Optimizing Probability Threshold of Convolution Neural Network to Improve HRV-based Acute Stress Detection Performance
Bibliographic record
Abstract
As stress is linked to numerous emotional and physical conditions, its timely detection and proper management is important for our health. Convolution neural network (CNN) has been shown to be promising in stress detection because it could automatically capture the discriminant information regarding physiological change from heart rate variability (HRV), usually derived from electrocardiogram (ECG) signals. This study proposed a two-step training method to improve the acute stress detection performance through optimizing the probability threshold of a CNN. The results showed that the average error rate was significantly reduced from 17.3 ± 9.2% to 9.2 ± 5.7% after probability threshold optimization, and the classification results were more balanced between stress and rest data. This study presented a simple method to improve stress detection performance using CNN without additional data, rendering benefits for the practical application of HRV-based stress measurement.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".