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Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

Title
Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity
Type
Article in International Scientific Journal
Year
2013
Authors
Argentina Leite
(Author)
Other
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Ana Paula Rocha
(Author)
FCUP
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Maria Eduarda Silva
(Author)
FEP
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Journal
Title: ChaosImported from Authenticus Search for Journal Publications
Vol. 23 No. 2
ISSN: 1054-1500
Scientific classification
FOS: Natural sciences > Mathematics
Other information
Authenticus ID: P-005-726
Abstract (EN): 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. (C) 2013 AIP Publishing LLC
Language: English
Type (Professor's evaluation): Scientific
Notes: Article number 023103
No. of pages: 10
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