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ARXIV:2605.12241 · MEDICAL AI · SUBMITTED 13 MAY · 21:02 UTC · FRESHNESS STALE
ARXIV:2605.12241MEDICAL AISUBMITTED 13 MAY · 21:02 UTCFRESHNESS STALEM A Al-Masud · Nils Strodthoff · arXiv
Systematically studies pretraining strategies and scaling for ECG foundation models, identifying contrastive predictive coding and structured state space models as superior for transferable representations.
Opportunity summary
Pain Systematically studies pretraining strategies and scaling for ECG foundation models, identifying contrastive predictive coding and structured state space models as superior for transferable representations.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Systematically studies pretraining strategies and scaling for ECG foundation models, identifying contrastive predictive coding and structured state space models as superior for transferable representations. This work focuses on foundation models for electrocardiography (ECG) data,…
Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the size of the pretraining dataset are rarely assessed systematically and in a like-for-like manner. This…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We hypothesize that the strong inductive biases of structured state space models, rather than pretraining scale alone, are the primary driver of effective ECG…
Medical AI moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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Systematically studies pretraining strategies and scaling for ECG foundation models, identifying contrastive predictive coding and structured state space models as superior for transferable representations.
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10.48550/arXiv.2605.12241Systematically studies pretraining strategies and scaling for ECG foundation models, identifying contrastive predictive coding and structured state space models as superior for transferable representations.
Abstract
Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the size of the pretraining dataset are rarely assessed systematically and in a like-for-like manner. This work focuses on foundation models for electrocardiography (ECG) data, one of the most widely captured physiological time series world-wide. We present a comprehensive assessment of pretraining methodologies, covering five different contrastive and non-contrastive self-supervised learning objectives for ECG foundation models, and investigate their scaling behavior with pretraining dataset sizes up to 11M input samples, exclusively from publicly available sources. Pretraining strategy has a meaningful and consistent impact on downstream performance, with contrastive predictive coding (slightly ahead of JEPA) yielding the most transferable representations across diverse clinical tasks. Scaling pretraining data continues to yield meaningful improvements up to 11M samples for most objectives. We also compare model architectures across all pretraining methodologies and find evidence for a clear superiority of structured state space models compared to transformers and CNN models. We hypothesize that the strong inductive biases of structured state space models, rather than pretraining scale alone, are the primary driver of effective ECG representation learning, with important implications for future foundation model development in this and potentially other physiological signal domains.
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Dimensions overall score 4.0
PROBLEM
Systematically studies pretraining strategies and scaling for ECG foundation models, identifying contrastive predictive coding and structured state space models as superior for transferable representations. This work focuses on foundation models for electrocardiography (ECG) dat...
METHOD
Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the size of the pretraining dataset are rarely assessed systematically and in a like-for-like manner. This work focuses on foundation mo...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We hypothesize that the strong inductive biases of structured state space models, rather than pretraining scale alone, are the primary driver of effective ECG representation learning, with important impli...
WHY NOW
Medical AI moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Systematically studies pretraining strategies and scaling for ECG foundation models, identifying contrastive predictive coding and structured state space models as superior for transferable representations. This work focuses on foundation models for electrocardiography (ECG) data, one of the most widely captured physiological time series world-wide.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the size of the pretraining dataset are rarely assessed systematically and in a like-for-like manner. This work focuses on foundation models for electrocardiography (ECG) data, one of the most widely captured physiological time series world-wide.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We hypothesize that the strong inductive biases of structured state space models, rather than pretraining scale alone, are the primary driver of effective ECG representation learning, with important implications for future foundation model development in this and potentially other physiological signal domains. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Systematically studies pretraining strategies and scaling for ECG foundation models, identifying contrastive predictive coding and structured state space models as superior for transferable representations.
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Medical AI
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4.0/10 public viability
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