Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset
Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset
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DOI：
10.1111/j.1528-1157.1998.tb01430.x
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发表时间：
1998-06-01
期刊：
影响因子：
5.6
通讯作者：
Wilkinson, SB
中科院分区：
文献类型：
--
作者：
Osorio, I;Frei, MG;Wilkinson, SB
目的：我们描述一种用于癫痫发作的快速实时检测、定量、定位以及预测其临床发作的算法。
方法：利用时频定位、图像处理以及时变随机系统识别中使用的先进数字信号处理技术来开发该算法，该算法以通用或可适应的“模式”运行。“通用模式”在以下数据上进行了测试：(a) 125次部分性癫痫发作（每次发作包含在10分钟的时间段内），涉及内侧颞叶区域，使用来自16名受试者的深部电极记录；(b) 205个随机选择的发作间期（非发作）的10分钟时间段的数据。将该算法的性能与专家视觉分析（当前的“金标准”）进行了比较。
结果：通用算法在整个数据集上实现了完美的敏感性和特异性（无假阳性和假阴性检测）。癫痫发作强度这一似乎与临床相关的新指标，范围在35.7到6129之间。检测速度足够快，能够在92%的癫痫发作中平均提前15.5秒预测临床发作。
结论：这种用个人计算机实现的算法代表了朝着癫痫发作的快速准确检测和预测迈出的决定性一步。它还可能有助于开发用于自动癫痫发作预警和治疗的智能设备，并激发对癫痫发作动力学以及癫痫大脑的新研究。
Purpose: We describe an algorithm for rapid realtime detection, quantitation, localization of seizures, and prediction of their clinical onset.Methods: Advanced digital signal processing techniques used in time-frequency localization, image processing, and identification of time-varying stochastic systems were used to develop the algorithm, which operates in generic or adaptable "modes." The "generic mode" was tested on (a) 125 partial seizures (each contained in a 10-min segment) involving the mesial temporal regions and recorded using depth electrodes from 16 subjects, and (b) 205 ten-minute segments of randomly selected interictal (nonseizure) data. The performance of the algorithm was compared with expert visual analysis, the current "gold standard."Results: The generic algorithm achieved perfect sensitivity and specificity (no false-positive and no false-negative detections) over the entire data set. Seizure intensity, a novel measure that seems clinically relevant, ranged between 35.7 and 6129. Detection was sufficiently rapid to allow prediction of clinical onset in 92% of seizures by a mean of 15.5 s.Conclusions: This algorithm, which was implemented with a personal computer, represents a definitive step toward rapid and accurate detection and prediction of seizures. It may also enable development of intelligent devices for automated seizure warning and treatment and stimulate new study of the dynamics of seizures and of the epileptic brain.