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openai.com·14h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
SafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification
TL;DR
Electrocardiogram (ECG) classification models face challenges due to a lack of labeled data, especially when encountering out-of-distribution (OOD) anomalies. SafeECGMatch is a calibration-aware semi-supervised learning (SSL) method designed to improve predictions by avoiding incorrect pseudo-labels for unseen classes.
✦ Why It Matters
Engineers can implement SafeECGMatch to enhance ECG classification accuracy in clinical applications with limited labeled data.
Key Takeaways
- Utilize SafeECGMatch for improved ECG classification accuracy.
- Incorporate adaptive label smoothing to enhance model calibration.
- Leverage dual-branch architecture for better feature extraction.
- Evaluate performance on PTB-XL and PhysioNet datasets.
How It Works
SafeECGMatch employs a dual-branch architecture that extracts time-frequency representations from ECG signals. It uses adaptive label smoothing and temperature scaling to ensure that the model's confidence levels are aligned with empirical accuracy, effectively calibrating the classifier and the OOD detector.