Multiscale analysis of short term heart beat interval, arterial blood pressure, and instantaneous lung volume time series.
BACKGROUND
Physiological systems are ruled by mechanisms operating across multiple temporal scales. A recently proposed approach, multiscale entropy analysis, measures the complexity at different time scales and has been successfully applied to long term electrocardiographic recordings. The purpose of this work is to show the applicability of this methodology, rooted on statistical physics ideas, to short term time series of simultaneously acquired samples of heart rate, blood pressure and lung volume, from healthy subjects and from subjects with chronic heart failure. In the same spirit, we also propose a multiscale approach, to evaluate interactions between time series, by performing a multivariate autoregressive (AR) modeling of the coarse grained time series.


METHODS
We apply the multiscale entropy analysis to our data set of short term recordings. Concerning the multiscale version of the multivariate AR approach, we apply it to the four dimensional time series so as to detect scale dependent patterns of interactions between the physiological quantities.


RESULTS
Evaluating the complexity of signals at the multiple time scales inherent in physiologic dynamics, we find new quantitative indicators which are statistically correlated with the pathology. Our results show that multiscale entropy calculated on all the measured quantities significantly differs (P<10(-2) and less) in patients and control subjects, and confirms the complexity-loss theory of aging and disease. Also applying the multiscale autoregressive approach significant differences were found between controls and patients; in the sight of finding a possible diagnostic tools, satisfactory results came also from a receiver-operating-characteristic curve analysis (with some values above 0.8).


CONCLUSIONS
The multiscale entropy analysis can give useful information also when only short term physiological recordings are at disposal, thus enlarging the applicability of the methodology. Also the proposed multiscale version of the multivariate regressive analysis, applied to short term time series, can shed light on patterns of interactions between cardiorespiratory variables.
Angelini
Leonardo
L
TIRES-Center of Innovative Technologies for Signal Detection and Processing, University of Bari, Via Amendola 173, 70126 Bari, Italy.
Maestri
Roberto
R
Marinazzo
Daniele
D
Nitti
Luigi
L
Pellicoro
Mario
M
Pinna
Gian Domenico
GD
Stramaglia
Sebastiano
S
Tupputi
Salvatore A
SA
D001794:Blood Pressure; D001795:Blood Pressure Determination; D002908:Chronic Disease; D004562:Electrocardiography; D005260:Female; D006333:Heart Failure / Q000175:diagnosis* / Q000503:physiopathology; D006339:Heart Rate; D006801:Humans; D008168:Lung / Q000503:physiopathology*; D008176:Lung Volume Measurements; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors