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
| Autor: | Francesca Della Torre, Massimo Buscema, Ronald J. Swatzyna, Enzo Grossi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Male
medicine.medical_specialty Adolescent Autism Spectrum Disorder Predictive capability Audiology Electroencephalography Diagnosis Differential Machine Learning Correlation 03 medical and health sciences 0302 clinical medicine Eeg data mental disorders medicine Humans Child 030304 developmental biology 0303 health sciences medicine.diagnostic_test Eeg analysis General Medicine medicine.disease Degree (music) Early Diagnosis Neurology Autism spectrum disorder Child Preschool Autism Female Neural Networks Computer Neurology (clinical) Psychology 030217 neurology & neurosurgery |
| Zdroj: | Clinical EEG and Neuroscience. 50:319-331 |
| ISSN: | 2169-5202 1550-0594 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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