Wang Hujun, Wang Yingpeng, Li Yingqi, Wang Congxiao, Qie Shuyan
Department of Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Front Hum Neurosci. 2023 Jul 4;17:1176001. doi: 10.3389/fnhum.2023.1176001. eCollection 2023.
This study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients.
Fifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of tibialis anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMS-peak time, mean power frequency (MPF), and median frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the receiver operating characteristic (ROC) curve.
The results showed that delayed activation of TA and decreased activation of LG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter.
These findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future.
本研究旨在通过表面肌电图（SEMG）研究腰椎间盘突出症（LDH）患者行走过程中的肌肉激活情况，并使用随机森林（RF）算法基于SEMG参数建立诊断模型，用于LDH患者受压神经根的定位诊断。
招募了58例LDH患者和30名健康受试者。在行走过程中双侧采集胫前肌（TA）和腓外侧肌（LG）的SEMG。分析均方根峰值（RMS-peak）、RMS-peak时间、平均功率频率（MPF）和中位数频率（MF）。建立基于RF算法的SEMG参数诊断模型以定位受压神经根，并进行重复保留实验进行验证。本研究使用准确率、精确率、召回率、F1分数、Kappa值和受试者工作特征（ROC）曲线下面积评估模型的诊断效率。
结果显示，L5组观察到TA激活延迟和LG激活降低，而S1组观察到LG激活降低和LG激活提前。基于八个SEMG参数的RF模型平均准确率为84%，ROC曲线下面积为0.93。TA的RMS峰值时间被确定为最重要的SEMG参数。
这些发现表明，RF模型可辅助LDH患者受压神经根的定位诊断，且SEMG参数可为未来优化诊断模型提供进一步参考。
