Modeling of the Cardiovascular System using Breathing Stimuli for Evaluation of the Autonomic Nervous System

Goldouzian, Layli Sadat | 2017

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 50193 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Zahedi, Edmond; Jehed, Mehran; Zarzoso, Vicente
  7. Abstract:
  8. Autonomic nervous system (ANS) controls involuntary activities of the body, e.g. heartbeat function and blood circulation. Common tests used for evaluation of the functionality of ANS system in regulation of the cardiovascular system include heart rate variability (HRV) analysis and studying the changes in heart rate or blood pressure during deep respiration, sit-to-stand or head-up tilt table test and Valsalva maneuver. Test parameters affect the amplitude of the variations of the cardiovascular signals. Therefore, in the recent years, analysis and modeling of the interaction between the relevant signals, e.g. heart rate, blood pressure and respiration, have been widely used for the characterization of ANS. ANS plays an important role in the respiration-related variations of the heart rate (HR). This work specifically focuses on modeling and discriminating the respiratory related variations of the cardiovascular signals, especially heart rate. The study is performed in two parallel phases. In the first phase, the effects of respiration on the cardiovascular system have been modelled considering physiological knowledge. Models consisting of electrical elements have been used for the cardiovascular system. The respiratory effects including both mechanical and neural effects and also baroreflex control on the cardiovascular system have been applied. The effect of changing different respiratory parameters on the variations of the heart rate and some other cardiovascular parameters, has been studied and the model results have been validated using the data in the literature as well as the recorded data in this study. In the second phase, a model for time-varying separation of HRV components as well as estimation of HRV parameters using respiration information has been proposed. High frequency (HF) component of HR, which is known as the respiratory-related component, overlaps with the typical low frequency (LF) band when the respiratory rate is low. Therefore, a reference signal for HF variations would help in better discriminating the LF and HF components of HR. 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, ectrocardiogram (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 obtained using either PTT or PPGamp as inputs are compared with the respective results while using real respiration signal as input. It has been illustrated that PTT performs better than PPGamp during normal respiration with correlation coefficient of 0.81±0.19 vs. 0.6±0.2 for LF and 0.76±0.19 vs. 0.59±0.197 for HF. On the other hand, PPGamp represents real respiration signal better than PTT during deep respiration with correlation coefficient of 0.74±0.12 vs. 0.87±0.1 for the HF component. Therefore, it can be deduced that PTT and PPGamp offer good potential to be used as references for the respiratory-related variations, thus avoiding additional devices for recording respiration
  9. Keywords:
  10. Respiration ; Photoplethysmography ; Cardiovascular System ; Automatic Nervous System ; Cardiovascular Regulation

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