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

dc.contributor.author	Argentina Leite	en
dc.contributor.author	Rocha,AP	en
dc.contributor.author	Silva,ME	en
dc.date.accessioned	2018-01-17T11:02:12Z
dc.date.available	2018-01-17T11:02:12Z
dc.date.issued	2013	en
dc.description.abstract	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	en
dc.identifier.uri	http://repositorio.inesctec.pt/handle/123456789/6644
dc.identifier.uri	http://dx.doi.org/10.1063/1.4802035	en
dc.language	eng	en
dc.relation	6245	en
dc.rights	info:eu-repo/semantics/embargoedAccess	en
dc.title	Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity	en
dc.type	article	en
dc.type	Publication	en

Now showing 1 - 1 of 1