Robust Heart Rate Variability and Interbeat Interval Detection Algorithm in the Presence of Motion Artifacts

This paper proposes a novel method to estimate heart rate variability (HRV) and interbeat interval (IBI) in the presence of motion artifacts. A robust heartbeat selection method is introduced as a shortest path exploration problem to detect HRV/IBI from a noisy signal. The time-continuity of heartbeats is used to model the shortest path. The end point of one heartbeat is the starting point of the next one. A graph is constructed whose vertices and edges are assumed to be the beginning of each possible heartbeats and the IBIs, respectively. Weights are assigned to the edges by using average heart rate (HR) values from state-of-the-art algorithms. The results indicate that IBI percentage error is around 3% for distorted ECG signal even with 0dB SNR. In addition, HRV parameters of clean versus noisy signals show high correlation.