CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation

2026-05-21 · Source: Machine Learning · Field: Health & Wellbeing — Medical Devices & Health Technology, Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, quick

Summary

CogAdapt is a novel framework designed to adapt clinical ECG foundation models for real-time cognitive load assessment using wearable devices. This approach addresses critical challenges like limited labeled data and poor cross-subject generalization inherent in current methods. CogAdapt integrates LeadBridge, a learnable adapter that transforms 3-lead wearable signals into anatomically consistent 12-lead representations, and ProFine, a progressive fine-tuning strategy that gradually unfreezes encoder layers while preventing catastrophic forgetting. Evaluated using leave-one-subject-out cross-validation on the CLARE and CL-Drive public datasets, CogAdapt achieved macro-F1 scores of 0.626 and 0.768, respectively. These results significantly outperform baselines trained from scratch, demonstrating the potential of foundation model adaptation for subject-independent cognitive load assessment from wearable sensors.

Key takeaway

For Machine Learning Engineers developing wearable health applications and facing limited labeled data for cognitive load assessment, CogAdapt offers a robust solution. You should consider implementing its LeadBridge and ProFine strategies to adapt pre-trained clinical ECG foundation models. This approach significantly improves cross-subject generalization and assessment accuracy, enabling more effective real-time cognitive load monitoring without extensive new data collection.

Key insights

Adapting clinical ECG foundation models via lead transformation and progressive fine-tuning enables robust wearable cognitive load assessment.

Principles

• Foundation models provide rich representations.
• Sensor mismatch requires learnable adaptation.
• Progressive fine-tuning prevents forgetting.

Method

CogAdapt employs LeadBridge to transform 3-lead wearable ECGs into 12-lead representations, followed by ProFine, a progressive fine-tuning strategy, to adapt clinical foundation models for cognitive load assessment.

In practice

• Adapt 3-lead wearable data to 12-lead format.
• Utilize progressive fine-tuning for stability.
• Use clinical ECG models for new tasks.

Topics

• ECG Foundation Models
• Wearable Sensors
• Cognitive Load Assessment
• Transfer Learning
• Fine-tuning Strategies
• Lead Adaptation

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.