Schizophrenia Patient Classification with Long Short-Term Memory Analysis of Electroencephalography Signals
Bibliographic record
Abstract
Schizophrenia is a severe mental illness, the main symptoms of which include delusions, hallucinations, and cognitive disturbances.This disease can affect the quality of human life.Schizophrenia affects around 24 million people worldwide.This study involved 14 patients with paranoid schizophrenia and 14 healthy controls with 19 channels.This study aims to apply the Long Short-Term Memory (LSTM) method to Electroencephalography (EEG) signals for classifying people with schizophrenia.EEG signal analysis uses a bandpass filter with an interval frequency of 0.5 -45 Hz with a maximum EEG segment duration of 5 seconds with an overlap of 1 second.Feature extraction used is based on Frequency-Domain Features.The data is standardized with a scaler by dividing training, validation, and testing data by 80%, 15%, and 15%, with a random state 42.The dense layer uses one layer LSTM, Dropout of 0.25, and Activation ReLu and Adam optimization.Therefore, the model accuracy is 99.94%.The K-Fold Cross Validation evaluation matrix results for the validation dataset are 98.18%.From the selected model, predictions were made using data testing to obtain an evaluation matrix for the diagnosis of schizophrenia, including a precision of 95%, recall of 93%, F1-score of 94%, and accuracy of 94%.Hence, in this study, it is evident that LSTM demonstrates effectiveness in accurately classifying schizophrenia patients using their brainwave data.
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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) | 0.000 | 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".