A foundation model learns from synchronized ECG and PCG data, cutting labeling effort while supporting detection of atrial fibrillation, murmur, and pulmonary hypertension.
Acoustic and ultrasonic sensing are combined to automate heart, blood pressure, and oxygen measurements in noisy point-of-care settings.
Pretraining on synchronized ECG and PCG data cuts labeling burden while preserving accurate cardiovascular condition detection.
Synchronized ECG and PCG pretraining reduces labeled data needs while improving reliable non-invasive detection of cardiovascular conditions.
Cloud offloading cuts CPU load and heat in a wireless patient monitor, then switches to local processing when network criteria are met.
A skin-adhered conductive sensor captures ECG and PCG signals at once, removing separate electrodes and manual stethoscope pressing.