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首页> 外文期刊>Annals of Biomedical Engineering: The Journal of the Biomedical Engineering Society >Seizure detection using seizure probability estimation: comparison of features used to detect seizures.
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Seizure detection using seizure probability estimation: comparison of features used to detect seizures.

机译：使用癫痫发作概率估算进行癫痫发作检测：用于检测癫痫发作的特征比较。

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摘要

This paper analyses seizure detection features and their combinations using a probability-based scalp EEG seizure detection framework developed by Marc Saab and Jean Gotman. Our method was evaluated on 525 h of data, including 88 seizures in 21 patients. The individual performances of the three features used by Saab and Gotman were compared to six alternative features, and combinations of these nine features were analyzed in order to find a superior detector. On a testing set with the combination of their three features, Saab and Gotman reported a sensitivity of 0.78, a false positive rate of 0.86/h, and a median detection delay of 9.8 s. Based on 10-fold cross-validation the testing performance of our implementation of their method achieved a sensitivity of 0.79, a false positive rate of 0.62/h, and a median detection delay of 21.3 s. A detector based on an alternative combination of features achieved sensitivity of 0.81, a false positive rate of 0.60/h, and a median detection delay of 16.9 s. By including filtering techniques, it was possible to achieve performance levels similar to Saab and Gotman using our implementation of their method, although this involved increases in detection delays. Of the seizure detection measures investigated, relative average amplitude, relative power, relative derivative, and coefficent of variation of amplitude provided the best performing combinations. These better-performing features can be employed together to make robust and reliable seizure detectors.
机译：本文使用Marc Saab和Jean Gotman开发的基于概率的头皮脑电图癫痫发作检测框架来分析癫痫发作检测特征及其组合。我们对525小时的数据进行了评估，其中包括21例患者的88例癫痫发作。萨博和戈特曼使用的三个功能的个人性能与六个替代功能进行了比较，并对这九个功能的组合进行了分析，以找到一种出色的探测器。 Saab和Gotman在结合了这三个功能的测试装置上，灵敏度为0.78，假阳性率为0.86 / h，中位检测延迟为9.8 s。基于10倍交叉验证，我们实施他们的方法的测试性能达到了0.79的灵敏度，0.62 / h的假阳性率和21.3 s的中值检测延迟。基于特征的替代组合的检测器实现了0.81的灵敏度，0.60 / h的误报率和16.9 s的中值检测延迟。通过包括滤波技术，使用我们的方法实现有可能达到与Saab和Gotman相似的性能水平，尽管这会增加检测延迟。在调查的癫痫发作检测措施中，相对平均幅度，相对功率，相对导数和幅度变化系数提供了最佳的组合。这些性能更好的功能可以一起使用，以制造出坚固可靠的癫痫发作检测器。

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