Key result
ARFIMA-GARCH models applied to 24-hour heart rate variability recordings successfully discriminated among normal individuals, heart failure patients, and patients with atrial fibrillation.
Why the study?
Can ARFIMA-GARCH models applied to 24-hour HRV recordings discriminate between normal individuals, heart failure patients, and atrial fibrillation patients?
Can ARFIMA-GARCH models applied to 24-hour HRV recordings discriminate between normal individuals, heart failure patients, and atrial fibrillation patients?
ARFIMA-GARCH modeling of heart rate variability can effectively capture long memory and conditional volatility to distinguish between normal sinus rhythm, heart failure, and atrial fibrillation.
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.
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Leite et al. (2013) studied Heart failure and atrial fibrillation (n=15). ARFIMA-GARCH models was evaluated on Discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation. ARFIMA-GARCH models applied to 24-hour heart rate variability recordings successfully discriminated among normal individuals, heart failure patients, and patients with atrial fibrillation.
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