生物反馈中脑电信号分析及其在癫痫治疗中应用的研究
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摘要
癫痫是一种常见的慢性神经系统疾病,发病率约占世界人口的1%,其中25%左右的患者属于难治性癫痫。对这部分病人,已经开始采用脑电生物反馈训练方法缓解其癫痫病情与减少发作频度。论文从神经电生理角度出发,研究难治性癫痫病人经生物反馈训练后脑电参数的变化情况,并探讨生物反馈训练对神经疾病的治疗机制与疗效评估方法。
论文首先进行了难治性癫痫患者的脑电生物反馈训练实验,训练方案为强化患者C4导联处12~15Hz的感觉运动节律波,同时抑制4~8Hz的theta波。实验组21例患者的反馈治疗有效率为76.2%。论文还对治疗效果较好的其中6例患者跟踪采集了患者在反馈前、中、后不同时期的的长程、多导脑电记录。
为了用电生理指标评估生物反馈的疗效,论文将功率谱估计引入反馈前后脑电记录的分析中。结果表明:6个难治性癫痫患者在经过一定疗程的脑电生物反馈治疗后,其电生理指标SMR功率/theta功率的变化非常严格的契合了反馈方案,印证了神经信息反馈训练的脑电自我调节机制及其对行为习性的治疗效用,解决了目前对脑电生物反馈疗效的评价多局限于患者症状改善的问题。
因为非线性动力学方法比传统方法更能反映大脑的状态变化,论文将非线性分析方法——近似熵与相关维数引入到病例反馈训练前后的脑电信号分析中,结果表明在接受反馈训练大约1个月左右的时间后,所有病例在几乎所有导联处,其脑电信号的近似熵和相关维数都比反馈治疗前增加。说明生物反馈训练有助于皮层神经元群体电生理活动向更加混沌的状态转化,通过提高系统自身的混沌性(近似熵、相关维数增加)来增强其抗应激能力,改善癫痫病态症状。
论文还设计开发了一套脑电生物反馈系统,能够实现脑电信号的采集、反馈阈值的设定、反馈参数的提取、反馈动画的驱动播放,以及病例与系统管理等功能。反馈参数的设置中加入了商用系统中没有的非线性反馈参数。对系统的测试表明设计开发的脑电系统满足各项功能要求。
Epilepsy is a common serious neurological disorder. Approximately 1% of the world population suffers from this chronic disease, among them about 25% are intractable to current medical and/or surgical therapies. For these patients, EEG biofeedback (also called neurofeedback) is an effective therapy that can help in seizure control. To assess the effects of EEG biofeedback on brain electrophysiology and to determine how biofeedback works, different EEG measurements are applied to explore the variations in EEG signals recorded from epileptic patients taking biofeedback training.
21 patients with intractable epilepsy were trained to increase production of sensorimotor rhythm (SMR, 12–15 Hz) activity and decrease production of theta (4–8 Hz) wave activity with one scalp electrode placed on the position C4. The treatment effective rate was 76.2%. To evaluate the effects of the treatment using EEG analysis, 16-channel EEG recordings of six patients were acquired before a patient received biofeedback training and after about 10 training sessions, others were acquired at different times over the treatment duration.
In this dissertation, the EEG power spectral density (PSD) was used to evaluate the differences in EEG signals before and after biofeedback. After sessions of treatment, the EEG SMR to theta PSD ratio calculated from the C4 electrode site became larger than that before treatment, which agrees well with the biofeedback protocol. This result verified the self-regulatory mechanism of neurofeedback, so that we could evaluate the efficacy of neurofeedback from a new perspective other than the reduction of seizure frequency.
Since nolinear EEG measurements can reflect more information in changes of brain function state than linear measurements, the variations of approximate entropy (ApEn) and correlation dimension (D2) were also explored. The results demonstrated that the ApEn and D2 over the 16-channel EEG recordings all increased about one month later compared with those of the EEG recordings before training. This reveals that biofeedback training can increase the degree of random electrical activity of the cortical neuron population, so that the symptoms of epilepsy are improved and seizures alleviated. Thus the ApEn and D2 criterions can be used to evaluate the effect of EEG biofeedback. It may give an indication of the electrophysiologic basis of EEG biofeedback.
