Automatic Detection of Schizophrenia by Applying Deep Learning over Spectrogram Images of EEG Signals
Why this work is in the frame
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Bibliographic record
Abstract
This study presents a method that aims to automatically diagnose Schizophrenia (SZ) patients by using EEG recordings. Unlike many literature studies, the proposed method does not manually extract features from EEG recordings, instead it transforms the raw EEG into 2D by using Short-time Fourier Transform (STFT) in order to have a useful representation of frequency-time features. This work is the first in the relevant literature in using 2D timefrequency features for the purpose of automatic diagnosis of SZ patients. In order to extract most useful features out of all present in the 2D space and classify samples with high accuracy, a state-of-art Convolutional Neural Network architecture, namely VGG-16, is trained. The experimental results show that the method presented in the paper is successful in the task of classifying SZ patients and healthy controls with a classification accuracy of 95% and 97% in two datasets of different age groups. With this performance, the proposed method outperforms most of the literature methods. The experiments of the study also reveal that there is a relationship between frequency components of an EEG recording and the SZ disease. Moreover, Grad-CAM images presented in the paper clearly show that mid-level frequency components matter more while discriminating a SZ patient from a healthy control.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it