Preclinical Diagnosis of Schizophrenia using EEGTime-Frequency Analysis During Motor Function and verbal Fluency Tests
Bibliographic record
Abstract
Abstract Schizophrenia is a severe mental illness that impairs the way a person perceives reality. It causes a variety of issues related to behavior, emotions, and thinking (cognition). Patients experience auditory hallucinations, delusion, and sleep deprivation. Although the Diagnostic and Statistical Manual (DSM) IV version helps in diagnosis, the lack of clinical tools available leads to delayed diagnosis. To ease preclinical diagnosis, this study conducted two tests, namely Motor Function Test (MFT) and the verbal Fluency Test (VFT). The neural activity of the brain during these two tests is captured by an Electroencephalogram(EEG). However, EEG signals are not widely used for clinical analysis, as the signals are blurred during acquisition, random, and shortcoming of the signal processing algorithm. Hence, in this paper, we propose a Preclinical Diagnosis of Schizophrenia using Multi-SynchroSqueezing Transform (MSST) (PDS-M) to analyze the blurred EEG signal with Time-frequency analysis.PDS-M produces a sharper signal and achieves perfect signal reconstruction. The mono component of MSST in each mode is termed intermediate frequency and it is used as the feature (such as lack of emotion, hallucinations, or disorganized thinking)for discriminating schizophrenic subjects from normal ones. The results show that the MFT is better than the VFT for preclinical study and this study also suggests that MSST is more suitable for time-frequency analysis of EEG signal since all the three modes multi-components are different from normal having a low p-value of p=0, p=0, and p=0.008, respectively, where p is the probability of data having occurred under the null hypothesis of a statistical test.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".