Refined Matrix Completion for Spectrum Estimation of Heart Rate Variability

Abstract

Heart rate variability (HRV) is the reflection of physiological effects modulating heart rhythm. In particular, spectral HRV metrics provide valuable information to investigate activities of the cardiac autonomic nervous system. However, uncertainties and artifacts from measurements can reduce signal quality and therefore affect the evaluation of HRV measures. In this paper, we propose a new method for HRV spectrum estimation with measurement uncertainties using matrix completion (MC). We show that missing values of HRV spectrum can be efficiently estimated using the MC method by leveraging the low rank property of the spectrum matrix. In addition, we proposed a refined matrix completion (RMC) method to improve the estimation accuracy and computational efficiency by introducing model information for the HRV spectrum. Experimental studies on five public benchmark datasets show the effectiveness and robustness of the developed RMC method for estimating missing entries for HRV spectrum with different masking ratios. Furthermore, our developed RMC method is compared with five deep learning models and the traditional MC method; the results of this comparison study demonstrate that our developed RMC method obtains the least estimation error with the minimal computation cost, indicating the advantages of our developed method for HRV spectrum estimation.

Identification of Blue Horizontal Branch Stars with Multimodal Fusion

“…Considering the excellent results of Transformer, researchers have started investigating the use of Transformers for multimodal learning. The most significant advantage of the Transformer used for multimodal learning is its inherent strength and scalability in modeling various modalities and tasks (Xu et al 2022). In the multimodal Transformer, the interaction between the different modalities is actually achieved through its internal attention mechanism.…”