Diagnosis of schizophrenia using an extended multivariate autoregressive model for EEGs
Bibliographic record
Abstract
Schizophrenia is a complex brain disorder that leads to an abnormal interpretation of reality. One of its reliable biological markers is the auditory evoked potential P300. The aim of the current paper is to classify healthy-control subjects from schizophrenic patients using EEG signals collected during an auditory oddball paradigm. The electroencephalogram (EEG) is modeled by a multivariate autoregressive (MVAR) model that takes into account the instantaneous causality between the EEG channels. After preprocessing, 19 channels of the recorded signals were divided into seven clusters based on their location. Next, the PCA technique was employed to obtain the first principal component inside each cluster. By imposing realistic constraints to estimate instantaneous effects between the variables, the instantaneous interactions matrix and, consequently, the extended multivariate autoregressive (eMVAR) model were estimated. Then, extended partial directed coherences (ePDCs) were extracted as connectivity features. The mRMR algorithm was utilized to reduce the feature dimension, and finally, the selected features were imported into a deep neural network for classification between healthy and schizophrenic states. The results showed that the eMVAR model outperformed the strictly causal model in classifying schizophrenic patients. With eMVAR modeling, an accuracy of 91.11% was obtained by using only four features. Furthermore, the most discriminative connectivity feature was ePDC from left posterior (LP) to (LP), and the most informative frequency band was the gamma sub-band. We have therefore presented evidence that the proposed approach enhances the characterization and diagnosis of schizophrenia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".