University of California, San Diego, Neuromuscular Division, 402 Dickinson Street, Suite 190, San Diego, CA 92103-8465, USA.
Med Eng Phys. 2012 Mar;34(2):249-55. doi: 10.1016/j.medengphy.2011.07.017. Epub 2011 Aug 9.
Quantification in clinical, diagnostic electromyography (EMG) currently includes motor unit action potential (MUAP) analysis and interference pattern analysis. Early efforts to examine the frequency/power spectra of the interference pattern showed modest value but the technique was not developed further. This paper re-examines spectral analysis, extending it into the time-varying domain, which has never been studied in diagnostic needle EMG. Time-frequency and time-scale analysis employing wavelet and non-wavelet techniques were applied to short trains of MUAPs. The results show that time-varying analysis produces clear visual representations of the energy content of individual MUAPs within an interference pattern. The time frequency representations allow easy, qualitative distinction between normal and neurogenic MUAPs. Furthermore, the quantified MUAP energy correlates well with the current morphological standard and the quantification process is substantially faster. Time-varying analysis links classical power spectral analysis in the realm of interference patterns with quantitative MUAP analysis. In addition to morphological classification, MUAPs might also be classified by energy content, which more closely reflects the physical and physiological consequences of neuromuscular pathology on the motor unit.
目前，临床诊断肌电图（EMG）中的定量分析包括运动单位动作电位（MUAP）分析和干扰模式分析。早期研究干扰模式的频率/功率谱的努力显示出一定的价值，但该技术并未进一步发展。本文重新审视了频谱分析，将其扩展到时变域，这在诊断性针 EMG 中从未研究过。采用小波和非小波技术的时频和时标分析应用于 MUAP 短序列。结果表明，时变分析可在干扰模式内对单个 MUAP 的能量含量产生清晰的直观表示。时频表示可轻松、定性地区分正常和神经源性 MUAP。此外，量化后的 MUAP 能量与当前的形态学标准高度相关，并且量化过程快得多。时变分析将经典的干扰模式中的功率谱分析与定量 MUAP 分析联系起来。除了形态分类之外，MUAP 还可以根据能量含量进行分类，这更能反映神经肌肉病理学对运动单位的物理和生理影响。
