Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
H., Landesberg, A., & Behar, J
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PhysioLite delivers Transformer-comparable ECG/EMG performance using learnable wavelet filters and hardware-aware design at ~370KB quantized size on μNPUs.
rPPG algorithms with Lp-norm and fractional-order peak enhancement achieve 1.92 bpm MAE for pulse rate and good HRV metrics vs ECG in 29 drivers, recommending 2SR for rate and CHROM for variability with 20 superpixels.
citing papers explorer
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Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study
Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
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Towards Real-Time ECG and EMG Modeling on $\mu$NPUs
PhysioLite delivers Transformer-comparable ECG/EMG performance using learnable wavelet filters and hardware-aware design at ~370KB quantized size on μNPUs.
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A Signal Extraction Approach for Remote Heart Rate Variability Assessment Using Proxy Measure in a Driving Simulator
rPPG algorithms with Lp-norm and fractional-order peak enhancement achieve 1.92 bpm MAE for pulse rate and good HRV metrics vs ECG in 29 drivers, recommending 2SR for rate and CHROM for variability with 20 superpixels.