想绕过复杂预处理和黑箱模型检测癫痫发作？这篇论文的临界转换方法在多个大鼠记录中达到接近专家水平，通用参数依然高表现，值得研究。
该研究提出一种基于临界转换的癫痫发作检测算法，克服了传统方法需大量预处理和依赖黑箱机器学习的局限。通过对癫痫大鼠不同形态发作的电压记录进行受试者工作特征分析，量化算法与专家标注的发作起止时间的一致性。在多数记录会话中，算法达到接近专家水平的性能，并最终推导出一组适用于所有会话的通用参数，保持高性能。该算法对多变癫痫形态具有鲁棒性，可补充现有机器学习方法。
Detecting seizure onset and offset times using human intelligence: A critical-transitions-based approach
Most existing seizure detection algorithms require extensive pre-processing of the data and rely on heuristic or currently unexplainable machine learning approaches. These approaches often struggle with balancing detection sensitivity and specificity in the presence of variable seizure morphologies, interictal epileptiform discharges, and artefacts. Here, we consider an alternative approach: our seizure detection algorithm, which is based on the concept of critical transitions and overcomes the aforementioned limitations. Specifically, we perform a receiver-operating-characteristic analysis to quantify the performance of our algorithm in terms of its agreement with expert annotations of seizure onset and offset times in the voltage recordings of seizure activity in epileptic rodents with different seizure morphologies. We demonstrate how performance depends on algorithm parameters and varies across different rodent recording sessions. We determine the optimal set of algorithm parameters for each recording session, with near expert-level performance achieved in most cases. Finally, we derive a single general set of algorithm parameters applicable across all recording sessions. The algorithm maintains its high performance in this general setting, demonstrating its versatility, robustness across varying seizure morphologies, and potential to complement machine learning algorithms.