Multiscale properties of instantaneous parasympathetic activity in severe congestive heart failure: A survivor vs non-survivor study

Multifractal analysis of cardiovascular variability series is an effective tool for the characterization of pathological states associated with congestive heart failure (CHF). Consequently, variations of heartbeat scaling properties have been associated with the dynamical balancing of nonlinear symp...

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Veröffentlicht in:Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jg. 2017; S. 3761 - 3764
Hauptverfasser: Valenza, G., Wendt, H., Kiyono, K., Hayano, J., Watanabe, E., Yamamoto, Y., Abry, P., Barbieri, R.
Format: Tagungsbericht Journal Article
Sprache:Englisch
Japanisch
Veröffentlicht: United States IEEE 01.07.2017
Schlagworte:
Data models
Fractals
Heart rate variability
Interpolation
Probability density function
Wavelet analysis
ISSN:1557-170X, 2694-0604, 2694-0604
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Zusammenfassung:Multifractal analysis of cardiovascular variability series is an effective tool for the characterization of pathological states associated with congestive heart failure (CHF). Consequently, variations of heartbeat scaling properties have been associated with the dynamical balancing of nonlinear sympathetic/vagal activity. Nevertheless, whether vagal dynamics has multifractal properties yet alone is currently unknown. In this study, we answer this question by conducting multifractal analysis through wavelet leader-based multiscale representations of instantaneous series of vagal activity as estimated from inhomogeneous point process models. Experimental tests were performed on data gathered from 57 CHF patients, aiming to investigate the automatic recognition accuracy in predicting survivor and non-survivor patients after a 4 years follow up. Results clearly indicate that, on both CHF groups, the instantaneous vagal activity displays power-law scaling for a large range of scales, from ≃ 0.5s to ≃ 100s. Using standard SVM algorithms, this information also allows for a prediction of mortality at a single-subject level with an accuracy of 72.72%.
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ISSN:1557-170X
2694-0604
2694-0604
DOI:10.1109/EMBC.2017.8037675