Robust heart rate variability analysis by generalized entropy minimization

Davide La Vecchia, Lorenzo Camponovo and Davide Ferrari

Computational Statistics & Data Analysis, 2015, vol. 82, issue C, 137-151

Abstract: Typical heart rate variability (HRV) times series are cluttered with outliers generated by measurement errors, artifacts and ectopic beats. Robust estimation is an important tool in HRV analysis, since it allows clinicians to detect arrhythmia and other anomalous patterns by reducing the impact of outliers. A robust estimator for a flexible class of time series models is proposed and its empirical performance in the context of HRV data analysis is studied. The methodology entails the minimization of a pseudo-likelihood criterion function based on a generalized measure of information. The resulting estimating functions are typically re-descending, which enable reliable detection of anomalous HRV patterns and stable estimates in the presence of outliers. The infinitesimal robustness and the stability properties of the new method are illustrated through numerical simulations and two case studies from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital data, an important benchmark data set in HRV analysis.

Keywords: Heart rate variability; Time series analysis; Influence function; M-estimators; Robust estimation (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:82:y:2015:i:c:p:137-151

DOI: 10.1016/j.csda.2014.09.001

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