Wearable Cardiovascular And Sleep Monitoring
We develop methods to assess ECG and optical pulse quality, restore noisy recordings and improve heartbeat detection. Machine learning and nonlinear analysis also support hypertension-risk assessment, sleep-apnea detection and synchronization of cardiovascular recordings. Together, these studies address practical challenges in personalized monitoring, including movement artifacts, calibration and limited generalization beyond training datasets.




