Diagnosis of Compressed Nerve Root in Lumbar Disc Herniation Patients by Surface Electromyography

Abstract

Objective: To establish a logistic regression model using surface electromyography (SEMG) parameters for diagnosing the compressed nerve root at L₅ or S₁ level in patients with lumbar disc herniation (LDH).

Methods: This study recruited 24 patients with L₅ nerve root compression and 23 patients with S₁ nerve root compression caused by LDH from May 2014 to May 2016. SEMG signals from the bilateral tibialis anterior and lateral gastrocnemius were measured. The root mean square (RMS), the RMS peak time, the mean power frequency (MPF), and the median frequency (MF) were analyzed. The accuracy, sensitivity, and specificity values were calculated separately. The areas under the curve (AUC) of the receiver-operating characteristic (ROC) curve and the kappa value were used to evaluate the accuracy of the SEMG diagnostic model.

Results: The accuracy of the SEMG model ranged from 85.71% to 100%, with an average of 93.57%. The sensitivity, specificity, AUC, and kappa value of the logistic regression model were 0.98 ± 0.05, 0.92 ± 0.09, 0.95 ± 0.04 (P = 0.006), and 0.87 ± 0.11, respectively (P = 0.001). The final diagnostic model was: P=1-11+ey; y = 10.76 - (5.95 × TA_RMS Ratio) - (0.38 × TA_RMS Peak Time Ratio) - (5.44 × 44 × LG_RMS Peak Time Ratio). L₅ nerve root compression is diagnosed when P < 0.5 and S₁ nerve root compression when P ≥ 0.5.

Conclusions: The logistic regression model developed in this study showed high diagnostic accuracy in detecting the compressed nerve root (L₅ and S₁ ) in these patients with LDH.

Keywords: Compressed nerve root; Diagnosis; Logistic regression model; Lumbar disc herniation; Surface electromyography.