Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency
Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains due to shifts in acquisition devices and patient populations. Test-time adaptation (TTA) offers a practical solution by adapting models using only unlabeled data at inference time. However, existing TTA methods often underperform on ECG tasks, since naive online updates ignore the hierarchical beat-rhythm structure of cardiac cycles and are vulnerable to signal artifacts, which leads to unstable adaptation and model drift. We propose BeatRhythm-T
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- PossiblePossibly related (embedding) · 47%Machine Learning May Improve Arrhythmic Risk Prediction - EMJ →
- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
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- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
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- LinkedLinked via arxiv author · 85%Wenhan Jiang →
“Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency”
- LinkedLinked via arxiv author · 85%Zhipeng Deng →
“Test-Time Adaptation for ECG Classification via SQI-Gated Self-Training and Beat-Rhythm Consistency”