What question did this study set out to answer?

The aim is to evaluate the reliability of AIC in selecting autoregressive model order in HRV analysis.

March 25, 2026Open Access

Robustness of AIC-Based AR Order Selection in HRV Analysis

Key Result

Akaike Information Criterion-based autoregressive order selection in HRV analysis leads to overfitting and false spectral peaks when the maximum search order is excessively high (p=50).

Key Points

The aim is to evaluate the reliability of AIC in selecting autoregressive model order in HRV analysis.
Systematic examination of AIC for AR order selection using RR interval time series.
Analysis of data from the PhysioNet healthy subject database with N = 1257.
Investigated effects of using a high maximum search order (p = 50).
AIC exhibited reduced robustness leading to overfitting due to excessive order selection.
Model began to capture noise rather than intrinsic HRV structure.
Supported the use of additional criteria, like BIC and FPE, for reliable model selection.

Structured PICO

Population

RR interval time series of the PhysioNet healthy subject database

Intervention

AIC-based AR order selection with maximum search order set to 50

Outcome

Robustness of AR order selection and model stabilitysurrogate

AIC-based AR order selection for HRV analysis becomes unstable and overfits when the search range is too large, suggesting the need for more stringent criteria like BIC or FPE.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

This study systematically examines the robustness of the Akaike Information Criterion (AIC) in determining the optimal order (p) of an autoregressive (AR) model applied to the RR interval time series of the PhysioNet healthy subject database. The AR approach is widely used to estimate the power spectral density (PSD) of heart rate variability (HRV), and accurate order selection is essential for model stability and reliable spectral estimation. Although the AIC is designed to balance model fit and complexity, it suffers from the problem of arbitrary model selection. This study provides a quantitative robustness analysis of information-criterion-based AR order selection under controlled expansion of the search space. Specifically, we investigated the behavior of the AIC using the PhysioNet database (N = 1257) under conditions where the maximum search order was set to an excessively high value (p = 50), far exceeding the commonly recommended range. Our analysis suggested that the AR model began to capture subtle noise and nonstationary components rather than the intrinsic HRV structure, leading to overfitting and excessive order selection, resulting in false peaks in the PSD and reduced robustness. In conclusion, order decisions based solely on information criteria such as the AIC become unstable when the search range is too large. To ensure robustness, it is recommended to complement the AIC with more stringent criteria such as the Bayesian Information Criterion (BIC) or Final Prediction Error (FPE), in addition to the traditional maximum order restriction.