Background and Objective. In a previous study, we showed a new EEG processing methodology called Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time (MS-ROM/IFAST) performing an almost perfect distinction between computerized EEG of Italian children with autism spectrum disorder (ASD) and typically developing children. In this study, we assessed this system in distinguishing ASD subjects from children affected with other neuropsychiatric disorders (NPD). Methods. At a psychiatric practice in Texas, 20 children diagnosed with ASD and 20 children diagnosed with NPD were entered into the study. Continuous segments of artifact-free EEG data lasting 10 minutes were entered in MS-ROM/IFAST. From the new variables created by MS-ROM/IFAST, only 12 has been selected according to a correlation criterion. The selected features represent the input on which supervised machine learning systems (MLS) acted as blind classifiers. Results. The overall predictive capability in distinguishing ASD from other NPD cases ranged from 93% to 97.5%. The results were confirmed in further experiments in which Italian and US data have been combined. In this analysis, the best MLS reached 95.0% global accuracy in 1 out of 3 classes distinction (ASD, NPD, controls). This study demonstrates the value of EEG processing with advanced MLS in the differential diagnosis between ASD and NPD cases. The results were not affected by age, ethnicity and technicalities of EEG acquisition, confirming the existence of a specific EEG signature in ASD cases. To further support these findings, it was decided to test the behavior of already trained neural networks on 10 Italian very young ASD children (25-37 months). In this test, 9 out of 10 cases have been correctly recognized as ASD subjects in the best case. Conclusions. These results confirm the possibility of an early automatic autism detection based on standard EEG.

中文翻译：

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“MS-ROM/IFAST”模型是一种新型并行非线性脑电图分析技术，可将 ASD 受试者与受其他神经精神疾病影响的儿童进行高准确度区分

背景和目标。在之前的一项研究中，我们展示了一种新的脑电图处理方法，称为多尺度排序组织图/隐式函数作为挤压时间 (MS-ROM/IFAST)，在意大利自闭症谱系障碍 (ASD) 儿童的计算机化脑电图之间进行了几乎完美的区分和通常发育的儿童。在这项研究中，我们评估了该系统将 ASD 受试者与其他神经精神疾病 (NPD) 影响的儿童区分开来。方法。在德克萨斯州的一家精神病诊所，20 名被诊断为 ASD 的儿童和 20 名被诊断为 NPD 的儿童被纳入研究。在 MS-ROM/IFAST 中输入持续 10 分钟的无伪影 EEG 数据的连续片段。从 MS-ROM/IFAST 创建的新变量中，根据相关标准仅选择了 12 个。所选特征表示监督机器学习系统 (MLS) 作为盲分类器的输入。结果。区分 ASD 与其他 NPD 病例的整体预测能力范围为 93% 至 97.5%。结果在进一步的实验中得到证实，其中意大利和美国的数据已经结合。在该分析中，最佳 MLS 在 3 个类别中的 1 个类别（ASD、NPD、对照）中达到了 95.0% 的全局准确度。本研究证明了使用高级 MLS 进行 EEG 处理在 ASD 和 NPD 病例之间的鉴别诊断中的价值。结果不受年龄、种族和 EEG 采集技术的影响，证实了 ASD 病例中存在特定的 EEG 特征。为了进一步支持这些发现，决定在 10 名意大利非常年幼的 ASD 儿童（25-37 个月）上测试已经训练好的神经网络的行为。在该测试中，10 个案例中有 9 个在最佳案例中被正确识别为 ASD 对象。结论。这些结果证实了基于标准 EEG 进行早期自动自闭症检测的可能性。