On the Application of Metaheuristics and Deep Wavelet Scattering Decompositions for the Prediction of Adolescent Psychosis Using EEG Brain Wave Signals

Schizophrenia is a common psychotic disorder which affects substantial amount of the population, where paranoid variant viewed as most form disorder. This psychosis has been seen to affect both adults and adolescents; in case adolescents, it increasingly challenging diagnose with traditional means involving clinical interviews. The use electroencephalography (EEG) signals proven be an effective non-invasively diagnosing brain disorders, alongside having ability mitigate any subjective bias from diagnosis process. paper explores acquired EEG signals, metaheuristics deep wavelet scattering decomposition, combination supervised unsupervised learning, for automated prediction adolescent schizophrenia. results showed best accuracy metaheuristic decomposition candidate learning methods, region 95%+ across various classification metrics, showcases enhanced Further work would now explore Long ShortTerm Memory Convolution Neural Networks investigate performances.