Improved patient specific seizure detection during pre-surgical evaluation
Improved patient specific seizure detection during pre-surgical evaluation
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DOI：
10.1016/j.clinph.2010.10.002
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发表时间：
2011-04-01
影响因子：
4.7
通讯作者：
Bleakley, Chris J.
中科院分区：
文献类型：
--
作者：
Chua, Eric C. -P.;Patel, Kunjan;Bleakley, Chris J.
目的：有相当大的兴趣在改进的离线自动癫痫发作检测方法，将减少EEG监测单元的工作量。针对特定主题的方法已被证明比独立于主题的方法更好。然而，对于术前诊断，传统的方法获得先验数据来训练特定于受试者的分类器是不切实际的。我们提出了一种替代方法，其工作原理是通过适应一个独立于主体的阈值，以一个特定的主题，根据反馈从user.Methods：一个独立于主体的二次判别分类器，将修改后的功能部分基于Gotman算法的第一个建成。然后，通过用户交互确定受试者特定的后验概率阈值，将其用于推导受试者特定的分类器。这两个计划进行了测试，529小时的颅内脑电图，其中包括63次癫痫发作，从15名受试者进行术前评估。为了提供比较，标准的Gotman算法的实施和优化，通过调整检测thresholds.Results：相比，调整Gotman算法，受试者无关的计划减少了51%的假阳性率（0.23至0.11小时（-1）），同时增加灵敏度从53%到62%。特定于受试者的方案进一步提高了灵敏度为78%，但假阳性率小幅增加至0.18 h（-1）。结论：结果表明，具有修改功能的独立于受试者的分类器方案有助于降低假阳性率，而受试者自适应通过提高灵敏度进一步提高性能。结果还表明，所提出的主题适应分类方案近似的性能的主题特定的Gotman algorithm.Significance：所提出的方法可能会增加离线EEG分析的生产力。该方法也可以推广到提高其他主题的独立算法的性能。(C)2010年国际临床神经生理学联合会。由Elsevier爱尔兰有限公司出版。保留所有权利。
Objective: There is considerable interest in improved off-line automated seizure detection methods that will decrease the workload of EEG monitoring units. Subject-specific approaches have been demonstrated to perform better than subject-independent ones. However, for pre-surgical diagnostics, the traditional method of obtaining a priori data to train subject-specific classifiers is not practical. We present an alternative method that works by adapting the threshold of a subject-independent to a specific subject based on feedback from the user.Methods: A subject-independent quadratic discriminant classifier incorporating modified features based partially on the Gotman algorithm was first built. It was then used to derive subject-specific classifiers by determining subject-specific posterior probability thresholds via user interaction. The two schemes were tested on 529 h of intracranial EEG containing 63 seizures from 15 subjects undergoing pre-surgical evaluation. To provide comparison, the standard Gotman algorithm was implemented and optimised for this dataset by tuning the detection thresholds.Results: Compared to the tuned Gotman algorithm, the subject-independent scheme reduced the false positive rate by 51% (0.23 to 0.11 h(-1)) while increasing sensitivity from 53% to 62%. The subject-specific scheme further improved sensitivity to 78%, but with a small increase in false positive rate to 0.18 h(-1).Conclusions: The results suggest that a subject-independent classifier scheme with modified features is useful for reducing false positive rate, while subject adaptation further enhances performance by improving sensitivity. The results also suggest that the proposed subject-adapted classifier scheme approximates the performance of the subject-specific Gotman algorithm.Significance: The proposed method could potentially increase the productivity of offline EEG analysis. The approach could also be generalised to enhance the performance of other subject independent algorithms. (C) 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.