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. 2023 Jul 4:17:1176001.
doi: 10.3389/fnhum.2023.1176001. eCollection 2023.

A diagnostic model of nerve root compression localization in lower lumbar disc herniation based on random forest algorithm and surface electromyography

Hujun Wang #  1 , Yingpeng Wang #  1 , Yingqi Li  1 , Congxiao Wang  1 , Shuyan Qie  1
Affiliations

A diagnostic model of nerve root compression localization in lower lumbar disc herniation based on random forest algorithm and surface electromyography

Hujun Wang et al. Front Hum Neurosci. .

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: diagnosis model; lumbar disc herniation; nerve root compression; random forest; surface electromyography.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
(A) Magnetic resonance imaging axial planes in patients with L4/L5 nerve root compression. (B) MRI sagittal planes in patients with L4/L5 nerve root compression. (C) MRI axial planes in patients with L5/S1 nerve root compression. (D) MRI sagittal planes in patients with L5/S1 nerve root compression planes.
FIGURE 2
FIGURE 2
(A) Frontal view of the test. (B) Lateral view of the test. (C) Gait and SEMG capture interface. (D) Trigno™ Wireless Systems.
FIGURE 3
FIGURE 3
Panels (A–C) are typical SEMG presentations of L5 nerve root compression: on the symptomatic side, delayed activation of TA (A) and decreased peak RMS of LG (B), showing co-contraction of TA and LG (C). Panels (D–F) are typical SEMG presentations of S1 nerve root compression: on the symptomatic side, activation of the LG is shifted forward (D), peak RMS of the TA is decreased (E), and a double peak and co-contraction of the LG and TA is found (F).
FIGURE 4
FIGURE 4
Confusion matrix of optimal RF diagnosis model based on SEMG parameters. The color scheme represents the consistency between the predicted and actual results. The numbers in the matrix denote the count of correctly predicted samples within specific categories. The percentages indicate the proportion of correctly predicted samples within those categories.
FIGURE 5
FIGURE 5
The ROC curve of RF diagnosis model based on SEMG parameters.
FIGURE 6
FIGURE 6
The weight of SEMG parameters in RF model.
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