Wavelet Based and Statistical EEG Analysis in Patients with Schizophrenia
Bibliographic record
Abstract
Schizophrenia, which is considered a serious mental disorder and a psychological illness, is quite common in society today. Schizophrenia manifests itself with disordered thought development, hallucinations, and different behaviours and reactions. In this study, EEG data were collected and analyzed on a total of 84 subjects diagnosed with normal and schizophrenia. EEG data used as a diagnostic tool with low-resolution level has been used to distinguish between schizophrenic and normal individuals. First of all, statistical methods were used in the analyzes, and also frequency properties of the data were extracted by Wavelet analysis. As a result of the analysis, statistical findings include characteristics that distinguish between diagnosed schizophrenia and normal individuals. In addition, the findings obtained as a result of the Wavelet analysis were determined to distinguish between normal and schizophrenic individuals. While the mean used in statistical analysis takes the value 1.6 for normal individuals, it takes the value 2.9 for individuals diagnosed with schizophrenia. Also, in the results of Continuous Wavelet (CW) Analysis, very important findings were obtained in terms of detection in scale 16 and 64 bands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".