Key result
The weighted quadratic variation reduction (WQVR) algorithm outperformed state-of-the-art methods for detrending RR series, proving to be statistically uniformly better and computationally faster.
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A novel, computationally efficient algorithm for detrending unevenly sampled RR series outperforms existing methods, making it highly suitable for long-term HRV analysis.
Detrending RR series is a common processing step prior to HRV analysis. Customarily, RR series, which are inherently unevenly sampled, are interpolated and uniformly resampled, thus introducing errors in subsequent HRV analysis. We have recently proposed a novel approach to detrending unevenly sampled series, which is based on the notion of weighted quadratic variation reduction. In this paper, we extensively assess its performance on RR series through a statistical analysis. Numerical results confirm the effectiveness of the approach, which outperforms state-of-the-art methods. Furthermore, it is statistically uniformly better than competing algorithms. A sensitivity analysis shows that it is robust to variations of its controlling parameter. The algorithm is simple and favorable in terms of computational complexity, thus being suitable for long-term HRV analysis. To the best of the authors' knowledge, it is the fastest algorithm for detrending RR series.
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Fasano et al. (2015) studied Heart rate variability (synthetic RR series) (n=300). Weighted quadratic variation reduction (WQVR) algorithm vs. Smoothness priors (SP) and smoothing by Gaussian process priors (SGP) algorithms was evaluated on Empirical distribution function of the detrending error (squared Euclidean norm of the difference between detrended and original trend-free series). The weighted quadratic variation reduction (WQVR) algorithm outperformed state-of-the-art methods for detrending RR series, proving to be statistically uniformly better and computationally faster.
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