基于超聲影像分析的功能性電刺激肌肉形態(tài)及活動研究
發(fā)布時間：2018-07-13 10:54
【摘要】：近年來,脊髓損傷導致癱瘓的發(fā)病率呈顯著上升趨勢。肢體功能重建是截癱患者康復治療時關注的一個重點與難點。在肢體功能重建方面,功能性電刺激(functional electrical stimulation, FES)技術(shù)被普遍認為是一種有效的臨床工具。 但是,很多新的FES技術(shù)還只局限于實驗室階段,臨床應用的FES刺激模式以及達到的效果都非常有限,其原因之一就是對于FES作用的機理方面的研究還有所欠缺,目前還鮮有研究在宏觀水平上說明FES如何影響肌肉的功能活動和狀態(tài)。而骨骼肌的收縮和舒張是人體各種運動的基礎,骨骼肌的結(jié)構(gòu)是其功能活動的首要決定因素。所以,要想利用FES實現(xiàn)骨骼肌功能重建并使其得到快速發(fā)展和廣泛應用的目的,就必然需要對FES作用下骨骼肌的結(jié)構(gòu)及其功能活動與狀態(tài)的機理進行更深入的探究。 本研究設計了等角和等矩兩種收縮狀態(tài),每種狀態(tài)包含F(xiàn)ES誘發(fā)和自主運動兩種運動模式。利用B型超聲成像系統(tǒng)對FES下的目標肌肉進行成像,提取目標肌肉的功能活動信息—肌肉厚度和紋理特征,同時采集關節(jié)角度并將其作為反饋信號,采集相應角度的自主收縮下肌肉的超聲圖像信息。 在每種收縮狀態(tài)下,本研究提取了FES誘發(fā)和自主運動下受試者的肌肉厚度和紋理特征。首先,計算了FES下肌肉厚度、紋理特征與關節(jié)角度的相關性,結(jié)果顯著,表明周期性FES是周期性肌肉活動的直接原因;其次,在達到相同的運動效果前提下,利用比值對比了FES誘發(fā)的和自主運動產(chǎn)生的肌肉特征信息的差異,然后根據(jù)受試者的個體差異性分析造成這種差異的影響因素,并利用逐步篩選的多元線性回歸方法對影響因素進行主因素分析,從而確定每個肌肉特征的主要影響因素,旨在通過調(diào)節(jié)FES的刺激模式和強度以調(diào)整主因素,使得FES誘發(fā)下的肌肉特征參數(shù)向著達到相同運動效果下的自主收縮的肌肉特征參數(shù)回歸。 本研究利用支持向量機和人工神經(jīng)網(wǎng)絡的機器學習方法,利用FES條件下的既定刺激強度、肌肉厚度和紋理信息對關節(jié)角度進行預測。結(jié)果表明,采用肌肉信息與FES相結(jié)合的方法在預測效果上明顯優(yōu)于只有FES的預測,充分體現(xiàn)了肌肉信息在反映肌肉活動和狀態(tài)上了重要作用,能為臨床FES系統(tǒng)提供了一種潛在的精確控制方法。
[Abstract]:In recent years, the incidence of paralysis caused by spinal cord injury has increased significantly. Limb function reconstruction is an important and difficult point in rehabilitation treatment of paraplegic patients. In limb function reconstruction, functional electrical stimulation (functional electrical stimulation, FES) is widely regarded as an effective clinical tool. However, many new FES technologies are confined to the laboratory stage, and the clinical application of FES stimulation mode and the effect achieved are very limited, one of the reasons is that the mechanism of FES action is still lacking. There are few studies on how FES affects muscle function and state at macro level. The contraction and relaxation of skeletal muscle are the basis of all kinds of exercise, and the structure of skeletal muscle is the primary determinant of its function. Therefore, in order to use FES to reconstruct skeletal muscle function and make it develop rapidly and widely, it is necessary to probe into the structure, the mechanism of functional activity and state of skeletal muscle under the action of FES. In this study, two kinds of contraction states, equal angle and equal moment, are designed. Each state includes two motion modes: FES induced motion and autonomous motion. The target muscles under FES are imaged by B-mode ultrasound imaging system, and the functional activity information of target muscles is extracted-muscle thickness and texture features. At the same time, the angle of joint is collected and used as feedback signal. To collect the ultrasonic image information of muscle under the corresponding angle of autonomic contraction. In each contractive state, we extracted the muscle thickness and texture features of FES-induced and autonomic subjects. Firstly, the correlation between muscle thickness, texture feature and joint angle under FES is calculated. The results show that periodic FES is the direct cause of periodic muscle activity. The ratio was used to compare the differences of FES induced and autonomic muscle characteristic information, and then the influencing factors of the differences were analyzed according to the individual differences of the subjects. The main factors of each muscle feature were analyzed by stepwise multivariate linear regression method. The main factors were adjusted by adjusting the stimulation mode and intensity of FES. The FES induced muscle characteristic parameters are regressed to the muscle characteristic parameters with the same exercise effect. In this study, support vector machine (SVM) and artificial neural network (Ann) were used to predict the angle of joint by using the given stimulation intensity, muscle thickness and texture information under the condition of FES. The results show that the method of combining muscle information with FES is superior to only FES in predicting effect, which fully reflects the important role of muscle information in reflecting muscle activity and state. It can provide a potential accurate control method for clinical FES system.
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R310
本文編號：2119115
[Abstract]:In recent years, the incidence of paralysis caused by spinal cord injury has increased significantly. Limb function reconstruction is an important and difficult point in rehabilitation treatment of paraplegic patients. In limb function reconstruction, functional electrical stimulation (functional electrical stimulation, FES) is widely regarded as an effective clinical tool. However, many new FES technologies are confined to the laboratory stage, and the clinical application of FES stimulation mode and the effect achieved are very limited, one of the reasons is that the mechanism of FES action is still lacking. There are few studies on how FES affects muscle function and state at macro level. The contraction and relaxation of skeletal muscle are the basis of all kinds of exercise, and the structure of skeletal muscle is the primary determinant of its function. Therefore, in order to use FES to reconstruct skeletal muscle function and make it develop rapidly and widely, it is necessary to probe into the structure, the mechanism of functional activity and state of skeletal muscle under the action of FES. In this study, two kinds of contraction states, equal angle and equal moment, are designed. Each state includes two motion modes: FES induced motion and autonomous motion. The target muscles under FES are imaged by B-mode ultrasound imaging system, and the functional activity information of target muscles is extracted-muscle thickness and texture features. At the same time, the angle of joint is collected and used as feedback signal. To collect the ultrasonic image information of muscle under the corresponding angle of autonomic contraction. In each contractive state, we extracted the muscle thickness and texture features of FES-induced and autonomic subjects. Firstly, the correlation between muscle thickness, texture feature and joint angle under FES is calculated. The results show that periodic FES is the direct cause of periodic muscle activity. The ratio was used to compare the differences of FES induced and autonomic muscle characteristic information, and then the influencing factors of the differences were analyzed according to the individual differences of the subjects. The main factors of each muscle feature were analyzed by stepwise multivariate linear regression method. The main factors were adjusted by adjusting the stimulation mode and intensity of FES. The FES induced muscle characteristic parameters are regressed to the muscle characteristic parameters with the same exercise effect. In this study, support vector machine (SVM) and artificial neural network (Ann) were used to predict the angle of joint by using the given stimulation intensity, muscle thickness and texture information under the condition of FES. The results show that the method of combining muscle information with FES is superior to only FES in predicting effect, which fully reflects the important role of muscle information in reflecting muscle activity and state. It can provide a potential accurate control method for clinical FES system.
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R310
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