| Title | Beyond long memory in heart rate variability: an approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity. | ||
| Author | Leite, Argentina; Rocha, Ana Paula; Silva, Maria Eduarda | ||
| Journal | Chaos | Publication Year/Month | 2013-Jun |
| PMID | 23822468 | PMCID | -N/A- |
| Affiliation | 1.Departamento de Matematica, Escola de Cie;ncias e Tecnologia, Universidade de Tras-os-Montes e Alto Douro and CM-UTAD, Portugal. | ||
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.