Multi-task model that jointly assesses signal quality and detects atrial fibrillation from wrist-worn wearable photoplethysmography (PPG), pretrained on roughly one million simulated unlabeled signals before fine-tuning on labeled wearable data. Uses a 1D CNN with separate output heads for signal quality and arrhythmia detection. Developed by Stanford's Ashley Lab.

Translates a raw PPG waveform into a full continuous arterial blood-pressure waveform, from which systolic, diastolic, and mean arterial pressure are derived. Uses a two-stage cascaded 1D convolutional network in a U-Net style, with a coarse approximation stage followed by a refinement stage. Meets BHS Grade A and AAMI accuracy standards for diastolic and mean arterial pressure. Developed at BUET.