Comparison of Seizure Detection Algorithms in Continuously Monitored Pediatric Patients
Comparison of Seizure Detection Algorithms in Continuously Monitored Pediatric Patients
复制标题
持续监测儿科患者癫痫发作检测算法的比较
DOI：
10.1097/wnp.0b013e318033715b
复制
发表时间：
2007
影响因子：
2.4
通讯作者：
M. Kohrman
中科院分区：
文献类型：
--
作者：
Hyong Lee;W. van Drongelen;A. McGee;D. Frim;M. Kohrman
摘要：由于便携式干预设备的可能性以及在临床中提供更及时、更有效治疗的潜力，强大的、自动的癫痫发作检测一直是癫痫研究的一个重要目标。作者介绍了四种癫痫检测算法（基于主特征值[EI]，总功率，Kolmogorov熵[KE]和相关维数）如何区分4例患者（13个月至21岁）的发作期和间歇期脑电图和皮质脑电图（ECoG）。测试数据包括每位患者连续获得的46至78小时的EEG/ECoG（共245小时），检测器的准确性由委员会认证的神经科医生和经验丰富的注册脑电图技术人员检查癫痫发作。结果是因人而异的：没有一种算法在一个13个月大的病人身上表现得很好，也没有一种算法在其他三个病人身上表现得最好。其中一项指标（EI）支持在三名年龄最大的患者中存在5至15分钟的阳性期，但没有发现预测阳性期的有力证据。在一名21岁的患者中，两个指标（EI和KE）连续循环了几个小时，突出了连续分析以区分背景循环和预期循环的重要性。
Summary: Robust, automated seizure detection has long been an important goal in epilepsy research because of both the possibilities for portable intervention devices and the potential to provide prompter, more efficient treatment while in clinic. The authors present results on how well four seizure detection algorithms (based on principal eigenvalue [EI], total power, Kolmogorov entropy [KE], and correlation dimension) discriminated between ictal and interictal EEG and electrocorticoencephalography (ECoG) from four patients (aged 13 months to 21 years). Test data consisted of 46 to 78 hours of continuously acquired EEG/ECoG for each patient (245 hours total), and the detectors’ accuracy was checked against seizures found by a board-certified neurologist and an experienced registered EEG technician. The results were patient-specific: no algorithm performed well on a 13-month-old patient, and no algorithm consistently performed best on the other three patients. One of the metrics (EI) supported the existence of a postictal period of 5 to 15 minutes in the three oldest patients, but no strong evidence of a preictal anticipation was found. Two metrics (EI and KE) cycled continuously with a period of several hours in a 21-year-old patient, highlighting the importance of continuous analysis to differentiate background cycling from anticipation.