癫痫影响全球1%的人口,大约三分之一的患者对抗癫痫药物(ASM)有抗药性,造成身体伤害和心理问题的风险。癫痫发作预测算法旨在通过提供及时警报来提高这些个体的生活质量。本研究提出了一种应用于不同数据库的患者特异性癫痫发作预测算法(EPILEPSIAE,CHB-MIT,AES,和癫痫生态系统)。所提出的算法经历了一个标准化的框架,包括数据预处理,特征提取,培训,测试,和后处理。各种数据库需要在算法中进行调整,考虑数据可用性和特征的差异。该算法在数据库中表现出可变的性能,考虑到敏感性,FPR/h,特异性,和AUC评分。这项研究区分了基于样本的方法,通常通过忽略癫痫发作的时间方面来产生更好的结果,和基于警报的方法,旨在模拟现实生活条件,但产生不太有利的结果。统计评估揭示了超越机会水平的挑战,强调癫痫事件的罕见性。与现有研究的比较分析强调了标准化评估的复杂性,鉴于不同的方法和数据集的变化。旨在模拟现实生活条件的严谨方法产生不太有利的结果,强调现实假设和全面假设的重要性,长期的,和系统结构化的数据集,供未来研究使用。
Epilepsy affects 1% of the global population, with approximately one-third of patients resistant to anti-seizure medications (ASMs), posing risks of physical injuries and psychological issues. Seizure prediction algorithms aim to enhance the quality of life for these individuals by providing timely alerts. This study presents a patient-specific seizure prediction algorithm applied to diverse databases (EPILEPSIAE, CHB-MIT, AES, and Epilepsy Ecosystem). The proposed algorithm undergoes a standardized framework, including data preprocessing, feature extraction, training, testing, and postprocessing. Various databases necessitate adaptations in the algorithm, considering differences in data availability and characteristics. The algorithm exhibited variable performance across databases, taking into account sensitivity, FPR/h, specificity, and AUC score. This study distinguishes between sample-based approaches, which often yield better results by disregarding the temporal aspect of seizures, and alarm-based approaches, which aim to simulate real-life conditions but produce less favorable outcomes. Statistical assessment reveals challenges in surpassing chance levels, emphasizing the rarity of seizure events. Comparative analyses with existing studies highlight the complexity of standardized assessments, given diverse methodologies and dataset variations. Rigorous methodologies aiming to simulate real-life conditions produce less favorable outcomes, emphasizing the importance of realistic assumptions and comprehensive, long-term, and systematically structured datasets for future research.