In 8 patients with cardiac dilative myopathy and pseudo-normalized mitral Doppler pattern (DP, age 60±2, mean±SE), and in 11 age-matched control subjects (NL, age 61±4), beat-by-beat end diastolic (EDV), end systolic (ESV), stroke (SV) ventricular volume (VV) measurements were obtained at rest for periods of at least 4 min by echocardiographic acoustic quantification (AQ) signal. RR intervals and respiration were recorded as well. In all subjects, spectral analysis showed a prevalence of high frequency (HF) components in all VV series, with an increased absolute power in DP (53±12 ml2 of HF SV variability compared to 16±5 in NL). Folded scatter diagrams of the VV values vs the respiratory phase revealed consistent patterns. Parameters of a linear prediction model of SV were estimated by considering as inputs:1) three samples of respiration; 2) the previous sample of EDV slow trends, 3) the previous RR interval with a goodness of fit of 47±4% in NL and 43±6% in DP.

Interaction between respiration and beat-by-beat ventricularparameters from acoustic quantification / E.G. Caiani, M. Turiel, S. Muzzupappa, L. Colombo, A. Gandini, A. Porta, G. Baselli, S. Cerutti. - In: COMPUTERS IN CARDIOLOGY. - ISSN 0276-6574. - 27:(2000), pp. 49-52.

Interaction between respiration and beat-by-beat ventricularparameters from acoustic quantification

M. Turiel;A. Porta;
2000

Abstract

In 8 patients with cardiac dilative myopathy and pseudo-normalized mitral Doppler pattern (DP, age 60±2, mean±SE), and in 11 age-matched control subjects (NL, age 61±4), beat-by-beat end diastolic (EDV), end systolic (ESV), stroke (SV) ventricular volume (VV) measurements were obtained at rest for periods of at least 4 min by echocardiographic acoustic quantification (AQ) signal. RR intervals and respiration were recorded as well. In all subjects, spectral analysis showed a prevalence of high frequency (HF) components in all VV series, with an increased absolute power in DP (53±12 ml2 of HF SV variability compared to 16±5 in NL). Folded scatter diagrams of the VV values vs the respiratory phase revealed consistent patterns. Parameters of a linear prediction model of SV were estimated by considering as inputs:1) three samples of respiration; 2) the previous sample of EDV slow trends, 3) the previous RR interval with a goodness of fit of 47±4% in NL and 43±6% in DP.
Settore ING-INF/06 - Bioingegneria Elettronica e Informatica
Settore MED/11 - Malattie dell'Apparato Cardiovascolare
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/24282
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