Abstract
We present the multi-scale entropy analysis of short-term physiological time series of simultaneously acquired samples of heart rate, blood pressure and lung volume, from healthy subjects and from subjects with Chronic Heart Failure. 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. As the multi-scale entropy analysis has been applied up to now to 24 hours electrocardiographic signals, these results on short-term recordings enlarge the applicability of the method. In the same spirit of the multi-scale entropy approach, we also propose a multi-scale approach, to evaluate interactions between time series, by performing a multivariate autoregressive modelling of the coarse grained time series. We then address the problem of classifying a subject as healthy or affected by Chronic Heart Failure on the basis of all the collected indicators.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of VIPIMAGE 2007 - 1st ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing |
| Pages | 35-40 |
| Number of pages | 6 |
| Publication status | Published - 2008 |
| Event | VIPIMAGE 2007 - 1st ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing - Porto, Portugal Duration: Oct 17 2007 → Oct 19 2007 |
Other
| Other | VIPIMAGE 2007 - 1st ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing |
|---|---|
| Country | Portugal |
| City | Porto |
| Period | 10/17/07 → 10/19/07 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Biomedical Engineering
Cite this
Multiscale analysis of short-term cardiorespiratory signals. / Angelini, L.; Creanza, T. M.; Maestri, R.; Marinazzo, D.; Pellicoro, M.; Pinna, G. D.; Stramaglia, S.; Tupputi, S.
Proceedings of VIPIMAGE 2007 - 1st ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing. 2008. p. 35-40.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multiscale analysis of short-term cardiorespiratory signals
AU - Angelini, L.
AU - Creanza, T. M.
AU - Maestri, R.
AU - Marinazzo, D.
AU - Pellicoro, M.
AU - Pinna, G. D.
AU - Stramaglia, S.
AU - Tupputi, S.
PY - 2008
Y1 - 2008
N2 - We present the multi-scale entropy analysis of short-term physiological time series of simultaneously acquired samples of heart rate, blood pressure and lung volume, from healthy subjects and from subjects with Chronic Heart Failure. 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. As the multi-scale entropy analysis has been applied up to now to 24 hours electrocardiographic signals, these results on short-term recordings enlarge the applicability of the method. In the same spirit of the multi-scale entropy approach, we also propose a multi-scale approach, to evaluate interactions between time series, by performing a multivariate autoregressive modelling of the coarse grained time series. We then address the problem of classifying a subject as healthy or affected by Chronic Heart Failure on the basis of all the collected indicators.
AB - We present the multi-scale entropy analysis of short-term physiological time series of simultaneously acquired samples of heart rate, blood pressure and lung volume, from healthy subjects and from subjects with Chronic Heart Failure. 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. As the multi-scale entropy analysis has been applied up to now to 24 hours electrocardiographic signals, these results on short-term recordings enlarge the applicability of the method. In the same spirit of the multi-scale entropy approach, we also propose a multi-scale approach, to evaluate interactions between time series, by performing a multivariate autoregressive modelling of the coarse grained time series. We then address the problem of classifying a subject as healthy or affected by Chronic Heart Failure on the basis of all the collected indicators.
UR - http://www.scopus.com/inward/record.url?scp=60949101736&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=60949101736&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9780415457774
SP - 35
EP - 40
BT - Proceedings of VIPIMAGE 2007 - 1st ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing
ER -