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首页> 外文期刊>Epilepsia: Journal of the International League against Epilepsy >Performance Reassessment of a Real-time Seizure-detection Algorithm on Long ECoG Series.
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Performance Reassessment of a Real-time Seizure-detection Algorithm on Long ECoG Series.

机译：Long ECoG系列上实时癫痫发作检测算法的性能重新评估。

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PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
机译：目的：自动检测和阻止癫痫发作需要高度敏感和特定的算法。这项研究通过使用比以前的研究更广泛的数据库，重新评估了算法的性能，以及该算法是否适用于安全性/有效性闭环研究以阻止人类癫痫发作。方法：离线分析了来自15个受试者的多达8个脑电图（EcoG）通道。数据的可视化和计算机化分析是由不同的（盲人）研究人员进行的。还针对临床发作和仅通过算法识别的检测结果进行了独立的视觉分析。计算出以下内容：FP率，FN数，自动检测的潜伏期，临床发作和警告时间的警告率，癫痫发作持续时间/强度和间质一致性。指示时进行适应以改善性能。结果：14名受试者符合入选标准。临床癫痫发作的通用算法“相对敏感性”为100％;适应后捕获了两个未发现的亚临床癫痫发作和两个未分类的癫痫发作。每天FP的比例为七分之零，少于其他三分之一。对四名受试者的调整率大于1 FP /天（7.7-66.6 /天），将一名受试者的比率降至0，在其余受试者中降至少于五FP /天（1.7-4.2 /天）。自动检测的一般潜伏期在13名受试者中的8名中以及在适应后的13名中的12名中小于5秒。检测为始终遵循电子照相发作的四分之三的受试者以及适应后的四分之四的受试者提供了临床发作的警告。 FP和ED的评分员间协议较低。结论：该通用算法显示出高灵敏度，特异性和速度，适应性进一步增强了特性。该算法非常适合癫痫发作的检测/警告，并用于安全性/有效性闭环治疗研究中。

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