Diagnostic value of electromyographic tremor indicators for Parkinson's disease based on Logistic regression model

Objective To investigate the diagnostic value of electromyographic (EMG) tremor indicators for Parkinson's disease (PD) using the Logistic regression model.

Methods A total of 65 patients with PD (PD group) and 39 patients with essential tremor (ET) (ET group) were enrolled and underwent EMG tremor analysis. General information, disease-related data, and EMG tremor characteristics were compared between the two groups. Multivariate Logistic regression analysis was performed to screen for independent influencing factors of PD, and receiver operating characteristic (ROC) curves were plotted. The area under the curve (AUC) was used to evaluate the diagnostic value of EMG tremor indicators for PD.

Results Compared with the ET group, the PD group had a higher proportion of patients with unilateral onset and those with tremor spectrum frequency ≥2 times, and a lower proportion of patients with a family history of tremor (P < 0.05). The tremor peak frequencies in the resting, postural, and weight-bearing (1 000 g) states were lower in the PD group than in the ET group (P < 0.05). There were statistically significant differences in the tremor rhythm patterns between the two groups in the resting and weight-bearing states (P < 0.05), with the PD group dominated by alternating contraction patterns and the ET group by synchronous contraction patterns. Multivariate Logistic regression analysis revealed that the tremor peak frequency in the weight-bearing state, the tremor rhythm pattern in the resting state, and the frequency of tremor spectrum were independent influencing factors of PD (P < 0.05). The ROC curves showed that the AUCs of the tremor peak frequency in the weight-bearing state, the tremor rhythm pattern in the resting state, and the frequency of tremor spectrum for diagnosing PD were 0.886, 0.750, and 0.779, respectively. The combination of these three indicators yielded the highest AUC (0.936) for diagnosing PD, with a sensitivity of 81.54% and a specificity of 94.87%.

Conclusion The tremor peak frequency in the weight-bearing state, the tremor rhythm pattern in the resting state, and the frequency of tremor spectrum provided by EMG tremor analysis can serve as clinical indicators for early diagnosis of PD, and their combined use offers higher diagnostic value, which can be used to differentiate PD from ET.