Heart Rate Variability Estimation with Dynamic Fine Filtering and Global-Local Context Outlier Removal

Consumer hearable technologies such as earbuds are increasingly embedding physiological sensors, including photoplethysmography (PPG) and inertial measurements. They create unique opportunities to passively monitor stress and deliver digital interventions such as music. However, PPG signals recorded from ear canals are often very noisy due to head movement and fit issues. This work proposes algorithms to estimate heart rate variability (HRV) features from noisy PPG signals recorded using earbuds. We have used template matching to determine the signal quality for dynamic fine filtering around the estimated heart rate. We have also improved the inter-beat interval (IBI) outlier detection and removal algorithm using the global-local context of the input PPG signal. The mean absolute error of estimating RMSSD decreased from 70.83 milliseconds (ms) to 24.88 ms, and SDNN decreased from 46.89 ms to 16.60 ms.