Refining Stability and Clinical Plausibility: Validating the Single-Lead ECG-FM
This week marked a transition from initial adaptation to rigorous validation and system stabilization. Our focus shifted toward evaluating the fine-tuned ECG foundation model on real-world single-lead inputs and enforcing physiological constraints to ensure our outputs meet clinical standards.+2
Rather than just proving feasibility, we are now hardening the system—moving from experimental code to a reproducible, high-performance diagnostic pipeline.+1
Key Accomplishments This Week
- Validated High-Performance Single-Lead Adaptation We evaluated the fine-tuned ECG-FM on duplicated single-lead inputs using real-world samples provided by our sponsor. The model achieved a strong AUROC of approximately 0.96, demonstrating that single-lead adaptation can reach performance levels comparable to dual-lead baselines.
- Optimized Temporal Aggregation for Prediction Stability By comparing 10-second and 30-second inference windows, we observed that longer temporal aggregation significantly improves stability. This strategy reduces inconsistent arrhythmia predictions, providing a more reliable output for clinical review.
- Advanced PQRST Delineation with Physiological Constraints We improved our post-processing by enforcing rules such as temporal ordering and minimum inter-peak intervals. By clustering nearby candidate peaks, we successfully reduced duplicate detections and began addressing over-generation issues in T-wave localization.+1
- Refined Clinical Plausibility and Thresholding In collaboration with sponsor feedback, we analyzed multi-label outputs—including PVC, tachycardia, and bundle branch blocks—to assess their clinical plausibility. We investigated thresholding strategies to suppress low-confidence or physiologically impossible diagnoses in a deployment setting.+1
- Initiated Model Interpretability Research We began exploring saliency and attribution maps to localize PVC-related regions. This work supports the debugging of both classification and fiducial detection, ensuring the model is looking at the correct features for its predictions.
Next Steps: Toward a Deployment-Ready System
In the coming week, we will:
- Benchmark window aggregation (10s vs. 30s) alongside tuned confidence thresholds to quantify the trade-offs between system responsiveness and prediction stability.
- Refine T-wave localization by further adjusting temporal windows and confidence filtering within the post-processing pipeline.
- Finalize patient-level data splits and document preprocessing workflows to support future regulatory-oriented documentation.
- Package the fine-tuning pipeline and inference scripts into a clean, reproducible repository for team sharing and review.
- Draft a structured technical report detailing the single-lead adaptation approach, experimental setup, and key performance results.
With our core performance validated and physiological constraints in place, we are moving closer to a cohesive, deployment-oriented system.
See you next week!