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arxiv: 2605.22774 · v1 · pith:GNKPT7CInew · submitted 2026-05-21 · 💻 cs.LG · cs.AI· cs.HC

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

Amir Mousavi , Mohammad Sadegh Sirjani , Erfan Nourbakhsh , Mimi Xie , Rocky Slavin , Leslie Neely , John Davis , John Quarles This is my paper

Pith reviewed 2026-05-22 06:35 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.HC
keywords cognitive load assessmentECG foundation modelswearable sensorslead adaptationtransfer learningprogressive fine-tuningsubject-independent evaluation
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The pith

A learnable adapter lets clinical ECG foundation models assess cognitive load from 3-lead wearables by converting signals to 12-lead form.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to overcome scarce labeled data and weak cross-subject generalization in real-time cognitive load detection from wearables. It does so by transferring representations from large clinical ECG foundation models using a new framework called CogAdapt. The key pieces are LeadBridge, which turns 3-lead wearable recordings into anatomically consistent 12-lead versions, and ProFine, which gradually unfreezes layers during fine-tuning to limit forgetting. Tested under leave-one-subject-out validation on the CLARE and CL-Drive datasets, the adapted models reach macro-F1 scores of 0.626 and 0.768 and beat models trained from scratch. A reader would care because the approach could make subject-independent cognitive load monitoring practical on everyday wearable hardware.

Core claim

CogAdapt transfers clinical ECG foundation models to wearable cognitive load assessment by using LeadBridge, a learnable adapter that transforms 3-lead wearable ECG signals into anatomically consistent 12-lead representations, and ProFine, a progressive fine-tuning strategy that gradually unfreezes encoder layers while preventing catastrophic forgetting. On the CLARE and CL-Drive datasets under leave-one-subject-out cross-validation, CogAdapt achieves macro-F1 scores of 0.626 and 0.768 and substantially outperforms baselines trained from scratch.

What carries the argument

LeadBridge, a learnable adapter that transforms 3-lead wearable signals into 12-lead representations, paired with ProFine progressive fine-tuning to avoid forgetting.

If this is right

  • Real-time cognitive load assessment becomes possible on consumer wearables without collecting millions of new labeled recordings.
  • Subject-independent performance improves because the clinical pre-training supplies robust features that survive the lead transformation.
  • Progressive unfreezing during fine-tuning lets the model retain clinical knowledge while learning the new task.
  • The same adapter-plus-progressive-tuning pattern could be applied to other sensor mismatches in physiological signal processing.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The technique might generalize to other wearable signals such as PPG or EEG for detecting related states like fatigue or attention.
  • Consumer devices could incorporate the adapted model to adjust interfaces dynamically during driving or office work.
  • Future tests on larger and more diverse wearable datasets would show whether the performance gap over scratch training holds outside the two evaluated collections.

Load-bearing premise

The load-bearing premise is that a learnable adapter can turn 3-lead wearable ECG signals into 12-lead versions that still contain the features needed to tell cognitive load levels apart.

What would settle it

If a model trained from scratch on the same wearable data matches or exceeds the adapted model's macro-F1 scores on the CLARE or CL-Drive datasets under the same leave-one-subject-out protocol, the benefit of the clinical foundation model transfer would be refuted.

Figures

Figures reproduced from arXiv: 2605.22774 by Amir Mousavi, Erfan Nourbakhsh, John Davis, John Quarles, Leslie Neely, Mimi Xie, Mohammad Sadegh Sirjani, Rocky Slavin.

Figure 1
Figure 1. Figure 1: The core challenge: Pre-trained foundation models expect [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: CogAdapt architecture bridging wearable 3-lead ECG to clinical 12-lead foundation models. The framework consists of data [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Progressive fine-tuning scenarios. Scenario A (left) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: ECG lead reconstruction on PTB-XL. Reconstructions of [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Plasticity–stability trade-off for CLARE dataset across [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Plasticity–stability trade-off for CL-DRIVE dataset across [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Per-subject macro F1-score across all training stages under LOSO cross-validation on the CLARE dataset [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Per-subject accuracy across all training stages under LOSO cross-validation on the CLARE dataset. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Performance distribution comparison on CLARE [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Performance distribution comparison on CL-Drive [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: CLARE dataset K-fold cross-validation performance across 10 folds. Line charts show macro F1-score (left), accuracy (center), [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: CL-Drive dataset K-fold cross-validation performance across 10 folds. Similar visualization to Figure 11, showing consistently [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
read the original abstract

Real-time cognitive load assessment is essential for adaptive human-computer interaction but remains challenging due to limited labeled data and poor cross-subject generalization. Recent ECG foundation models pre-trained on millions of clinical recordings offer rich representations, but cannot be directly applied to wearable devices due to sensor configuration mismatch and task differences. In this paper, we propose CogAdapt, a framework that adapts clinical ECG foundation models to wearable cognitive load assessment. CogAdapt introduces 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. Evaluations on two public datasets (CLARE and CL-Drive) under leave-one-subject-out cross-validation show that CogAdapt substantially outperforms baselines trained from scratch, achieving macro-F1 scores of 0.626 and 0.768. These results demonstrate the promise of foundation model adaptation for subject-independent cognitive load assessment from wearable sensors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes CogAdapt, a framework for adapting clinical ECG foundation models to wearable cognitive load assessment. It introduces LeadBridge, a learnable adapter that maps 3-lead wearable ECG signals to anatomically consistent 12-lead representations, and ProFine, a progressive fine-tuning strategy that gradually unfreezes encoder layers. On the public CLARE and CL-Drive datasets, CogAdapt achieves macro-F1 scores of 0.626 and 0.768 respectively under leave-one-subject-out cross-validation, substantially outperforming baselines trained from scratch.

