Heart rate variability analysis using a high-frequency band centered on respiratory frequency significantly differentiated Joy from Fear (median PLF/PHFb 3.79 vs 3.21, p=0.0004).
Observational (n=25)
Randomized order of emotion videos
No
Does HRV analysis with a high-frequency band centered on respiratory frequency improve the recognition of human emotions (Joy, Fear, Relax)?
Centering the high-frequency band of heart rate variability on the respiratory frequency improves the statistical differentiation of emotional states compared to standard fixed frequency bands.
Absolute Event Rate: 3.79% vs 3.21%
p-value: p=0.0004
The work presented in this paper aims at assessing human emotion recognition by means of the analysis of the heart rate variability (HRV) with varying spectral bands based on respiratory frequency (RF). Three specific emotional states are compared corresponding to calm-neutral state (Relax), positive elicitation (Joy) and negative elicitation (Fear). Standard HRV analysis in time and frequency domain is performed. In order to better characterize the HRV component related to respiratory sinus arrhythmia, the high frequency (HF) band is centered on RF. Results reveal that the power content in low band (PLF), the normalized power content in HF band (PHFn) and the sympathovagal ratio (LF/HF) can be suitable indices to distinguish Relax and Joy. Mean heart rate and RF are significantly different between Relax and Fear. Different HRV indices show significant differences between Joy and Fear, such as pNN50, PLF, PHFn and LF/HF. Statistical analysis of HRV indices with HF centered in the RF results in a lower p-value than the ones with a HF standard band.
Yamuza等人（Sat,）在情绪识别中进行了观察性研究（n=25）。评估了情绪引发（快乐）与情绪引发（恐惧）在PLF/PHFb（以呼吸频率为中心的高频段交感-迷走比）上的表现（p=0.0004）。使用以呼吸频率为中心的高频带进行的心率变异性分析明显区分了快乐与恐惧（中位数PLF/PHFb 3.79对比3.21，p=0.0004）。