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Needle detection by electro-localization for a needle EMG exam robotic simulator

机译:电动肌电图检查机器人模拟器的电定位检针

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Needle EMG (Electromyogram) Exam (NEE) is an important neurological exam, and neurology interns and novice medical need repetitive training to gain the necessary skill to perform the exam. However, until now it has been difficult to reproduce multiple pathological conditions for their training, since in most cases, trainees serve as human subjects for each other. A robotic simulator that could reproduce behavior with various pathological disorders can be of great help for NEE skill training. Needle tip localization is a key component of the robotic simulator, since position-dependent-EMG is the signal source for skilled neurologists to determine the pathological situation. The needle tip localization has been investigated for many medical tests and applications, such as prostate brachytherapy, etc. However, only few studies have been reported on the process of needle operation in muscle based on EMG signals dependent on needle tip position. Our idea is to apply a tissue-like conductive phantom so as to realize both physical sense of insertion, and needle localization for the NEE robotic simulator. A pair of electrodes was designed to generate a near-linear voltage distribution along the depth direction of the tissue-like phantom, by which the inserted needle could be localized. The influence of the shape of phantom and electrodes on detection accuracy were investigated by a set of measurement experiment and a computer simulation. The results showed that, the estimated depth values agreed with that of the computer simulation model, and the curved phantom had a much steeper distribution in the deeper region (better accuracy for needle tip detection).
机译:针EMG(脑电图)考试(NEE)是一项重要的神经科考试,神经科实习生和新手医学需要重复培训以获取执行考试所需的技能。但是,直到现在,要复制多种病理条件进行训练还是很困难的,因为在大多数情况下,受训人员彼此都是人类的受试者。可以重现各种病理性疾病行为的机器人模拟器对NEE技能培训有很大帮助。针尖定位是机器人模拟器的关键组成部分,因为位置相关的EMG是熟练的神经科医生确定病理情况的信号源。已经针对许多医学测试和应用(例如前列腺近距离放射治疗等)对针尖的定位进行了研究。但是,根据基于针尖位置的EMG信号,关于肌肉中的针操作过程的报道很少。我们的想法是应用一种类似组织的导电体模,从而实现NEE机器人模拟器的物理插入感和针头定位。设计一对电极以沿组织状体模的深度方向生成近乎线性的电压分布,通过该分布可以定位插入的针头。通过一组测量实验和计算机仿真研究了体模和电极形状对检测精度的影响。结果表明,估计的深度值与计算机仿真模型的估计值一致,并且弯曲的体模在更深的区域中具有更陡峭的分布(用于针尖检测的精度更高)。

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