ECG-biometrics-bench standardizes evaluation to expose the Random Split Fallacy, where intra-session splits inflate ECG biometric performance, revealing temporal drift degradation that is not model-specific and can be partially mitigated by multi-session template fusion.
Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals
6 Pith papers cite this work. Polarity classification is still indexing.
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CardioMix uses cardiac pattern-guided bidirectional fusion to mix labeled and unlabeled ECG data for better semi-supervised segmentation while keeping samples physiologically valid.
MedMamba introduces a principle-guided bidirectional multi-scale Mamba model that outperforms prior methods on EEG, ECG, and activity classification benchmarks while delivering 4.6x inference speedup.
Three lightweight CNN architectures for ECG interpretation achieve competitive performance with reduced computational cost across multiple public datasets and tasks.
MTEEG uses task-specific LoRA modules to jointly adapt a pre-trained EEG model across multiple tasks, outperforming single-task baselines on most metrics in evaluations on six downstream tasks.
Lightweight federated learning with frozen embeddings and MLP heads reaches competitive micro and macro F1 scores for ICD-9 and ICD-10 coding on MIMIC-IV, nearly matching centralized training.
citing papers explorer
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ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics
ECG-biometrics-bench standardizes evaluation to expose the Random Split Fallacy, where intra-session splits inflate ECG biometric performance, revealing temporal drift degradation that is not model-specific and can be partially mitigated by multi-session template fusion.
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Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation
CardioMix uses cardiac pattern-guided bidirectional fusion to mix labeled and unlabeled ECG data for better semi-supervised segmentation while keeping samples physiologically valid.
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MedMamba: Recasting Mamba for Medical Time Series Classification
MedMamba introduces a principle-guided bidirectional multi-scale Mamba model that outperforms prior methods on EEG, ECG, and activity classification benchmarks while delivering 4.6x inference speedup.
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Efficient Neural Architectures for Real-Time ECG Interpretation on Limited Hardware
Three lightweight CNN architectures for ECG interpretation achieve competitive performance with reduced computational cost across multiple public datasets and tasks.
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Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation
MTEEG uses task-specific LoRA modules to jointly adapt a pre-trained EEG model across multiple tasks, outperforming single-task baselines on most metrics in evaluations on six downstream tasks.
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Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
Lightweight federated learning with frozen embeddings and MLP heads reaches competitive micro and macro F1 scores for ICD-9 and ICD-10 coding on MIMIC-IV, nearly matching centralized training.