癫痫发作检测：Reveal算法的评估
Seizure detection: evaluation of the Reveal algorithm.
作者信息
Wilson Scott B, Scheuer Mark L, Emerson Ronald G, Gabor Andrew J
机构信息
Persyst Development Corporation, 1060 Sandretto Drive, Suite E2, Prescott, AZ 86305, USA.
出版信息
Clin Neurophysiol. 2004 Oct;115(10):2280-91. doi: 10.1016/j.clinph.2004.05.018.
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.
目的
本研究旨在评估一种改进的癫痫发作检测算法，并与其他两种算法及人类专家进行比较。
方法
采用（新的）Reveal算法对426例癫痫患者的672次癫痫发作进行检测，该算法运用了三种方法，这些方法在癫痫发作检测中的应用具有创新性：匹配追踪、小型神经网络规则和一种新的连接对象层次聚类算法。
结果
Reveal算法的灵敏度为76%，假阳性率为0.11次/小时。对其他两种算法（Sensa和CNet）进行了测试，其灵敏度分别为35.4%和48.2%，假阳性率分别为0.11次/小时和0.75次/小时。
结论
本研究验证了Reveal算法，并表明其与其他方法相比具有优势。
意义
改进的癫痫发作检测可改善癫痫监测病房和重症监护病房的患者护理。
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