Next Article in Journal
A Robust Design for Aperture-Level Simultaneous Transmit and Receive with Digital Phased Array
Next Article in Special Issue
Advanced Computing Methods for Impedance Plethysmography Data Processing
Previous Article in Journal
Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing
Previous Article in Special Issue
Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals
Article

Pilot Study on Analysis of Electroencephalography Signals from Children with FASD with the Implementation of Naive Bayesian Classifiers

1
St. Louis Children Hospital, 31-503 Krakow, Poland
2
Department of Pathophysiology, Jagiellonian University in Krakow—Collegium Medicum, 31-121 Krakow, Poland
3
Department of Bioinformatics and Telemedicine, Jagiellonian University in Krakow—Collegium Medicum, 30-688 Krakow, Poland
4
Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Krakow, Poland
5
Department of Cybernetics and Biomedical Engineering, VSB—Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic
6
Faculty of Electrical Engineering, Opole University of Technology, 45-758 Opole, Poland
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Andrea Facchinetti
Sensors 2022, 22(1), 103; https://doi.org/10.3390/s22010103
Received: 17 November 2021 / Revised: 15 December 2021 / Accepted: 21 December 2021 / Published: 24 December 2021
(This article belongs to the Special Issue Biomedical Data in Human-Machine Interaction)
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. View Full-Text
Keywords: digital signal processing; electroencephalography (EEG); Naive Bayesian classifiers; Fetal Alcohol Spectrum Disorders (FASD) digital signal processing; electroencephalography (EEG); Naive Bayesian classifiers; Fetal Alcohol Spectrum Disorders (FASD)
Show Figures

Figure 1

MDPI and ACS Style

Dyląg, K.A.; Wieczorek, W.; Bauer, W.; Walecki, P.; Bando, B.; Martinek, R.; Kawala-Sterniuk, A. Pilot Study on Analysis of Electroencephalography Signals from Children with FASD with the Implementation of Naive Bayesian Classifiers. Sensors 2022, 22, 103. https://doi.org/10.3390/s22010103

AMA Style

Dyląg KA, Wieczorek W, Bauer W, Walecki P, Bando B, Martinek R, Kawala-Sterniuk A. Pilot Study on Analysis of Electroencephalography Signals from Children with FASD with the Implementation of Naive Bayesian Classifiers. Sensors. 2022; 22(1):103. https://doi.org/10.3390/s22010103

Chicago/Turabian Style

Dyląg, Katarzyna A., Wiktoria Wieczorek, Waldemar Bauer, Piotr Walecki, Bozena Bando, Radek Martinek, and Aleksandra Kawala-Sterniuk. 2022. "Pilot Study on Analysis of Electroencephalography Signals from Children with FASD with the Implementation of Naive Bayesian Classifiers" Sensors 22, no. 1: 103. https://doi.org/10.3390/s22010103

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop