A discriminant analysis of the P3b wave with electroencephalogram by feature‐electrode pairs in schizophrenia diagnosis - Juan I. Arribas; Luis M. San‐José‐Revuelta (IET Signal Processing, 2023)
Abstract Schizophrenia is a disease that affects approximately 1% of the population. Its early accurate diagnosis is of vital importance to apply adequate therapy as soon as possible. We present a Statistical Discriminant Diagnosing (SDD) system that discriminates between healthy controls and subjects and that supports diagnosis by a medical professional. The system works with {feature, electrode} EEG pairs which are selected based on the statistical significance of the p‐values computed over the brain P3b wave. A bank of evoked potential pre‐processed and filtered EEG signals is recorded during an auditory odd‐ball (AOD) task and serves as input to the SDD system. These EEG signals comprise 20 features and 17 electrodes, both in time (t) and frequency (f) domain. The relevance of the Parieto‐Temporal region is shown, allowing us to identify highly discriminant {feature, electrode} pairs in the detection of schizophrenia, resulting lower p‐values in both Right and Left Hemispheres, as well as in Parieto‐Temporal EEG signals. See for instance, the {PSE, P4} pair, with p‐value = 0.00003 for (parametric) t Student and p‐value = 0.00019 for (nonparametric) U Mann‐Whitney tests, both under the 15 Hz cutoff frequency of a low pass EEG preprocessing filter. The relevance of this pair is in agreement with previously published related results. The proposed SDD system may provide the human expert (psychiatrist) with an objective complimentary information to help in the early diagnosis of schizophrenia.