Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study

Abstract

Background: Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues. Aims: To sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording. Settings and Design: Data collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute. Materials and Methods: Data of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight priori regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification. Statistical Analysis: Mann–Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications. Results: Accuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances. Conclusions: SVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the “validity” and reducing the “heterogeneity” in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.

EEG microstate features for schizophrenia classification

“…Considering research reproducibility, we utilized all but some of the epochs, which consist of the remaining epochs after the epochs containing artefact were removed in the preprocessing step. Next, we applied feature selection methods to conventional EEG features introduced in a previous study [7][8][9][10][11][12][13][14][15][16][17][18][19][20] to compare classification performances. The method would prevent classification performance of conventional EEG features from being underestimated in multivariate analysis.…”

“…Johannesen et al used frequency domain features in the working memory task to differentiate between patients with schizophrenia and healthy controls [12]. Schizophrenia classification was also performed using statistical features including mean, skewness, and kurtosis [13,14]. It has been reported that the statistical descriptor of the variability within the EEG signal, the entropy measure and the fractal dimension related with it, are useful for schizophrenia classification [15][16][17][18].…”

Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review)

“…clustering coefficient in the right temporal region (T6) in the range of EEG theta rhythms (from 4 to 8 Hz) and concentration of Ruminococcus in microbiota; degree centrality in the range of beta-2 rhythms (from 20 to 30 Hz) in the right frontal area (Fp2) and white blood cell count; degree centrality in the range of beta-2 rhythms (from 20 to 30 Hz) in the right occipital area (O2) and neutrophil to lymphocyte ratio. Tikka et al [102] used high-resolution EEG (256 channels) isolating from it other features: maximum and minimum amplitude of spectrum power peaks in delta (0−4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32), and gamma (32-64 Hz) ranges, and performing wavelet analysis in these rhythm ranges. The following regions of interest were selected: projection on the cortex of the left inferior frontal gyrus, dorsolateral prefrontal cortex (DLPFC) of the inferior parietal lobe (IPL), and superior temporal gyrus (STG).…”

Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects

“…Within this context, there have also been recently developed artificial intelligence (AI)- and machine learning (ML)-based approaches, which promise an interesting implementation of statistical tools to build more accurate and precise predictive models of schizophrenia onset, illness course, and potential therapeutic outcomes [ 28 ]. These can also identify candidate variables that are putative to be characteristics of schizophrenia spectrum disorders, by allowing a personalized diagnosis, such as a set of resting-state electroencephalographic (EEG) quantitative features, and magnetic resonance imaging of structural and functional anomalies, and so forth [ 29 , 30 , 31 ].…”