Automatic Detection of Schizophrenia by Applying Deep Learning over Spectrogram Images of EEG Signals
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".