Contrastive Self-Supervised Learning for Stress Detection from ECG Data
Bibliographic record
Abstract
In recent literature, ECG-based stress assessment has become popular due to its proven correlation to stress and increased accessibility of ECG data through commodity hardware. However, most ECG-based stress assessment models use supervised learning, relying on manually-annotated data. Limited research is done in the area of self-supervised learning (SSL) approaches that leverage unlabelled data and none that utilize contrastive SSL. However, with the dominance of contrastive SSL in domains such as computer vision, it is essential to see if the same excellence in performance can be obtained on an ECG-based stress assessment dataset. In this paper, we propose a contrastive SSL model for stress assessment using ECG signals based on the SimCLR framework. We test our model on two ECG-based stress assessment datasets. We show that our proposed solution results in a 9% improvement in accuracy on the WESAD dataset and 3.7% on the RML dataset when compared with SOTA ECG-based SSL models for stress assessment. The development of more accurate stress assessment models, particularly those that employ non-invasive data such as ECG for assessment, leads to developments in wearable technology and the creation of better health monitoring applications in areas such as stress management and relaxation therapy.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".