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1.
J Biomech ; 22(5): 447-53, 1989.
Article in English | MEDLINE | ID: mdl-2777819

ABSTRACT

The effects of aorto-femoral bypass grafts on the vascular input impedance, and the ratio of pulsatile to total power were studied in eight dogs. Unilateral ileo-femoral stenosis was simulated and comparisons were made between the input impedance and power distribution in healthy and simulated disease situations. Input impedance magnitude spectra and phase were displayed graphically and it was shown that the presence of the simulated disease increases the ratio of pulsatile to total power as measured in the abdominal aorta from 7.5 to 14.8% (p less than 0.05). This suggests that the presence of the stenosis creates an impedance mismatch thus causing reflected waves to propagate proximally towards the heart. It was concluded that the way in which the heart transfers fluid power into the arterial bed was compromised by the presence of the ileo-femoral partial stenoses. It is further suggested that the system described in the paper makes it possible to quantitatively assess afterload, vascular input impedance and cardiovascular power distribution as a quasi-real time diagnostic procedure.


Subject(s)
Aorta, Abdominal/surgery , Femoral Artery/surgery , Thrombophlebitis/physiopathology , Thrombophlebitis/surgery , Vascular Resistance , Anastomosis, Surgical , Animals , Aorta, Abdominal/physiopathology , Dogs , Femoral Artery/physiopathology , Monitoring, Physiologic , Pressure , Pulsatile Flow
2.
IEEE Trans Biomed Eng ; 37(4): 417-20, 1990 Apr.
Article in English | MEDLINE | ID: mdl-2338355

ABSTRACT

This communication presents the development of an optimal digital differentiating filter based on the Wiener theory for estimating the peak derivative of the left ventricular pressure (LVP) signal. The magnitude coherence function is used to estimate the signal and noise spectra. The peak derivative obtained by this method is found to be within 2% of the results obtained by a seven-point second-order data fit and a DFT technique.


Subject(s)
Models, Cardiovascular , Signal Processing, Computer-Assisted , Ventricular Function , Algorithms , Humans , Pressure
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