{"id":1017,"date":"2025-10-24T17:56:16","date_gmt":"2025-10-24T17:56:16","guid":{"rendered":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/?p=1017"},"modified":"2025-11-15T00:04:14","modified_gmt":"2025-11-15T00:04:14","slug":"week-9","status":"publish","type":"post","link":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/2025\/10\/24\/week-9\/","title":{"rendered":"Week 9"},"content":{"rendered":"\n
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Screenshot<\/figcaption><\/figure>\n\n\n\n

<\/h3>\n\n\n\n

Laying the Groundwork for the Foundation Model<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

This week was about turning plans into concrete action\u2014defining how contrastive pre-training, masked autoencoder learning, and distance-map regression will shape the next generation of ECG analytics for Aventusoft\u2019s BeatNet device.<\/p>\n\n\n\n

Key Accomplishments This Week<\/h2>\n\n\n\n

Foundation-Model Strategy Defined:<\/strong>
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<\/em>, while windows from different patients act as negative pairs<\/em>. This approach allows the model to learn invariant, patient-specific embeddings robust to signal variations.<\/p>\n\n\n\n

Explored Alternate Self-Supervised Approaches:<\/strong>
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\u2014broadening the range of modeling techniques under consideration.<\/p>\n\n\n\n

Semester 2 Modeling Pillars Finalized:<\/strong>
Our Semester 2 deliverables are now centered around three primary components:<\/p>\n\n\n\n

    \n
  1. Landmark Detection Model<\/strong> \u2013 multi-output regression to identify P\/Q\/R\/S\/T time-stamps.<\/li>\n\n\n\n
  2. Arrhythmia & Disease Classification<\/strong> \u2013 fine-tuning foundation embeddings for AFib, PVC, LBBB\/RBBB, and conduction block detection.<\/li>\n\n\n\n
  3. Model Distillation for Mobile Deployment<\/strong> \u2013 quantization and pruning to adapt the foundation model for Aventusoft\u2019s BeatNet single-lead (3-electrode, 500 Hz) device.<\/li>\n<\/ol>\n\n\n\n

    Single-Lead Adaptation Framework:<\/strong>
    Discussions also clarified strategies for retraining 12-lead models to function effectively with single-lead input, aligning with Aventusoft\u2019s mobile ECG system requirements.<\/p>\n\n\n\n

    <\/a>Next Steps: Prototype Implementation<\/h2>\n\n\n\n

    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.<\/p>\n\n\n\n

    This week was about transforming technical insight into implementation readiness\u2014laying the foundation for self-supervised ECG intelligence that bridges research innovation with real-world device deployment.<\/p>\n\n\n\n

    See you next week!<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

    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… Read More<\/a><\/p>\n","protected":false},"author":849,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[10],"tags":[],"class_list":["post-1017","post","type-post","status-publish","format-standard","hentry","category-fallsemester"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/posts\/1017"}],"collection":[{"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/users\/849"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/comments?post=1017"}],"version-history":[{"count":4,"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/posts\/1017\/revisions"}],"predecessor-version":[{"id":1081,"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/posts\/1017\/revisions\/1081"}],"wp:attachment":[{"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/media?parent=1017"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/categories?post=1017"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ippd.ufl.edu\/blogs\/ay2526team03\/wp-json\/wp\/v2\/tags?post=1017"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}