title: Missing RRI interpolation for HRV analysis using locally-weighted partial least squares regression.
abstract: The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV). Since HRV reflects autonomic nervous function, HRV-based health monitoring services, such as stress estimation, drowsy driving detection, and epileptic seizure prediction, have been proposed. In these HRV-based health monitoring services, precise R wave detection from ECG is required; however, R waves cannot always be detected due to ECG artifacts. Missing RRI data should be interpolated appropriately for HRV analysis. The present work proposes a missing RRI interpolation method by utilizing using just-in-time (JIT) modeling. The proposed method adopts locally weighted partial least squares (LW-PLS) for RRI interpolation, which is a well-known JIT modeling method used in the filed of process control. The usefulness of the proposed method was demonstrated through a case study of real RRI data collected from healthy persons. The proposed JIT-based interpolation method could improve the interpolation accuracy in comparison with a static interpolation method.
authors.forename: Keisuke
authors.initials: K
authors.lastname: Kamata
authors.forename: Koichi
authors.initials: K
authors.lastname: Fujiwara
authors.forename: Toshiki
authors.initials: T
authors.lastname: Yamakawa
authors.forename: Manabu
authors.initials: M
authors.lastname: Kano
mesh_terms: D004562:Electrocardiography; D004827:Epilepsy; D006339:Heart Rate; D006801:Humans; D016018:Least-Squares Analysis; D008962:Models, Theoretical