Wilson Scott B, Emerson Ronald
Persyst Development Corporation, Prescott, AZ 86305, USA.
Clin Neurophysiol. 2002 Dec;113(12):1873-81. doi: 10.1016/s1388-2457(02)00297-3.
For algorithm developers, this review details recent approaches to the problem, compares the accuracy of various algorithms, identifies common testing issues and proposes some solutions. For the algorithm user, e.g. electroencephalograph (EEG) technician or neurologist, this review provides an estimate of algorithm accuracy and comparison to that of human experts. Manuscripts dated from 1975 are reviewed. Progress since Frost's 1985 review of the state of the art is discussed. Twenty-five manuscripts are reviewed. Many novel methods have been proposed including neural networks and high-resolution frequency methods. Algorithm accuracy is less than that of experts, but the accuracy of experts is probably less than what is commonly believed. Larger record sets will be required for expert-level detection algorithms.
对于算法开发者而言，本综述详细介绍了针对该问题的最新方法，比较了各种算法的准确性，识别了常见的测试问题并提出了一些解决方案。对于算法使用者，例如脑电图（EEG）技术人员或神经科医生，本综述提供了算法准确性的评估，并与人类专家的准确性进行了比较。对1975年以来的手稿进行了综述。讨论了自弗罗斯特1985年对该领域技术水平进行综述以来的进展。共审查了25篇手稿。已经提出了许多新颖的方法，包括神经网络和高分辨率频率方法。算法的准确性低于专家，但专家的准确性可能也低于普遍认知。专家级检测算法将需要更大的记录集。
