In this paper Naive Bayesian classifiers were applied for the purpose of differentiation between the EEG signals recorded from children with Fetal Alcohol Syndrome Disorders (FASD) and healthy ones. This work also provides a brief introduction to the FASD itself, explaining the social, economic and genetic reasons for the FASD occurrence. The obtained results were good and promising and indicate that EEG recordings can be a helpful tool for potential diagnostics of FASDs children affected with it, in particular those with invisible physical signs of these spectrum disorders.

Department of Automatic Control and Robotics AGH University of Science and Technology 30 059 Krakow Poland

Department of Bioinformatics and Telemedicine Jagiellonian University in Krakow Collegium Medicum 30 688 Krakow Poland

Department of Cybernetics and Biomedical Engineering VSB Technical University Ostrava FEECS 708 00 Ostrava Poruba Czech Republic

Department of Pathophysiology Jagiellonian University in Krakow Collegium Medicum 31 121 Krakow Poland

Faculty of Electrical Engineering Opole University of Technology 45 758 Opole Poland

St Louis Children Hospital 31 503 Krakow Poland

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Duarte V.d.J., Leger P., Contreras S., Fukuda H. Detecting Fetal Alcohol Spectrum Disorder in children using Artificial Neural Network. arXiv. 20212105.15074