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  • Küçük Resim Yok
    Öğe
    Model based analysis of the effects of respiration signal parameters on heart rate variability [Solunum sinyali parametrelerinin kalp hizi de?işkenli? ine etkisinin model bazli i?ncelenmesi]
    (2006) Yidiz M.; Özbay Y.; Ider Y.Z.
    In this study, Ursino and Magosso model that includes respiration effect on cardiovascular system is implemented using Matlab. The simulations are performed to investigate the effects of respiration rate, tidal volume and expiration-inspiration time ratio on Heart Rate Variability (HRV) signals. Power Spectral Density (PSD) of HRV signals that are obtained from model simulation was determined by Periodogram and Yule-Walker methods. There is not a significant difference between the PSDs, obtained by the two methods. The simulation results that are obtained by changing respiration rate and tidal volume, are consistent with previous experimental studies reported in the literature. However the model does not include a mechanism that accounts for the effect on HRV of the rate of change of lung volume. This is conjectured to be the reason for why model results and experimental observations are not in complete conformity when the effect of inspiration-expiration time ratio on HRV is studied. © 2006 IEEE.
  • Küçük Resim Yok
    Öğe
    Use of chest circumference signal as an input to models of respiration–HRV interaction
    (Springer Verlag, 2007) Yildiz M.; Ider Y.Z.
    Contributions of respiration to the genesis of Heart Rate Variability are well known. Respiration not only generates the HF (High Frequency) component of the power spectrum of HRV, called respiratory sinus arrhythmia, but also contributes to the formation of the LF (Low Frequency) peak. Therefore it is necessary that respiration is also recorded during an HRV test so that LF/HF ratio, which is the most important parameter extracted from HRV for assessing sympathovagal balance, is more properly evaluated. LF components of lung volume (VL), intrathoracic pressure (PT), and abdominal pressure (PA), are more significant especially when respiration is slightly irregular, and may directly contribute to HRV LF power. Models developed to study these interactions need respiratory signal inputs. Measurements of VL, PT, and PA in a clinical setting are either not possible or induce stress in the patient and thus alter HRV. On the other hand chest and abdominal circumference signals (CT and CA respectively) are easier to acquire without inducing stress. It is shown that by appropriately scaling and offsetting CT and CA one obtains signals representative of PT and PA which then can be used as inputs to the models. VL can also be derived from PT through a linear empirical formula published elsewhere. CT, CA, and VL from 5 volunteers are recorded together with ECG. It is shown that CT is linearly related to VL, and hence to PT. The derived PT and PA signals are then applied to a Respiration-Cardiovascular System model and HRV is derived. It is observed that model derived and real HRVs are temporally well correlated. It is also shown that in the presence of increased LF power of the circumference signals HRV LF power also increases. © International Federation for Medical and Biological Engineering 2007.