基于脑电和肌电相干性的辅助中风病人主动康复方法研究
发布时间：2019-03-04 15:07
【摘要】：中风是一种高死亡率和高致残率的脑血管疾病。大部分的中风幸存患者会丧失很多功能,诸如运动功能、语言功能、记忆功能、视觉功能等。现代康复医学研究认为康复训练对中风患者的功能康复具有重大的促进作用。因此研究有效的康复训练方法对中风患者的恢复具有重大的实际意义。中风患者的康复可以分为被动康复和主动康复两大类。在被动康复中,中风患者的主动意识并没有参与到康复训练中。然而在主动康复中,中风患者自主意识主动参与到康复训练中。研究表明主动康复的效果比被动康复效果要理想。另外中风患者手部功能在日程生活中占有很大的比重,如何恢复手部功能具有重要意义。 为了中风患者手部功能的康复,本文以建立有效的主动康复方法为研究目标。在分析了关键技术和难点以后,本文从两个方面出发分别提出有效的解决方法,1)建立了一种基于肌电模型调制的电刺激系统用于辅助手部功能康复方法；2)在此基础上本文基于脑电肌电相干性(Cortico-muscular coherence (CMC))分析,提取患者的自主意识信号,建立了一套有效的主动康复方法,用于手部功能的康复训练。 本文根据健康被试抓握不同直径不同重量物体时发放的肌电信号建立一个肌电模型,克服了抓握不同物体时肌电信号难以表达的问题；通过该肌电模型来调制电刺激系统形成一个相应的电刺激模型,使该电刺激模型刺激强度和正常人肌电发放模型相一致,这与传统恒压或者恒流电刺激相比,通过肌电模型调制的电刺激模型可以有效延缓肌肉疲劳,延长使用时间。由于中风患者肌肉存在肌痉挛、肌萎缩、肌无力等症状,导致单纯利用肌电信号不能有效的提取出自主意识信号以区分不同的动作。另一方面脑电信号对手部精细动作的区分率不高,有效的提取出被试的自主意识信号也存在一定的问题。考虑到上述的不足,本文对基于现有的肌电信号(Electromyography (EMG))、脑电信号(electroencephalography (EEG))以及脑电肌电相干性信号(CMC)提取自主意识信号进行了重点研究。本文对EMG信号、EEG信号和CMC信号分类准确率进行了对比,发现在健康被试进行指伸vs.拇内收,指伸vs.指屈,指伸v s.静息手部不同动作时,单纯用EEG脑电信号的分类准确率分别为71.7_+12.32%,71.2_+12.80%,81.39_+11.52%；而采用CMC分类准确率则分别为78.,6_+4.29%,81.0_+7.34%和78.025_+9.39%。在中风患者进行指伸v s.指屈,指伸vs.静息手部不同动作时,单纯用EMG肌电信号的分类准确率为分别为68.79_+0.41%,97.42_+0.27%；单纯用EEG脑电信号的分类准确率分别为81-0_+0.53%,85-9_+0.23%；而利用CMC脑电肌电相干性的分类准确率则分别为85.44_+1.06%,91.87+132%。健康被试的CMC与EEC信号相比,指伸vs.拇内收,指伸vs.指屈提高了10%左右。中风患者的CMC与EEG相比,指伸vs.指屈,指伸vs.静息分别提高了3.54%和5.98%。CMC与EMG相比,指伸vs.指屈提高了16.65%。以上研究结果证明了无论是健康被试还是中风患者与单纯使用EEG信号或单纯使用EMC信号相比,CMC能更有效的提取自主意识信号。 根据以上的研究基础,本文设计了一个用于中风患者手部功能康复的系统。该系统以利用CMC信号用于准确提取中风患者主动意识信号,该主动意识信号有效的用于控制外部的多通道电刺激系统设备辅助中风患者的手部功能康复。 纵观本文的研究成果,主要创新点在于(1)以健康被试抓握不同直径、不同重量物体时的肌电信号为依据,建立了一个肌电模型,对手部不同动作进行了定量的表达,通过肌电模型调制电刺激强度建立一个电刺激模型,可以用于中风患者的电刺激(electrical stimulation,Es)治疗,相比恒定强度的电刺激方法,该电刺激可以延缓肌肉疲劳,增加使用时间；(2)研究表明,基于脑电肌电相干性信号(CMC信号)与BEG信号,EMG信号相比可以提高分类准确率,能有效的提取自主意识信号。 本研究为中风患者手部的主动康复提供客观的依据,有利于系统的进一步研制。
[Abstract]:Stroke is a high-mortality and high-disability-rate cerebrovascular disease. Most of the stroke survivors lose a lot of their functions, such as exercise, language, memory, vision, and so on. The study of modern rehabilitation medicine considers that the rehabilitation training has a great effect on the function rehabilitation of patients with stroke. Therefore, the effective rehabilitation training method is of great practical significance to the recovery of stroke patients. The rehabilitation of patients with stroke can be divided into two categories: passive rehabilitation and active rehabilitation. In passive rehabilitation, the active consciousness of stroke patients is not involved in the rehabilitation training. However, in the active rehabilitation, the self-consciousness of stroke patients is actively involved in the rehabilitation training. The results show that the effect of active rehabilitation is more than that of passive rehabilitation. In addition, that hand function of the stroke patient has a great specific gravity in the schedule life, and how to restore the hand function is of great significance. In order to recover the hand function of patients with stroke, this paper aims to establish an effective method of active rehabilitation for the purpose of study. On the basis of the analysis of the key technology and the difficult point, this paper puts forward an effective solution to solve the problem from two aspects, and 1) a kind of electric stimulation system based on the myoelectric model modulation is set up to assist the rehabilitation of the hand function. Methods:2) Based on the analysis of the corico-muscular coherence (CMC), the self-consciousness signal of the patient was extracted, a set of effective methods of active rehabilitation and the rehabilitation training for hand function were established. In this paper, a myoelectric model is set up according to the electromyographic signals that are distributed in different weight objects with different diameters, and the problem that the myoelectric signal is difficult to express when the different objects are grasped is overcome. The electric stimulation system is modulated by the myoelectric model to form a corresponding electric spike. The stimulation model of the electric stimulation model is consistent with the normal human myoelectric distribution model, and compared with the traditional constant-voltage or constant-current electric stimulation, the electric stimulation model which is modulated by the myoelectric model can effectively delay the muscle fatigue and prolong the time The symptoms of muscle spasm, amyotrophy, myasthenia