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

Greedy-optimized Approach for Interbeat Interval and Heart Rate Variability Daily Monitoring using Wearable PPG