Previously, we described how patients with new-onset Alzheimer’s disease were differentiated from healthy, normal subjects to 100% accuracy, based on the amplitudes of the nonrhythmic back-projected independent components of the P300 peak at the electroencephalogram electrodes and their latency in the response to an oddball, auditory evoked potential paradigm. A neural network and a voting strategy were used for classification. Here, we consider instead the statistical distribution functions of their latencies and amplitudes and suggest that the 2-sample Kolmogorov-Smirnov test based upon their latency distribution functions offers an alternative biomarker for AD, with their amplitude distribution at the frontal electrode fp2 as possibly another. The technique is general, relatively simple, and noninvasive and might be applied for presymptomatic detection, although further validation with more subjects, preferably in multicenter studies, is recommended. It may also be applicable to study the other P300 peaks and their associated interpretations.

之前，我们描述了如何根据脑电图电极上 P300 峰的非节律反向投影独立分量的振幅及其响应延迟，以 100% 的准确度区分新发阿尔茨海默病患者与健康正常受试者到一个奇怪的、听觉诱发的潜在范式。使用神经网络和投票策略进行分类。在这里，我们考虑的是它们的延迟和幅度的统计分布函数，并建议基于它们的延迟分布函数的 2 样本 Kolmogorov-Smirnov 测试为 AD 提供了替代生物标志物，其额叶电极 fp2 处的幅度分布可能是另一个。该技术通用、相对简单且无创，可用于症状前检测，但建议对更多受试者（最好是在多中心研究中）进行进一步验证。它也可能适用于研究其他 P300 峰及其相关解释。