Laying the Groundwork for the Foundation Model

All of our efforts this week are geared toward one major goal: advancing our project into the foundation-model implementation phase following our successful concept validation. After a productive technical meeting with Dr. Kejun Huang and Dr. Keider Hoyos, Team BeatNet solidified our strategy to move beyond basic CNN/UNet baselines and begin building a self-supervised ECG foundation model using large-scale datasets such as PTB-XL and MIMIC-III/IV.

This week was about turning plans into concrete action—defining how contrastive pre-training, masked autoencoder learning, and distance-map regression will shape the next generation of ECG analytics for Aventusoft’s BeatNet device.

Key Accomplishments This Week

Foundation-Model Strategy Defined:
We finalized the move toward a foundation model built on large-scale ECG corpora such as PTB-XL and MIMIC-III/IV. The model will leverage contrastive pre-training, treating 5-second and 10-second ECG windows from the same patient as positive pairs, while windows from different patients act as negative pairs. This approach allows the model to learn invariant, patient-specific embeddings robust to signal variations.

Explored Alternate Self-Supervised Approaches:
We evaluated potential pre-training routes including masked autoencoder (MAE) learning and distance-map regression, which predicts landmark-wise distance functions rather than explicit waveform peaks—broadening the range of modeling techniques under consideration.

Semester 2 Modeling Pillars Finalized:
Our Semester 2 deliverables are now centered around three primary components:

1. Landmark Detection Model – multi-output regression to identify P/Q/R/S/T time-stamps.
2. Arrhythmia & Disease Classification – fine-tuning foundation embeddings for AFib, PVC, LBBB/RBBB, and conduction block detection.
3. Model Distillation for Mobile Deployment – quantization and pruning to adapt the foundation model for Aventusoft’s BeatNet single-lead (3-electrode, 500 Hz) device.

Single-Lead Adaptation Framework:
Discussions also clarified strategies for retraining 12-lead models to function effectively with single-lead input, aligning with Aventusoft’s mobile ECG system requirements.

Next Steps: Prototype Implementation

In the coming week, our focus will shift toward implementing the prototype foundation-model pipeline. We will begin with contrastive pre-training using PTB-XL segments to establish the base embedding space and develop an augmentation module capable of simulating inter-patient variability through signal inversion, noise injection, and time-warping. Next, we will visualize these learned embeddings using t-SNE to validate whether the model can effectively cluster normal and arrhythmic patterns. Simultaneously, we plan to benchmark different encoder backbones, such as ResNet and EfficientNet-1D, to identify the most efficient architecture for downstream fine-tuning. Finally, our team will document the complete workflow from pre-training to distillation for inclusion in the upcoming System Level Design Review (SLDR) and coordinate with Aventusoft regarding access to internal anonymized ECG data and available GPU resources.

This week was about transforming technical insight into implementation readiness—laying the foundation for self-supervised ECG intelligence that bridges research innovation with real-world device deployment.

See you next week!