Improved Neurocognitive Classification by Means of Deep Learning Algorithms through Electroencephalograms
Bibliographic record
Abstract
In this paper, different techniques based on deep learning algorithms used for the classification and diagnosis of patients with mental disorders i.e., bipolar disorder, are presented.To this aim, the potentials obtained from 32 unipolar electrodes of non-invasive electroencephalogram analysis are studied to obtain its main features.More specifically, the analysis performed utilizes an innovative radial basis function neural network based on fuzzy means algorithm.Furthermore, the analysis of the variance to statistical parameters and entropy is applied.In total, 105 patients with bipolar disorder and 205 control subjects have been evaluated.The results obtained shows a correct classification in patients with bipolar disorder compared to healthy controls.The proposed methods achieved a better performance than other machine learning techniques such as support vector machine or k-nearest neighbour, with an accuracy close to 96%.It can be concluded that this type of classifications will allow the training of algorithms that can be used to identify and classify different mental disorders with very high accuracy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".