Machine Learning Based Framework for Depression Diagnosis Using EEG and ECG Signals
Bibliographic record
Abstract
Diagnosing depression manually requires patience, attention to detail, and significant expertise.The current approach leverages electroencephalogram (EEG) and electrocardiogram (ECG) signals to identify and diagnose depression.The proposed work aims to develop a machine learning-based system that uses EEG and ECG data for the effective detection of depression.Feature extraction and selection strategies, separate approaches, are employed in the design of classification methodologies.Key segments of the ECG signal P, QRS, and ST are extracted as functional features.From EEG signals, the most significant features include alpha band power, entropy, standard deviation, and Hjorth activity (HA).EEG data are classified using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models, while ECG signals are analyzed using Long Short-Term Memory (LSTM) Autoencoders and Recurrent Neural Network (RNN) architectures.The performance of these classifiers in terms of accuracy, sensitivity, selectivity, and specificity is enhanced when two-dimensional sequence inputs are utilized in RNNs and LSTM Autoencoders.The current approach achieves a 93% accuracy rate for ECG signal classification, while CNN outperforms SVM in EEG signal classification with an accuracy of 97.69%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".