| Autor: |
Leite, Argentina, Paula Rocha, Ana, Eduarda Silva, Maria |
| Předmět: |
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| Zdroj: |
Chaos; Jun2013, Vol. 23 Issue 2, p023103-023103-10, 1p, 1 Color Photograph, 1 Black and White Photograph, 4 Charts, 7 Graphs |
| Abstrakt: |
Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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