Graph-Enhanced Low-Resource ECG Representation Learning for Emotion Recognition Based on Wearable Internet of Things
Bibliographic record
Abstract
Internet of Things (IoT) devices like wearable devices have enabled quick monitoring of electrocardiogram (ECG) signals with lower resources than multielectrode ECG devices, opening up development opportunities for sustainable ECG-based emotion recognition. However, existing methods that rely on predesigned features extracted from single-lead ECG signals cannot automatically extract effective features from the original ECG signal collected by IoT devices. To address this limitation, we propose a novel approach leveraging signal transformation and graph representation learning for ECG-based emotion recognition. The signal graph learning process can be divided into local subgraph learning for ECG representation learning and signal enhancement graph to derive the graph-enhanced representation. We employ a designed loss function by calculating cosine similarity to extract an effective representation of the original signal from the transformed signal in the local subgraph learning. Additionally, we utilize a graph convolution model based on the signal enhancement graph to obtain a graph-enhanced representation of the ECG signal. The method incorporates six signal transformations and constructs a self-signal transformation graph. For emotion recognition, we design a classification network comprising convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. Experiments on public data sets show the superiority of our method among other baselines. Ablation studies are conducted to verify the performance.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".