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排列熵和排列最小熵在RRI时间序列多情绪状态分析中的应用

Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series.

作者信息

Xia Yirong, Yang Licai, Zunino Luciano, Shi Hongyu, Zhuang Yuan, Liu Chengyu

机构信息

School of Control Science and Engineering, Shandong University, Jinan, 250061, China.

Centro de Investigaciones Ópticas (CONICET La Plata-CIC), C.C. 3, 1897 Gonnet, Argentina.

出版信息

Entropy (Basel). 2018 Feb 26;20(3):148. doi: 10.3390/e20030148.

Abstract

This study's aim was to apply permutation entropy (PE) and permutation min-entropy (PME) over an RR interval time series to quantify the changes in cardiac activity among multiple emotional states. Electrocardiogram (ECG) signals were recorded under six emotional states (neutral, happiness, sadness, anger, fear, and disgust) in 60 healthy subjects at a rate of 1000 Hz. For each emotional state, ECGs were recorded for 5 min and the RR interval time series was extracted from these ECGs. The obtained results confirm that PE and PME increase significantly during the emotional states of happiness, sadness, anger, and disgust. Both symbolic quantifiers also increase but not in a significant way for the emotional state of fear. Moreover, it is found that PME is more sensitive than PE for discriminating non-neutral from neutral emotional states.

摘要

本研究的目的是将排列熵(PE)和排列最小熵(PME)应用于RR间期时间序列,以量化多种情绪状态下心脏活动的变化。在60名健康受试者中,以1000Hz的速率记录了六种情绪状态(中性、快乐、悲伤、愤怒、恐惧和厌恶)下的心电图(ECG)信号。对于每种情绪状态,记录5分钟的心电图,并从这些心电图中提取RR间期时间序列。所得结果证实,在快乐、悲伤、愤怒和厌恶的情绪状态下,PE和PME显著增加。对于恐惧情绪状态,这两个符号量化指标也增加,但不显著。此外,发现PME在区分非中性和中性情绪状态方面比PE更敏感。

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