Please use this identifier to cite or link to this item:
|Title:||Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity|
|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|
|Appears in Collections:||Non INESC TEC publications - Articles in International Journals|
Files in This Item:
|6.2 MB||Adobe PDF|
View/Open Request a copy
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.