Time-varying assessment of heart rate variability parameters using respiratory information

Abstract : Analysis of heart rate variability (HRV) is commonly used for characterization of autonomic nervous system. As high frequency (HF, known as the respiratory-related) component of HR, overlaps with the typical low frequency (LF) band when the respiratory rate is low, a reference signal for HF variations would help in better discriminating the LF and HF components of HR. The present study proposes a model for time-varying separation of HRV components as well as estimation of HRV parameters using respiration information. An autoregressive moving average with exogenous input (ARMAX) model of HRV is considered with a parametrically modeled respiration signal as the input. The model parameters are estimated using smoothed extended Kalman filtering. Results for different synthetic data show that our proposed joint model outperforms the classical AR modeling in estimation of HRV parameters especially in the case of low respiration rate. In addition, the possibility of using pulse transit time (PTT) and the amplitude of photoplethysmogram (PPGamp) as surrogates of the input respiratory signal has been investigated. To this end, electrocardiogram (ECG), PPG and respiration have been recorded from 21 healthy subjects (10 males and 11 females, mean age 27.5 ± 4.1) during normal and deep respiration. Results show that indeed PTT and PPGamp offer good potential to be used as references for respiratory-related variations of HR, thus avoiding additional devices for recording respiration.
Type de document :
Article dans une revue
Computers in Biology and Medicine, Elsevier, 2017
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Contributeur : Vicente Zarzoso <>
Soumis le : jeudi 14 septembre 2017 - 10:49:24
Dernière modification le : vendredi 15 septembre 2017 - 01:10:09


  • HAL Id : hal-01587394, version 1



Layli Goldoozian, Edmond Zahedi, Vicente Zarzoso. Time-varying assessment of heart rate variability parameters using respiratory information. Computers in Biology and Medicine, Elsevier, 2017. <hal-01587394>



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