Combining electromyographic and electrical impedance data sets through machine learning: A study in D2-mdx and wild-type mice.
Combining electromyographic and electrical impedance data sets through machine learning: A study in D2-mdx and wild-type mice.
复制标题
通过机器学习结合肌电图和电阻抗数据集：D2-mdx 和野生型小鼠的研究。
DOI：
10.1002/mus.27963
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发表时间：
2023
期刊：
影响因子：
3.4
通讯作者：
Rutkove,SewardB
中科院分区：
文献类型：
--
作者：
Pandeya,Sarbesh;Sanchez,Benjamin;Nagy,JaniceA;Rutkove,SewardB
针阻抗-肌电图（iEMG）通过使用带有6个电极的新型针，两个电极用于肌电图，四个电极用于肌电阻抗图（EIM），同时评估肌肉的主动和被动电特性。在这里，我们评估了一种通过机器学习（ML）结合多频肌电图和EIM数据来区分D2 - mdx肌肉萎缩症和野生型（WT）小鼠骨骼肌的方法。方法从D2 - mdx小鼠、肌营养不良模型和WT动物的股四头肌中获取脑电图数据。脑电数据在深度麻醉下采集，肌电数据在轻度麻醉下采集，允许有限的自发运动。使用三种不同的方法对肌电数据进行傅里叶变换，以提供在频率范围内采样的功率谱。基于随机森林的嵌套ML分别应用于EIM和EMG数据集，然后使用嵌套交叉验证程序一起评估健康与疾病类别分类。结果对20只D2 - mdx和20只WT肢体的数据进行分析。在区分健康小鼠和疾病小鼠方面，EIM数据的准确率分别为93.1%和75.6%，优于肌电图数据。EIM和肌电图数据集的结合产生了与EIM数据单独相似的性能，准确率为92.2%。我们已经展示了一种基于机器学习的方法，可以将iEMG针获得的EIM和EMG数据结合起来。虽然EIM - EMG联合使用并不比EIM单独使用更好，但这里使用的方法展示了一种结合两种技术来表征骨骼肌全部电特性的新方法。
Introduction/AimsNeedle impedance‐electromyography (iEMG) assesses the active and passive electrical properties of muscles concurrently by using a novel needle with six electrodes, two for EMG and four for electrical impedance myography (EIM). Here, we assessed an approach for combining multifrequency EMG and EIM data via machine learning (ML) to discriminate D2‐mdx muscular dystrophy and wild‐type (WT) mouse skeletal muscle.MethodsiEMG data were obtained from quadriceps of D2‐mdx mice, a muscular dystrophy model, and WT animals. EIM data were collected with the animals under deep anesthesia and EMG data collected under light anesthesia, allowing for limited spontaneous movement. Fourier transformation was performed on the EMG data to provide power spectra that were sampled across the frequency range using three different approaches. Random forest‐based, nested ML was applied to the EIM and EMG data sets separately and then together to assess healthy versus disease category classification using a nested cross‐validation procedure.ResultsData from 20 D2‐mdx and 20 WT limbs were analyzed. EIM data fared better than EMG data in differentiating healthy from disease mice with 93.1% versus 75.6% accuracy, respectively. Combining EIM and EMG data sets yielded similar performance as EIM data alone with 92.2% accuracy.DiscussionWe have demonstrated an ML‐based approach for combining EIM and EMG data obtained with an iEMG needle. While EIM‐EMG in combination fared no better than EIM alone with this data set, the approach used here demonstrates a novel method of combining the two techniques to characterize the full electrical properties of skeletal muscle.