PDF(Pubmed)
Abstract：
OBJECTIVE: The present pilot study aimed to propose an innovative scale-independent measure based on electroencephalographic (EEG) signals for the identification and quantification of the magnitude of chronic pain.
METHODS: EEG data were collected from three groups of participants at rest: seven healthy participants with pain, 15 healthy participants submitted to thermal pain, and 66 participants living with chronic pain. Every 30 s, the pain intensity score felt by the participant was also recorded. Electrodes positioned in the contralateral motor region were of interest. After EEG preprocessing, a complex analytical signal was obtained using Hilbert transform, and the upper envelope of the EEG signal was extracted. The average coefficient of variation of the upper envelope of the signal was then calculated for the beta (13-30 Hz) band and proposed as a new EEG-based indicator, namely Piqβ, to identify and quantify pain.
RESULTS: The main results are as follows: (1) A Piqβ threshold at 10%, that is, Piqβ ≥ 10%, indicates the presence of pain, and (2) the higher the Piqβ (%), the higher the extent of pain.
CONCLUSIONS: This finding indicates that Piqβ can objectively identify and quantify pain in a population living with chronic pain. This new EEG-based indicator can be used for objective pain assessment based on the neurophysiological body response to pain.
CONCLUSIONS: Objective pain assessment is a valuable decision-making aid and an important contribution to pain management and monitoring.
METHODS: EEG data were collected from three groups of participants at rest: seven healthy participants with pain, 15 healthy participants submitted to thermal pain, and 66 participants living with chronic pain. Every 30 s, the pain intensity score felt by the participant was also recorded. Electrodes positioned in the contralateral motor region were of interest. After EEG preprocessing, a complex analytical signal was obtained using Hilbert transform, and the upper envelope of the EEG signal was extracted. The average coefficient of variation of the upper envelope of the signal was then calculated for the beta (13-30 Hz) band and proposed as a new EEG-based indicator, namely Piqβ, to identify and quantify pain.
RESULTS: The main results are as follows: (1) A Piqβ threshold at 10%, that is, Piqβ ≥ 10%, indicates the presence of pain, and (2) the higher the Piqβ (%), the higher the extent of pain.
CONCLUSIONS: This finding indicates that Piqβ can objectively identify and quantify pain in a population living with chronic pain. This new EEG-based indicator can be used for objective pain assessment based on the neurophysiological body response to pain.
CONCLUSIONS: Objective pain assessment is a valuable decision-making aid and an important contribution to pain management and monitoring.
摘要：
目的：本初步研究旨在提出一种基于脑电图（EEG）信号的创新的独立于量表的测量方法，用于识别和量化慢性疼痛的程度。
方法：收集三组静息参与者的脑电图数据：七名健康参与者，15名健康参与者接受了热痛，和66名患有慢性疼痛的参与者。每30秒，还记录了参与者感觉到的疼痛强度评分.感兴趣的是位于对侧运动区域的电极。脑电图预处理后，使用希尔伯特变换获得了复杂的分析信号，提取脑电信号的上包络。然后计算β（13-30Hz）频段的信号上包络的平均变异系数，并将其作为新的基于EEG的指标，即Piqβ，识别和量化疼痛。
结果：主要结果如下：（1）在10％时的Piqβ阈值，也就是说,Piqβ≥10%,表示疼痛的存在，(2)Piqβ(%)越高，疼痛程度越高。
结论：这一发现表明Piqβ可以客观地识别和量化患有慢性疼痛的人群的疼痛。这种新的基于EEG的指标可用于基于神经生理体对疼痛的反应的客观疼痛评估。
结论：客观疼痛评估是一种有价值的决策辅助手段，也是疼痛管理和监测的重要贡献。
方法：收集三组静息参与者的脑电图数据：七名健康参与者，15名健康参与者接受了热痛，和66名患有慢性疼痛的参与者。每30秒，还记录了参与者感觉到的疼痛强度评分.感兴趣的是位于对侧运动区域的电极。脑电图预处理后，使用希尔伯特变换获得了复杂的分析信号，提取脑电信号的上包络。然后计算β（13-30Hz）频段的信号上包络的平均变异系数，并将其作为新的基于EEG的指标，即Piqβ，识别和量化疼痛。
结果：主要结果如下：（1）在10％时的Piqβ阈值，也就是说,Piqβ≥10%,表示疼痛的存在，(2)Piqβ(%)越高，疼痛程度越高。
结论：这一发现表明Piqβ可以客观地识别和量化患有慢性疼痛的人群的疼痛。这种新的基于EEG的指标可用于基于神经生理体对疼痛的反应的客观疼痛评估。
结论：客观疼痛评估是一种有价值的决策辅助手段，也是疼痛管理和监测的重要贡献。
