Blood Volume Pulse Signal Extraction based on Spatio-Temporal Low-Rank Approximation for Heart Rate Estimation

We propose a novel blood volume pulse (BVP) signal extraction method for heart rate estimation that incorporates the self-similarity properties of BVP in the spatial and temporal domains. The main novelty of the proposed method is the incorporation of the temporal self-similarity of BVP via low-rank approximation in the time-delay coordinate system for BVP signal extraction. To make a low-rank approximation of BVP in the time domain, we introduce knowledge of linear time-invariant systems, i.e., the autoregressive (AR) model lies in the low-rank subspace in the time-delay coordinate system. In the medical field, it is widely known that BVP has quasi-periodic temporal characteristics owing to the cardiac pulse and exhibits self-similarity properties in the temporal domain. Hence, we model the temporal behavior of BVP as an AR process, allowing for a low-rank approximation of BVP in the time-delay coordinate system. Low-rank approximation of BVP in the time and spatial domains enables reliable BVP signal extraction, resulting in accurate heart rate estimation. The experiments demonstrate the effectiveness of the proposed method.