Osorio Ivan, Frei Mark G, Giftakis Jon, Peters Tom, Ingram Jeff, Turnbull Mary, Herzog Michele, Rise Mark T, Schaffner Scott, Wennberg Richard A, Walczak Thaddeus S, Risinger Michael W, Ajmone-Marsan Cosimo
Comprehensive Epilepsy Center, The University of Kansas Medical Center, Kansas City, Kansas 66160-7314, USA.
Epilepsia. 2002 Dec;43(12):1522-35. doi: 10.1046/j.1528-1157.2002.11102.x.
Automated seizure detection and blockage requires highly sensitive and specific algorithms. This study reassessed the performance of an algorithm by using a more extensive database than that of a previous study and its suitability for safety/efficacy closed-loop studies to block seizures in humans.
Up to eight electrocorticography (EcoG) channels from 15 subjects were analyzed off-line. Visual and computerized analyses of the data were performed by different (blinded) investigators. Independent visual analysis also was performed for clinical seizures and for detections identified only by the algorithm. The following were computed: FP rate, number of FNs, latency to automated detection, warning rate for clinical onset and warning times, seizure duration/intensity, and interrater agreement. Adaptations to improve performance were performed when indicated.
Fourteen subjects met inclusion criteria. Generic algorithm "relative sensitivity" for clinical seizures was 100%; two undetected subclinical seizures and two unclassified seizures were captured after adaptation. FPs/day were zero in seven and fewer than one in an additional three subjects. Adaptations for four subjects with greater than 1 FP/day (7.7-66.6/day) reduced the rate to 0 in one subject and to fewer than five FP/day (1.7-4.2/day) in the remainder. Generic latency to automated detection was <5 s in eight of 13 subjects, and in 12 of 13 after adaptation. Detections provided warning of clinical onset in three of four subjects in whom it always followed electrographic onset, and in four of four after adaptation. Interrater agreement was low for FPs and EDs.
The generic algorithm demonstrated high sensitivity, specificity, and speed, characteristics further enhanced by adaptation. This algorithm is well suited for seizure detection/warning and use in safety/efficacy closed-loop therapy studies.
自动癫痫发作检测与阻断需要高度敏感和特异的算法。本研究通过使用比先前研究更广泛的数据库，重新评估了一种算法的性能及其在人类癫痫发作阻断的安全性/有效性闭环研究中的适用性。
离线分析了15名受试者多达8个皮质脑电图（EcoG）通道的数据。由不同（盲法）研究者对数据进行视觉和计算机分析。还对临床发作以及仅由算法识别出的检测结果进行了独立的视觉分析。计算了以下指标：误报率、漏报数、自动检测的潜伏期、临床发作的预警率和预警时间、发作持续时间/强度以及评分者间一致性。必要时进行了改进性能的调整。
14名受试者符合纳入标准。临床发作的通用算法“相对敏感性”为100%；调整后捕获了2次未检测到的亚临床发作和2次未分类发作。7名受试者的每日误报数为零，另外3名受试者的每日误报数少于1次。对每日误报数大于1次（7.7 - 66.6次/天）的4名受试者进行调整后，1名受试者的误报率降至0，其余受试者的误报率降至少于5次/天（1.7 - 4.2次/天）。13名受试者中有8名的自动检测通用潜伏期<5秒，调整后13名受试者中有12名如此。在4名临床发作总是跟随脑电图发作的受试者中，检测在其中3名中提供了临床发作预警，调整后在4名中均提供了预警。评分者间在误报和检测结果方面的一致性较低。
通用算法显示出高敏感性、特异性和速度，通过调整这些特性进一步增强。该算法非常适合癫痫发作检测/预警以及在安全性/有效性闭环治疗研究中的应用。