A set of EEG biofeedback system was developed, which has functions of EEG signal acquisition, threshold setting, feature extraction, animation and mp3 playing, system and patient database management and so on. Nonlinear EEG measurements were incorporated into this biofeedback system, which mainly consists of linear frequency parameters at present. The result of system testing proves that the system could satisfy the requirements of biofeedback training.
论文首先进行了难治性癫痫患者的脑电生物反馈训练实验,训练方案为强化患者C4导联处12~15Hz的感觉运动节律波,同时抑制4~8Hz的theta波。实验组21例患者的反馈治疗有效率为76.2%。论文还对治疗效果较好的其中6例患者跟踪采集了患者在反馈前、中、后不同时期的的长程、多导脑电记录。
为了用电生理指标评估生物反馈的疗效,论文将功率谱估计引入反馈前后脑电记录的分析中。结果表明:6个难治性癫痫患者在经过一定疗程的脑电生物反馈治疗后,其电生理指标SMR功率/theta功率的变化非常严格的契合了反馈方案,印证了神经信息反馈训练的脑电自我调节机制及其对行为习性的治疗效用,解决了目前对脑电生物反馈疗效的评价多局限于患者症状改善的问题。
因为非线性动力学方法比传统方法更能反映大脑的状态变化,论文将非线性分析方法——近似熵与相关维数引入到病例反馈训练前后的脑电信号分析中,结果表明在接受反馈训练大约1个月左右的时间后,所有病例在几乎所有导联处,其脑电信号的近似熵和相关维数都比反馈治疗前增加。说明生物反馈训练有助于皮层神经元群体电生理活动向更加混沌的状态转化,通过提高系统自身的混沌性(近似熵、相关维数增加)来增强其抗应激能力,改善癫痫病态症状。
论文还设计开发了一套脑电生物反馈系统,能够实现脑电信号的采集、反馈阈值的设定、反馈参数的提取、反馈动画的驱动播放,以及病例与系统管理等功能。反馈参数的设置中加入了商用系统中没有的非线性反馈参数。对系统的测试表明设计开发的脑电系统满足各项功能要求。
Epilepsy is a common serious neurological disorder. Approximately 1% of the world population suffers from this chronic disease, among them about 25% are intractable to current medical and/or surgical therapies. For these patients, EEG biofeedback (also called neurofeedback) is an effective therapy that can help in seizure control. To assess the effects of EEG biofeedback on brain electrophysiology and to determine how biofeedback works, different EEG measurements are applied to explore the variations in EEG signals recorded from epileptic patients taking biofeedback training.
21 patients with intractable epilepsy were trained to increase production of sensorimotor rhythm (SMR, 12–15 Hz) activity and decrease production of theta (4–8 Hz) wave activity with one scalp electrode placed on the position C4. The treatment effective rate was 76.2%. To evaluate the effects of the treatment using EEG analysis, 16-channel EEG recordings of six patients were acquired before a patient received biofeedback training and after about 10 training sessions, others were acquired at different times over the treatment duration.
In this dissertation, the EEG power spectral density (PSD) was used to evaluate the differences in EEG signals before and after biofeedback. After sessions of treatment, the EEG SMR to theta PSD ratio calculated from the C4 electrode site became larger than that before treatment, which agrees well with the biofeedback protocol. This result verified the self-regulatory mechanism of neurofeedback, so that we could evaluate the efficacy of neurofeedback from a new perspective other than the reduction of seizure frequency.
Since nolinear EEG measurements can reflect more information in changes of brain function state than linear measurements, the variations of approximate entropy (ApEn) and correlation dimension (D2) were also explored. The results demonstrated that the ApEn and D2 over the 16-channel EEG recordings all increased about one month later compared with those of the EEG recordings before training. This reveals that biofeedback training can increase the degree of random electrical activity of the cortical neuron population, so that the symptoms of epilepsy are improved and seizures alleviated. Thus the ApEn and D2 criterions can be used to evaluate the effect of EEG biofeedback. It may give an indication of the electrophysiologic basis of EEG biofeedback.
A set of EEG biofeedback system was developed, which has functions of EEG signal acquisition, threshold setting, feature extraction, animation and mp3 playing, system and patient database management and so on. Nonlinear EEG measurements were incorporated into this biofeedback system, which mainly consists of linear frequency parameters at present. The result of system testing proves that the system could satisfy the requirements of biofeedback training.
引文
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