自动化癫痫检测：未识别的挑战，意外的见解
Automated Seizure Detection: Unrecognized Challenges, Unexpected Insights
摘要：
癫痫学的基本目标之一是制定一个普遍的，内部一致的癫痫定义。为了评估这一目标的可行性，三种信号分析方法被应用到癫痫发作时间序列和性能进行比较，他们之间和相对于一个有效的算法。其中一种方法使用2n阶自回归模型的参数变化率的Fisher矩阵加权测量，另一种方法基于小波变换最大模来量化ECoG功率的标准偏差的对数的变化，并且另一种方法采用从皮层信号计算的短期到长期平均值的比率。中心发现，所有方法的输出之间的波动一致性作为癫痫发作持续时间的函数，揭示了在通往通用定义的道路上的意想不到的障碍，同时提供了动态（光谱非平稳性和不同的发作信号复杂性）和临床（可能达成共识）领域的相关知识。
英文摘要：
One of epileptology's fundamental aims is the formulation of a universal, internally consistent seizure definition. To assess this aim's feasibility, three signal analysis methods were applied to a seizure time series and performance comparisons were undertaken among them and with respect to a validated algorithm. One of the methods uses a Fisher's matrix weighted measure of the rate of parameters change of a 2n order auto-regressive model, another is based on the Wavelet Transform Maximum Modulus for quantification of changes in the logarithm of the standard deviation of ECoG power and yet another employs the ratio of short-to-long term averages computed from cortical signals. The central finding, fluctuating concordance among all methods' output as a function of seizure duration, uncovers unexpected hurdles in the path to a universal definition, while furnishing relevant knowledge in the dynamical (spectral non-stationarity and varying ictal signal complexity) and clinical (probable attainability of consensus) domains.
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