The “MS-ROM/IFAST” Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy

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.

Separation of EOG Artifact and Power Line Noise-50Hz from EEG by Efficient Stone's BSS Algorithm

An enhanced blind source separation algorithm based on Stone's BSS approach is proposed, to reject the Electrooculogram (EOG) artifact and power line noise (50Hz) from simulated and real human Electroencephalography (EEG) signals without the notch filter, in order not to lose any useful EEG data around the 50-Hz. The proposed algorithm which called efficient Stones BSS (ESBSS) has been compared with four well-known BSS algorithms over super-Gaussian, sub-Gaussian artifacts and EEG signals with a linear mixture. In Original Stones BSS, the half-life values taken as a constant, typically (hlong≥100 hShort), but in the proposed work, an optimization procedure is used to change these values until the maximum temporal predictability is found. The real EEG data are taken from Imperial College London using a computerized EEG device with eight electrodes placed according to the 10-20 system.