的算法预测。因此,本研究实现了基于XAI的计算机辅助ES检测系统(XAI-CAESDs)，包括特征工程模块在内的三个主要模块，癫痫发作检测模块，以及智能医疗保健系统中的可解释决策过程模块。为了确保生物医学脑电数据的隐私性和安全性,区块链被采用。最初，巴特沃斯滤波器消除了各种伪影，双树复小波变换(DTCWT)分解脑电信号，使用频域(FD)提取实值和虚值特征值特征，时域(TD)，线性和非线性特征的分形维数(FD)。通过使用相关系数(CC)和距离相关(DC)来选择最佳特征。所选择的特征被馈送到用于EEGES检测的堆叠集成分类器(SEC)中。Further,XAI的Shapley加法解释(SHAP)方法是为了方便对所提出的方法所做的预测进行解释，使医学专家能够做出准确和易于理解的决定。XAI-CAESD中提出的基于集成的堆叠分类器已证明了2％的最佳平均精度，回想一下，特异性，和F1分数使用加州大学，Irvine,波恩大学，和波士顿儿童医院-麻省理工学院脑电图数据集。所提出的框架使用生物医学EEG信号增强了决策和诊断过程，并确保了智能医疗保健系统中的数据安全。
