Improving the repeatability of Motor Unit Number Index (MUNIX) by introducing additional epochs at low contraction levels

Abstract

Objective To evaluate the repeatability of (Motor Unit Number Index) MUNIX under repeatability conditions, specify the origin of variations and provide strategies for quality control. Methods MUNIX calculations were performed on the bicep brachii muscles of eight healthy subjects. Negative effect of suboptimal electrode positions on MUNIX accuracy was eliminated by employing the high-density surface electromyography technique. MUNIX procedures that utilized a variety of surface interferential pattern (SIP) epoch recruitment strategies (including the original MUNIX procedure, two proposed improvement strategies and their combinations) were described. For each MUNIX procedure, ten thousands of different SIP pools were constructed by randomly recruiting necessary SIP epochs from a large SIP epoch pool (3 datasets, 9 independent electromyography recordings at different contraction levels per dataset and 10 SIP epochs per recording) and implemented for MUNIX calculation. The repeatability of each MUNIX procedure was assessed by summarizing the resulting MUNIX distribution and compared to investigate the effect of SIP epoch selection strategy on repeatability performance. Results SIP epochs selected at lower contraction levels have a stronger influence on the repeatability of MUNIX than those selected at higher contraction levels. MUNIX under repeatability conditions follows a normal distribution and the standard deviation can be significantly reduced by introducing more epochs near the MUNIX definition line. Conclusions The MUNIX technique shows an inherent variation attributable to SIP epochs at low contraction levels. It is recommended that more epochs should be sampled at these low contraction levels to improve the repeatability. Significance The present study thoroughly documented the inherent variation of MUNIX and the causes, and offered practical solutions to improve the repeatability of MUNIX.

Motor unit number index: A potential electrophysiological biomarker for pediatric spinal muscular atrophy

“…In addition, the MUNIX correlated with muscle strength and disability score, confirming that weakness depends on the severity of denervation. Our results agreed with the results obtained for adults 33‐53,55‐58 and those obtained for pediatric patients with SMA reported recently 59 …”

“…The MUNIX technique was performed on the ADM and APB muscles according to previously described methods. 26,32,33,49,55,56 The corresponding nerves underwent supramaximal distal stimulation to achieve maximal CMAP amplitude with minimum rise time and a sharp negative takeoff. The recordings were assessed in a 300-ms window with a filter setting of 3-3000 Hz.…”

A Preliminary Study of Effects of Channel Number and Location on the Repeatability of Motor Unit Number Index (MUNIX)

“…In this study, MUNIX was evaluated in eight healthy subjects using high-density surface EMG, and results were consistent with previous studies (1,8,15,30). A novel mMUNIX method has been proposed, by including additional recording channels and taking into account spatiotemporal EMG information.…”

“…However, MUNIX can provide more information and is proved more sensitive than CMAP alone (1,4,36). It had to be underlined that MUNIX does not estimate the actual quantity of existent MUs, but more of an "index" that related to the number of motor neurons (30,36). Although not carrying physiologically meaning, MUNIX provides a reliable biomarker to detect neurodegenerative diseases.…”

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

“…Moreover, the development of the smart energy management and building energy efficiency retrofitting will be more advanced and have more clear direction (Mathew et al, 2015;Zhou and Yang, 2016). It is also expected that machine learning could be applied to more other energy conversion processes (Langley, 2011) and some challenging topics in optimization and analysis related topics (Chen et al, 2016;Dai et al, 2014Dai et al, , 2015Hu et al, 2012Hu et al, , 2016Hu et al, , 2017Liu et al, 2016aLiu et al, , 2017aLiu et al, , 2017bPeng et al, 2016aPeng et al, , 2016bPeng et al, , 2016cPeng and Zhang, 2017;Shen et al, 2017;Wang et al, 2019;Yuan et al, 2011;Zhang et al, 2017bZhang et al, , 2017cZhang et al, , 2018bZhou et al, 2014).…”