• Conf Proc IEEE Eng Med Biol Soc · Aug 2016

    Missing RRI interpolation for HRV analysis using locally-weighted partial least squares regression.

    • Keisuke Kamata, Koichi Fujiwara, Toshiki Yamakawa, and Manabu Kano.
    • Conf Proc IEEE Eng Med Biol Soc. 2016 Aug 1; 2016: 2386-2389.

    AbstractThe 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.

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