Authors:
Samuel Cardoso
1
;
2
;
Juliano Buss
2
;
Javier Gomez
2
;
Helida Santos
3
;
Giancarlo Lucca
4
;
Adenauer Yamin
2
and
Renata Reiser
2
Affiliations:
1
Federal Institute of Education Science and Technology Sul-rio-grandense (IFSul), Av. Paul Harris, 97574-360, Brazil
;
2
Federal University of Pelotas (UFPel), R. Gomes Carneiro, 96010-610, Brazil
;
3
Center for Computational Sciences (C3), Federal University of Rio Grande (FURG), Av. Itália, km 8, 96203-900, Brazil
;
4
CCST, Catholic University of Pelotas (UCPel), R. Gonc ¸alves Chaves, 96015-560, Brazil
Keyword(s):
Fuzzy Logic, Machine Learning, Neonatal EEG, Hypoxic-Ischemic Encephalopathy, Seizure Detection, Neonatal Neurological Monitoring, Biomarkers for Neonatal Brain Injury, Systematic Review.
Abstract:
Machine learning has advanced in healthcare, aiding diagnostics, treatment, and monitoring. In neonatal health, it helps to classify and predict conditions such as hypoxic-ischemic encephalopathy, which requires early detection. Thus, EEG pattern analysis is key in improving the neonatal prognosis. In this work, we present a systematic review of the literature to identify strategies currently employed to classify and predict neonatal EEG patterns using fuzzy logic. Fuzzy logic is particularly valuable for handling uncertainties in biological signals and improving interpretability. Five studies were selected and analyzed, focusing on applying fuzzy systems to detect epileptic events. The reviewed studies highlight techniques involving EEG data, emphasizing the role of fuzzy logic in advancing the understanding and management of neonatal neurological conditions, contributing to the state of the art in this critical field.