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Titolo:
Prediction of short cardiovascular variability signals based on conditional distribution
Autore:
Porta, A; Baselli, G; Guzzetti, S; Pagani, M; Malliani, A; Cerutti, S;
Indirizzi:
Univ Milan, Dipartimento Sci Preclin, LITA Vialba, Milan, Italy Univ Milan Milan Italy artimento Sci Preclin, LITA Vialba, Milan, Italy Politecn Milan, Dipartimento Bioingn, I-20133 Milan, Italy Politecn MilanMilan Italy I-20133 timento Bioingn, I-20133 Milan, Italy Univ Milan, Osped L Sacco, Ctr Ric Cardiovasc, CNR, Milan, Italy Univ Milan Milan Italy d L Sacco, Ctr Ric Cardiovasc, CNR, Milan, Italy
Titolo Testata:
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
fascicolo: 12, volume: 47, anno: 2000,
pagine: 1555 - 1564
SICI:
0018-9294(200012)47:12<1555:POSCVS>2.0.ZU;2-9
Fonte:
ISI
Lingua:
ENG
Soggetto:
HEART-RATE-VARIABILITY; POWER SPECTRUM ANALYSIS; NONLINEAR DYNAMICS; TIME-SERIES; HUMANS; CHAOS;
Keywords:
cardiovascular control; cardiovascular variability; complexity; conditional distribution; nonlinear dynamics; nonlinearity index; predictability index; prediction; surrogate data;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Engineering, Computing & Technology
Citazioni:
25
Recensione:
Indirizzi per estratti:
Indirizzo: Porta, A Univ Milan, Dipartimento Sci Preclin, LITA Vialba, Milan, Italy Univ Milan Milan Italy Sci Preclin, LITA Vialba, Milan, Italy
Citazione:
A. Porta et al., "Prediction of short cardiovascular variability signals based on conditional distribution", IEEE BIOMED, 47(12), 2000, pp. 1555-1564

Abstract

A new approach measuring the predictability of a process is proposed. The predictor is defined as the median of the distribution conditioned by a sequence of L - 1 previous samples (i.e., a pattern). A function referred to as the corrected mean squared predictor error is defined to prevent the perfect adequacy to the data (i.e., the decrease to zero of the prediction error), thus avoiding to divide the whole set of data in learning and test sets. This function exhibits a minimum and this minimum is taken as a measure of predictability of the series. The use of the minimization procedure avoids to fix a priori the pattern length L, This approach permits one a reliable measure of predictability on short data sequences (around 300 samples). Moreover, this method, in connection with a surrogate data approach, is useful to detect nonlinear dynamics. The analysis indicates that, in simulated and real data, predictability and nonlinearity measures provide different information,The application of this approach to the analysis of cardiovascular variability series of the heart period (RR interval) and systolic arterial pressure (SAP) shows: 1) SAP series is more predictable than RR interval series; 2) predictability of the RR interval series is larger during tilt, during controlled respiration at 10 breaths/min (bpm) and after high-dose administration of atropine; 3) SAP series is dominated by linear correlation; 4) RR interval series exhibits nonlinear dynamics during controlled respiration at10 bpm and after low-dose administration of atropine, while it is linear during sympathetic activation produced by tilt and after peripheral parasympathetic blockade caused by high-dose administration of atropine.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 07/08/20 alle ore 15:00:26