gravis and other symptoms in the muscle of the stroke patient can not be effectively extracted by using the myoelectric signal to distinguish the difference of the self-consciousness signal. and on the other hand, the differentiation rate of the fine movement of the brain electrical signal opponent part is not high, and the effective extraction of the self-consciousness signal to be tested also exists In the light of the above-mentioned deficiencies, the present paper is based on Electromyography (EMG), Electroencephalography (EEG) and Electromyoelectric Coherence Signal (CMC) to extract the self-awareness signal. In this paper, the classification accuracy of EMG signal, EEG signal and CMC signal is compared, and it is found that the classification accuracy of EEG signal is 71.7 __ + 12.32%, 71.2 __ + 12.80%, 81.39 __ + 11, respectively. and the classification accuracy of the CMC is respectively 81.0 _ + 7.34% and 78.025 _ + 9 for 78,6 _ + 4.29%, 81.0 _ + 7.34% and 78.025 _ + 9. The classification accuracy of EMG signal was 68.79 _ + 0.41%, 97.42 _ + 0.27%, respectively. The classification accuracy of EEG was 81-0 _ + 0.53% and 85-9 _ + 0, respectively. The accuracy of the classification of the CMCs was 85.44, 1.06%, 91.87 + 1, respectively. 32%. The CMC of the healthy subjects compared with the EEC signal, the finger flexion and extension vs. the finger flexion increased by 10 compared to the EEC signal. The CMC of stroke patients increased by 3.54% and 5.98%, respectively, compared with the EEG. 65%. The results of the above study demonstrate that CMC can be more effective in extracting a self-mind, both in healthy subjects and in stroke patients compared to simply using an EEG signal or simply using an EMC signal Understanding the signal. Based on the above research basis, this paper designs a hand function for stroke patients. The system is used for accurately extracting a stroke patient active consciousness signal by using a CMC signal, and the active consciousness signal is effective for controlling an external multi-channel electric stimulation system device to assist a stroke patient's hand The main innovation point of this paper is to (1) establish a myoelectric model based on the electromyographic signals of different diameters and different weight objects in healthy subjects. the quantitative expression is performed, the electrical stimulation intensity is modulated by the myoelectric model to establish an electric stimulation model, and the electric stimulation model can be used for the treatment of the electrical stimulation (Es) of a stroke patient, and compared with the electric stimulation method with the constant intensity, the electric stimulation can delay the muscle fatigue and increase the use time; (2) The study shows that the classification accuracy can be improved compared with that of the BEG signal and the EMG signal based on the EEG signal (CMC signal). This study provides an objective basis for the active rehabilitation of the hand of a stroke patient.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：R743.3
[Abstract]:Stroke is a high-mortality and high-disability-rate cerebrovascular disease. Most of the stroke survivors lose a lot of their functions, such as exercise, language, memory, vision, and so on. The study of modern rehabilitation medicine considers that the rehabilitation training has a great effect on the function rehabilitation of patients with stroke. Therefore, the effective rehabilitation training method is of great practical significance to the recovery of stroke patients. The rehabilitation of patients with stroke can be divided into two categories: passive rehabilitation and active rehabilitation. In passive rehabilitation, the active consciousness of stroke patients is not involved in the rehabilitation training. However, in the active rehabilitation, the self-consciousness of stroke patients is actively involved in the rehabilitation training. The results show that the effect of active rehabilitation is more than that of passive rehabilitation. In addition, that hand function of the stroke patient has a great specific gravity in the schedule life, and how to restore the hand function is of great significance. In order to recover the hand function of patients with stroke, this paper aims to establish an effective method of active rehabilitation for the purpose of study. On the basis of the analysis of the key technology