*Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Illinois, U.S.A.; †Department of Bioengineering, University of Illinois, Chicago, Illinois, U.S.A.; ‡Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, U.S.A.; §Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, U.S.A.; and ‖Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, China.
J Clin Neurophysiol. 2014 Feb;31(1):35-40. doi: 10.1097/01.wnp.0000436896.02502.31.
Examination of spontaneous muscle activity is an important part of the routine electromyogram (EMG) in assessing neuromuscular diseases. The EMG is specifically valuable as a diagnostic test in supporting the diagnosis of amyotrophic lateral sclerosis. High-density surface EMG is a relatively new technique that has until now been used in research but has the potential for clinical application. This study presents a simple high-density surface EMG method for automatic detection of spontaneous action potentials from surface electrode array recordings of patients with amyotrophic lateral sclerosis. To reduce computational complexity while maintaining useful information from the electrode array recording, the multichannel high-density surface EMG was transferred to single-dimensional data by calculating the maximum difference across all channels of the electrode array. A spike detection threshold was then set in the single-dimensional domain to identify the firing times of each spontaneous action potential spike, whereas a spike extraction threshold was used to define the onset and offset of the spontaneous spikes. These data were used to extract the spontaneous spike waveforms from the electrode array EMG. A database of detected spontaneous spikes was thus obtained, including their waveforms, on all channels along with their corresponding firing times. This newly developed method makes use of the information from different channels of the electrode array EMG recording. It also has the primary feature of being simple and fast in implementation, with convenient parameter adjustment and user-computer interaction. Hence, it has good possibilities for clinical application.
自发性肌肉活动检查是评估神经肌肉疾病的常规肌电图(EMG)的重要组成部分。EMG 作为一种诊断测试特别有价值,可支持肌萎缩侧索硬化症的诊断。高密度表面 EMG 是一种相对较新的技术,迄今为止一直用于研究,但具有临床应用的潜力。本研究提出了一种简单的高密度表面 EMG 方法,用于自动检测肌萎缩侧索硬化症患者表面电极阵列记录中的自发性动作电位。为了降低计算复杂性,同时保持电极阵列记录中的有用信息,通过计算电极阵列所有通道之间的最大差异,将多通道高密度表面 EMG 转换为一维数据。然后在一维域中设置尖峰检测阈值,以识别每个自发性动作电位尖峰的触发时间,而尖峰提取阈值用于定义自发性尖峰的起始和结束。这些数据用于从电极阵列 EMG 中提取自发性尖峰波形。因此,获得了一个包括所有通道及其相应触发时间的检测到的自发性尖峰的数据库。这种新开发的方法利用了电极阵列 EMG 记录中不同通道的信息。它还具有实施简单、快速的主要特点,具有方便的参数调整和人机交互。因此,它具有很好的临床应用可能性。