Key result
Autoregressive modeling-based spectral analysis of heart rate variability may provide a meaningful quantitative measure for assessing an operator's mental workload.
Autoregressive modeling based spectral analysis of heart rate variability may provide a meaningful quantitative measure of an operator's mental workload.
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May support noninvasive workload monitoring in operators; leaves open prospective validation before clinical adoption.
In this paper, the autoregressive modeling based spectral analysis method is described and its application to spectral analysis of heart rate variability (HRV) signal is evaluated. The assessment of heart rate (HR) spectra aimed at providing meaningful measure of mental workload is also discussed. This work may provide insight into the effect of mental workload on the HRV spectral characteristics.
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Zhang et al. (2008) studied Mental workload. Autoregressive modeling based spectral analysis of heart rate variability was evaluated on Assessment of mental workload using HRV spectral characteristics. Autoregressive modeling-based spectral analysis of heart rate variability may provide a meaningful quantitative measure for assessing an operator's mental workload.
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