Seizure detection: evaluation of the Reveal algorithm
Seizure detection: evaluation of the Reveal algorithm
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DOI：
10.1016/j.clinph.2004.05.018
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发表时间：
2004-10-01
影响因子：
4.7
通讯作者：
Gabor, AJ
中科院分区：
文献类型：
--
作者：
Wilson, SB;Scheuer, ML;Gabor, AJ
目的：本研究的目的是评估一种改进的癫痫检测算法，并与其他两种算法和人类专家进行比较。方法：采用（新）Reveal算法对426例癫痫患者的672次癫痫发作进行检测，该算法采用匹配追踪、小神经网络规则和新的连接对象分层聚类算法3种方法，在癫痫发作检测中具有新颖的应用。结果：Reveal的敏感性为76%，假阳性率为0.11/h。另外两种算法（Sensa和CNet）的检测灵敏度分别为35.4%和48.2%，假阳性率分别为0.11/h和0.75/h。结论：本研究对Reveal算法进行了验证，并与其他方法进行了比较。意义：癫痫发作检测的改进可以改善癫痫监护病房和重症监护病房的患者护理。(C) 2004年国际临床神经生理学联合会。爱思唯尔爱尔兰有限公司出版。版权所有。
Objective: The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts.Methods: 672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm.Results: Reveal had a sensitivity of 76% with a false positive rate of 0.11/h. Two other algorithms (Sensa and CNet) were tested and had sensitivities of 35.4 and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively.Conclusions: This study validates the Reveal algorithm, and shows it to compare favorably with other methods.Significance: Improved seizure detection can improve patient care in both the epilepsy monitoring unit and the intensive care unit. (C) 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.