1 Villa Santa Maria Foundation, Neuropsychiatric Rehabilitation Center, Autism Unit, Tavernerio (Como), Italy.
2 Semeion Research Centre of Sciences of Communication, Rome, Italy.
Clin EEG Neurosci. 2019 Sep;50(5):319-331. doi: 10.1177/1550059419861007. Epub 2019 Jul 11.
. 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). . 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. . 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. . These results confirm the possibility of an early automatic autism detection based on standard EEG.
. 在之前的研究中，我们展示了一种新的 EEG 处理方法，称为多尺度排序组织图/隐函数作为挤压时间（MS-ROM/IFAST），可以近乎完美地区分意大利自闭症谱系障碍（ASD）儿童和正常发育儿童的计算机化 EEG。在这项研究中，我们评估了该系统在区分 ASD 受试者和患有其他神经精神障碍（NPD）的儿童的能力。. 在德克萨斯州的一家精神病诊所，我们纳入了 20 名被诊断为 ASD 的儿童和 20 名被诊断为 NPD 的儿童。无伪迹 EEG 数据的连续片段，持续 10 分钟，输入 MS-ROM/IFAST。从 MS-ROM/IFAST 创建的新变量中，仅根据相关性标准选择了 12 个变量。选择的特征代表了监督机器学习系统（MLS）作为盲分类器的输入。. 区分 ASD 与其他 NPD 病例的总体预测能力为 93%至 97.5%。在将意大利和美国的数据合并的进一步实验中，验证了这些结果。在这项分析中，最好的 MLS 在 1 个 3 类区分（ASD、NPD、对照）中达到了 95.0%的全局准确性。这项研究表明，在 ASD 和 NPD 病例的鉴别诊断中，先进的 MLS 对 EEG 处理具有重要价值。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，我们决定在 10 名意大利非常年轻的 ASD 儿童（25-37 个月）上测试已经训练好的神经网络的行为。在这项测试中，最好的情况下，有 9 例被正确识别为 ASD 患者。. 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。