Significance. If the central claim holds, the work would demonstrate a practical route for transferring large-scale clinical ECG foundation models to wearable sensor configurations despite lead mismatch and task shift. The use of public datasets with leave-one-subject-out validation provides a reproducible empirical foundation for subject-independent cognitive load assessment, which could benefit adaptive human-computer interaction systems. However, the absence of mechanistic validation for the adapter limits the current significance.

major comments (2)
  1. The manuscript reports clear performance gains (macro-F1 0.626 on CLARE, 0.768 on CL-Drive under LOSO) but provides no description of the LeadBridge adapter architecture, the loss functions employed during adaptation, hyperparameter choices, or any statistical significance testing of the improvements over baselines. These omissions are load-bearing because they prevent independent verification of whether the reported gains are robust or reproducible.
  2. No waveform-level fidelity metrics, feature-space alignment measures between original and adapted leads, or ablation experiments that isolate LeadBridge from the ProFine progressive unfreezing schedule are reported. This directly affects the central claim that the adapter produces representations preserving cognitive-load-relevant features (subtle HRV and morphological patterns), as the gains could arise from fine-tuning alone rather than successful lead adaptation.
minor comments (1)
  1. The abstract and results sections would benefit from explicit statements on the number of subjects, class distribution, and exact baseline implementations to strengthen reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We have carefully addressed each major comment below, providing clarifications and indicating the specific revisions made to improve the paper's reproducibility and validation of the central claims.

read point-by-point responses

  1. Referee: The manuscript reports clear performance gains (macro-F1 0.626 on CLARE, 0.768 on CL-Drive under LOSO) but provides no description of the LeadBridge adapter architecture, the loss functions employed during adaptation, hyperparameter choices, or any statistical significance testing of the improvements over baselines. These omissions are load-bearing because they prevent independent verification of whether the reported gains are robust or reproducible.

    Authors: We agree that these implementation details are essential for reproducibility and were insufficiently elaborated in the original submission. In the revised manuscript, we have expanded Section 3.2 to provide a complete architectural specification of LeadBridge, including its convolutional and attention-based layers, input/output dimensions, and parameter counts. We have added the exact loss functions (cross-entropy for classification combined with an L2 reconstruction term for lead adaptation), a comprehensive hyperparameter table (learning rates, batch sizes, unfreezing schedules), and statistical significance testing via paired t-tests with p-values reported for all baseline comparisons in the results tables. revision: yes

  2. Referee: No waveform-level fidelity metrics, feature-space alignment measures between original and adapted leads, or ablation experiments that isolate LeadBridge from the ProFine progressive unfreezing schedule are reported. This directly affects the central claim that the adapter produces representations preserving cognitive-load-relevant features (subtle HRV and morphological patterns), as the gains could arise from fine-tuning alone rather than successful lead adaptation.

    Authors: We acknowledge that the original manuscript lacked these targeted analyses, which would more directly support the role of LeadBridge in feature preservation. To address this, the revised version includes new experiments: waveform fidelity is now quantified via MSE and Pearson correlation on reconstructed leads; feature-space alignment uses cosine similarity and CCA between embeddings from the adapted and original clinical leads. We have also added ablation studies (reported in a new table) comparing full CogAdapt against variants without LeadBridge and with fixed vs. progressive unfreezing, demonstrating that the adapter contributes gains beyond ProFine alone while preserving HRV-related patterns. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical results on external datasets

full rationale

The paper introduces CogAdapt with a learnable LeadBridge adapter and ProFine progressive fine-tuning to transfer clinical ECG foundation models to wearable cognitive load tasks. All reported performance numbers (macro-F1 0.626 on CLARE, 0.768 on CL-Drive under LOSO) are measured outcomes from direct evaluation on two independent public datasets against scratch-trained baselines. No equation, adapter definition, or fine-tuning schedule reduces by construction to its own fitted inputs or to a self-citation chain; the derivation chain consists of architectural choices whose validity is tested externally rather than assumed.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The approach rests on the untested premise that lead transformation preserves task-relevant information and that progressive unfreezing avoids catastrophic forgetting in this domain. No numerical free parameters are stated in the abstract. Two new modules are introduced without external validation.

axioms (1)
  • domain assumption Clinical ECG foundation models contain transferable representations for cognitive load when input format mismatch is corrected.
    Invoked by the decision to adapt rather than train from scratch.
invented entities (2)
  • LeadBridge no independent evidence
    purpose: Learnable adapter that maps 3-lead wearable ECG to 12-lead format
    New module introduced to solve sensor mismatch; no independent evidence provided in abstract.
  • ProFine no independent evidence
    purpose: Progressive fine-tuning strategy that gradually unfreezes encoder layers
    New training procedure to prevent forgetting; no independent evidence provided in abstract.

pith-pipeline@v0.9.0 · 5730 in / 1433 out tokens · 41255 ms · 2026-05-22T06:35:33.910274+00:00 · methodology

discussion (0)

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    work page 2048