Key result
The developed Refined Matrix Completion method obtained the least estimation error with minimal computation cost for HRV spectrum estimation compared to five deep learning models and the traditional matrix completion method.
Why the study?
Measurement uncertainties and artifacts can degrade signal quality and impair the evaluation of heart rate variability metrics.
The proposed refined matrix completion method provides a more accurate and computationally efficient approach for estimating missing values in heart rate variability spectra compared to traditional and deep learning methods.
Matrix completion approach may bolster HRV spectral estimates amid artifacts; leaves open clinical validation before practice adoption.
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.
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Lü et al. (2022) studied Various (healthy, arrhythmias, coronary artery disease, apnea) (n=332). Refined Matrix Completion (RMC) vs. Traditional Matrix Completion and Deep Learning models was evaluated on Normalized root mean square error (NRMSE) for missing entry estimation. The developed Refined Matrix Completion method obtained the least estimation error with minimal computation cost for HRV spectrum estimation compared to five deep learning models and the traditional matrix completion method.
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