and the difficult point, this paper puts forward an effective solution to solve the problem from two aspects, and 1) a kind of electric stimulation system based on the myoelectric model modulation is set up to assist the rehabilitation of the hand function. Methods:2) Based on the analysis of the corico-muscular coherence (CMC), the self-consciousness signal of the patient was extracted, a set of effective methods of active rehabilitation and the rehabilitation training for hand function were established. In this paper, a myoelectric model is set up according to the electromyographic signals that are distributed in different weight objects with different diameters, and the problem that the myoelectric signal is difficult to express when the different objects are grasped is overcome. The electric stimulation system is modulated by the myoelectric model to form a corresponding electric spike. The stimulation model of the electric stimulation model is consistent with the normal human myoelectric distribution model, and compared with the traditional constant-voltage or constant-current electric stimulation, the electric stimulation model which is modulated by the myoelectric model can effectively delay the muscle fatigue and prolong the time The symptoms of muscle spasm, amyotrophy, myasthenia gravis and other symptoms in the muscle of the stroke patient can not be effectively extracted by using the myoelectric signal to distinguish the difference of the self-consciousness signal. and on the other hand, the differentiation rate of the fine movement of the brain electrical signal opponent part is not high, and the effective extraction of the self-consciousness signal to be tested also exists In the light of the above-mentioned deficiencies, the present paper is based on Electromyography (EMG), Electroencephalography (EEG) and Electromyoelectric Coherence Signal (CMC) to extract the self-awareness signal. In this paper, the classification accuracy of EMG signal, EEG signal and CMC signal is compared, and it is found that the classification accuracy of EEG signal is 71.7 __ + 12.32%, 71.2 __ + 12.80%, 81.39 __ + 11, respectively. and the classification accuracy of the CMC is respectively 81.0 _ + 7.34% and 78.025 _ + 9 for 78,6 _ + 4.29%, 81.0 _ + 7.34% and 78.025 _ + 9. The classification accuracy of EMG signal was 68.79 _ + 0.41%, 97.42 _ + 0.27%, respectively. The classification accuracy of EEG was 81-0 _ + 0.53% and 85-9 _ + 0, respectively. The accuracy of the classification of the CMCs was 85.44, 1.06%, 91.87 + 1, respectively. 32%. The CMC of the healthy subjects compared with the EEC signal, the finger flexion and extension vs. the finger flexion increased by 10 compared to the EEC signal. The CMC of stroke patients increased by 3.54% and 5.98%, respectively, compared with the EEG. 65%. The results of the above study demonstrate that CMC can be more effective in extracting a self-mind, both in healthy subjects and in stroke patients compared to simply using an EEG signal or simply using an EMC signal Understanding the signal. Based on the above research basis, this paper designs a hand function for stroke patients. The system is used for accurately extracting a stroke patient active consciousness signal by using a CMC signal, and the active consciousness signal is effective for controlling an external multi-channel electric stimulation system device to assist a stroke patient's hand The main innovation point of this paper is to (1) establish a myoelectric model based on the electromyographic signals of different diameters and different weight objects in healthy subjects. the quantitative expression is performed, the electrical stimulation intensity is modulated by the myoelectric model to establish an electric stimulation model, and the electric stimulation model can be used for the treatment of the electrical stimulation (Es) of a stroke patient, and compared with the electric stimulation method with the constant intensity, the electric stimulation can delay the muscle fatigue and increase the use time; (2) The study shows that the classification accuracy can be improved compared with that of the BEG signal and the EMG signal based on the EEG signal (CMC signal). This study provides an objective basis for the active rehabilitation of the hand of a stroke patient.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：R743.3
【共引文献】
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