Classification of First-Episode Psychosis with EEG Signals: ciSSA and Machine Learning Approach.

ABSTRACT: First-episode psychosis (FEP) typically marks the onset of severe psychiatric disorders and represents a critical period in the field of mental health. The early diagnosis of this condition is essential for timely intervention and improved clinical outcomes. In this study, the classification of FEP was investigated using the analysis of electroencephalography (EEG) signals and circulant spectrum analysis (ciSSA) sub-band signals. FEP poses a significant diagnostic challenge in the realm of mental health, and it is aimed at introducing a novel and effective approach for early diagnosis. To achieve this, the LASSO method was utilized to select the most significant features derived from entropy, frequency, and statistical-based characteristics obtained from ciSSA sub-band signals, as well as

SUBMITTER: Gengec Benli S 

PROVIDER: S-EPMC10741114 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature