Long Memory and Volatility in HRV: An ARFIMA-GARCH Approach

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Leite, A (Author) Other; Rocha, AP (Author) FCUP; Silva, ME (Author) FCUP

Conference proceedings International

Title: CINC: 2009 36TH ANNUAL COMPUTERS IN CARDIOLOGY CONFERENCE

Pages: 165-168

36th Annual Computers in Cardiology Conference (CinC 2009)

Pk City, UT, SEP 13-16, 2009

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 13 Citations

Scientific classification

FOS: Engineering and technology > Environmental biotechnology

Other information

Authenticus ID: P-003-PDN

Abstract (EN): Heart rate variability (HRV) data display nonstationary characteristics, exhibit long-range correlations (memory) and instantaneous variability (volatility). Recently, we have proposed fractionally integrated autoregressive moving average (ARFIMA) models for a parametric alternative to the widely-used technique detrended fluctuation analysis, for long memory estimation in HRV. Usually, the volatility in HRV studies is assessed by recursive least squares. In this work, we propose an alternative approach based on ARFIMA models with generalized autoregressive conditionally heteroscedastic (GARCH) innovations. ARFIMA-GARCH models, combined with selective adaptive segmentation, may be used to capture and remove long-range correlation and estimate the conditional volatility in 24 hour HRV recordings. The ARFIMA-GARCH approach is applied to 24 hour HRV recordings from the Noltisalis database allowing to discriminate between the different groups.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4