【作者】 王娟；

【导师】 李小俚；

【作者基本信息】 燕山大学 ， 系统工程， 2011， 硕士

【摘要】 慢性疼痛在人群中已经成为一种高发病率的疾病,作为一种持续性的疼痛,与大脑认知区域的损伤有着很密切的关系,进而对患者造成注意力、执行和一般的认知功能的损伤,因此慢性痛对患者和社会的影响是不容忽视的。慢性痛脑电信号的研究成为当前脑认知和临床治疗领域的研究热点和难点问题之一。由于传统的慢性痛脑电信号研究方法对信息提取的有限性和宏观性,我们不能够完全地了解慢性痛患者神经系统的病理生理变化,进而对慢性痛的有效治疗造成了一定的阻碍。本文针对这个问题,开展慢性痛的多通道脑电信号信息提取研究,做出了以下研究工作。首先,论文通过分析研究传统的脑电信号的处理方法,以及当前的慢性痛脑电信号研究的信息有限性和宏观性,提出了一种基于多通道脑电信号的分析的平行因子算法。与传统的方法相比,该方法在分析多通道自发脑电信号时,能够同时获得信号中的时间,频率,通道信息,为脑认知研究提供了更多的信号特征,为临床治疗提供了更加有效的依据。最后通过数据仿真验证了所提出的方法的有效性。其次,我们将基于小波变换的平行因子算法用在实验科学的数据分析中,研究疼痛组的大鼠在慢性痛发展过程中与对照组中大鼠在清醒和激光伤害性刺激的硬膜外脑电信号。通过设计合适的仿真和统计方案,该方法在处理诱发脑电信号中同样取得了很好的仿真结果,克服了传统的慢性痛研究方法中单纯地分析诱发脑电信号中的事件相关电位(Event-related Potentials, ERP)成分所造成的信息量不足的问题,此外,该实验得到的实验结论与先前文献中的生理研究结果也是一致的。最后,我们研究了临床上慢性痛病人和正常人在外界体感刺激下的诱发脑电信号,通过设计合适的仿真程序,时间-频率-空间域上的信号特征被提取,结合数学统计中的检验,结果发现病人和正常人的生理反应时间均在250ms左右,慢性痛病人的生理反应频率更低,大脑顶区和前额区是疼痛信息处理的关键区域。此外,本文通过设计统计方案研究了年龄对反映频率差异的影响。该方法的有效性为临床研究提供了病理特征信息。更多还原

【Abstract】 Chronic pain is an extremely high degree of neural diseases. This kind of persistent pain is closely connected to the injury of cognitive areas of brain so that reduces the patients’attention, executive, and cognitive performance. Chronic pain makes patients suffer great pain and becomes a serious social problem. Recently, studies to the EEG of chronic pain have been one of the hot and hard issues in the fields of brain and cognitive sciences and clinical care. For the limitation and macroscale in information extraction of EEG by current methods, it is hard to reveal the pathological of the neural systems of chronic pain patients, and can’t provide effective treatments to the chronic pain patients. To improve this problem, this thesis investigates the multi-channel EEG signal processing of chronic pain. The main research work is introduced as follows:First, analyzing the current methods of EEG signal processing and the limitation and macroscale of information extraction, this thesis proposes the Parallel Factor Analysis (PARAFAC) algorithms based on multi-channel EEG analysis. Comparing with the current methods, the proposed algorithms can extract more information simultaneously in time-frequency-channel domain from the multi-channel EEG signals. This information would provide more evaluation for the brain and cognitive research and clinical care. Some simulations demonstrate the effective of the algorithms.Second, this thesis proposes the PARAFAC algorithm based on wavelet transform to study the different characteristics of EEG signals between chronic pain rats and control rats under the laser stimuli. By this algorithm, we find that it is effective to process the evoked-EEG and verifies the previous study results, while it conquers the limitation of insufficient information in previous ERP analysis.At last, we study the evoked-EEG of chronic pain patients and healthy people in clinic. By the method of PARAFAC, the information in time-frequency-location domain is extracted. Analyzed by mathematical statistics of t-test, this work finds that the somatosensory cortical responses occurre around 250 ms in both patients and healthy people, and patients have lower neural response frequency than healthy people. We also find that the central and prefrontal regions are predominant in pain processing. Moreover, this thesis investigates the influences of different ages to the response frequency by multiple linear regression. Simulations and analysis demonstrate that PARAFAC is an effective method to reveal the pathologies of chronic pain in clinic.更多还原