Medical Scientist Training Program and Department of Biomathematics, University of California at Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, U.S.A.
Epilepsia. 2012 Nov;53(11):e189-92. doi: 10.1111/j.1528-1167.2012.03653.x. Epub 2012 Sep 11.
Interictal electroencephalography (EEG) has clinically meaningful limitations in its sensitivity and specificity in the diagnosis of epilepsy because of its dependence on the occurrence of epileptiform discharges. We have developed a computer-aided diagnostic (CAD) tool that operates on the absolute spectral energy of the routine EEG and has both substantially higher sensitivity and negative predictive value than the identification of interictal epileptiform discharges. Our approach used a multilayer perceptron to classify 156 patients admitted for video-EEG monitoring. The patient population was diagnostically diverse; 87 were diagnosed with either generalized or focal seizures. The remainder of the patients were diagnosed with nonepileptic seizures. The sensitivity was 92% (95% confidence interval [CI] 85-97%) and the negative predictive value was 82% (95% CI 67-92%). We discuss how these findings suggest that this CAD can be used to supplement event-based analysis by trained epileptologists.
间歇期脑电图（EEG）在诊断癫痫方面的敏感性和特异性存在临床意义上的局限性，因为它依赖于癫痫样放电的发生。我们开发了一种计算机辅助诊断（CAD）工具，该工具基于常规 EEG 的绝对光谱能量运行，其敏感性和阴性预测值均明显高于癫痫样放电的识别。我们的方法使用多层感知器对 156 名接受视频-EEG 监测的患者进行分类。患者人群具有诊断多样性；87 名被诊断为全身性或局灶性发作。其余患者被诊断为非癫痫性发作。敏感性为 92%（95%置信区间[CI] 85-97%），阴性预测值为 82%（95%CI 67-92%）。我们讨论了这些发现如何表明该 CAD 可用于补充受过训练的癫痫专家的基于事件的分析。
