  Continuous monitoring of heart rate variability (HRV) provides insights in
cardiovascular health. Wearable Photoplethysmography (PPG) assures convenient
measurement of HRV. PPG, however, is susceptible to motion artifacts,
considerably deteriorating the accuracy in estimation. In this study, a
greedy-optimized approach is proposed for attaining high accuracy of interbeat
intervals (IBIs) estimation from PPG signals collected during intensive daily
activities. Utilizing the fact of continuity in heartbeats, the IBI estimation
is converted into the shortest path problem in a directed acyclic graph, where
candidate heartbeats from motion-contaminated PPG are regarded as vertices. The
approach exploits a convex penalty function to optimize weight assignment in
the shortest path calculation and a greedy fusion method to strengthen the
selection process of optimal IBIs. Results achieve correlation of 0.96 for IBI
estimation with the improvement of 58.4% in percentage error on the
single-channel PPG signal from 2015 IEEE Signal Processing Cup. It also
achieves correlation of 0.98 with percentage error of 2.2% in the two-channel
PPG signal. Estimated and true HRV parameters are highly correlated. The
approach is further validated on the PPG-DaLiA dataset with high correlation
and low percentage error for IBI and HRV estimation in two daily activities,
indicating the robustness of the proposed technique.
