ECG-Based Stress Surveillance Using an Attention-Driven Hybrid CNN-RNN Model
Bibliographic record
Abstract
This paper presents a novel deep learning-based approach for anticipating workplace accident risks through artificial intelligence-driven stress monitoring. Our method focuses on the analysis of physiological signals, specifically electrocardiogram (ECG) data, using the publicly available Wearable Stress and Affect Detection (WESAD) dataset. We introduce a comprehensive framework that includes feature extraction from ECG segments and utilizes the combined strengths of onedimensional convolutional neural networks (1D-CNNs) and recurrent neural networks (RNNs), particularly bidirectional long short-term memory (BiLSTM) and bidirectional gated recurrent unit (BiGRU) architectures, to capture temporal patterns in the data. Two hybrid models are proposed, both incorporating attention mechanisms that dynamically focus on the most informative parts of the input sequence. We further investigate adversarial robustness through perturbation experiments to assess model reliability under challenging conditions. Experimental results demonstrate strong performance and robustness, with the CNNBiLSTM model with attention achieving superior results. This work contributes to the development of more effective and resilient stress monitoring systems for enhancing occupational safety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".