Multimodal cardiac-sensing foundation model pretrained with generative masked pretraining on ECG, PPG, and paired clinical/machine-generated text reports from roughly 1.7 million individuals across three large-scale critical-care and outpatient ECG datasets. A channel-embedding scheme lets the same model accept any combination of 12-lead ECG, single-lead/wearable ECG, and PPG. The resulting embeddings transfer to diagnostic classification, demographic recognition, vital-sign measurement, clinical-outcome prediction, and ECG question answering. Pretrained weights require a signed academic-access agreement rather than an open download.

1D residual neural network that predicts a patient's age directly from a 12-lead ECG; the gap between this predicted 'ECG age' and true chronological age is used as a biomarker of cardiovascular risk and mortality. Trained on the CODE-15% Brazilian ECG dataset, with the original R² of 0.71 later reproduced (R² = 0.70) in an independent German validation cohort. Developed by researchers at Uppsala University and UFMG.