CogAdapt adapts clinical ECG foundation models to 3-lead wearable signals for cognitive load assessment via a LeadBridge adapter and ProFine progressive fine-tuning, outperforming scratch-trained models with macro-F1 of 0.626 and 0.768 on public datasets under leave-one-subject-out validation.
[Hanet al., 2024 ] Yu Han, Xiaofeng Liu, Xiang Zhang, and Cheng Ding
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ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.
citing papers explorer
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CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation
CogAdapt adapts clinical ECG foundation models to 3-lead wearable signals for cognitive load assessment via a LeadBridge adapter and ProFine progressive fine-tuning, outperforming scratch-trained models with macro-F1 of 0.626 and 0.768 on public datasets under leave-one-subject-out validation.
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ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.