Developing Normal Turns-Amplitude Clouds for Upper and Lower Limbs

On the establishment of reference values of clouds of electromyography interference pattern by linear regression method and percentile method and comparison of sensitivity and specificity of both methods

Objective

Turn-amplitude clouds were widely used in automatic electromyography (EMG) interference pattern analysis. Earlier works employed the intercept ± 2SD (standard deviation) of the linear regression equation as the upper and lower boundaries of the clouds. The goal of this study was to employ the linear regression method and percentile method to calculate the reference value of turn-amplitude clouds, identify the determining criteria in accordance with the receiver operator characteristic curve (ROC), and analyze the sensitivity and specificity of the linear regression cloud, percentile cloud, and quantitative assessment of the motor unit potential (QMUP).

Methods

First, we explore what factors affect the number of turns per second and the mean amplitude. Then, their logarithms were taken for the normal test. All muscle data were used to calculate the reference values of percentile clouds. However, the reference values of the linear regression clouds were obtained for the muscles with a bivariate normal distribution, homogeneous variances and a linear correlation. We calculated the prediction interval with the standard errors of the intercept and slope of the linear regression equation, which can determine the upper and lower boundaries of the linear regression clouds. Furthermore, we obtained ROCs of these clouds, which were used as the determining criteria to determine the optimum cut-off values. Finally, our study analyzed the sensitivity and specificity of the linear regression cloud, percentile cloud, and QMUP.

Results

We here presented the reference values and ROCs of the linear regression clouds and percentile clouds. We suggest the determining criteria be based on ROCs. The areas under the curve (AUC) of both clouds are larger than 0.8, revealing that they have significant diagnostic value. Our results display that the specificities of the linear regression clouds, percentile clouds, and QMUP are almost identical to each other, whereas the sensitivity of percentile cloud is higher than those of QMUP and linear regression clouds.

Conclusion

According to ROCs, the researchers determine the determining criteria of the linear regression clouds and percentile clouds. Our findings suggest that the percentile clouds possess a wide application range and significant diagnostic value, therefore it may be the optimum for automatic EMG interference pattern analysis.

A strong linear relationship between Turns/Amplitude peak ratio and ratio at maximal effort

In EMG interference pattern analysis, the peak value of turns to mean amplitude ratio [peak(T/A)] is an established clinically significant marker, but its calculation requires specific software available only in few EMG apparatuses. On the contrary, the turns to mean amplitude ratio obtained at maximal muscle contraction (T/Amax) is easily calculated but less well standardized. We aimed to quantitatively assess the association between T/Amax and peak(T/A). Data were derived from 642 muscle contractions (Nc) from 270 consecutive patients (Np) who underwent EMG at our laboratory (software Dantec Keypoint, QEMG) from May 2015 to September 2016 and had interference patterns obtained from at least one of the following muscles: triceps-lateral head, brachioradialis, extensor digitorum communis and biceps. Statistics were calculated separately for normal and neurogenic muscles. Peak(T/A) was calculated by the built-in "peak ratio" function. T/Amax was calculated by the built-in Interference Pattern analysis function. The ratio with the highest amplitude was selected as T/Amax. Linear regression models provided high Pearson correlation coeffficientscoefficients (R) between peak(T/A) and T/Amax for all 4 muscles, normal or neurogenic, except a subgroup of biceps in patients aged <40y. Specifically, R were: (A) triceps normal 0.79 (Nc = 99), neurogenic 0.83 (Nc = 50) (B) brachioradialis normal 0.81 (Nc = 84), neurogenic 0.78 (Nc = 66) (C) extensor digitorum communis normal 0.72 (Nc = 92), neurogenic 0.73 (Nc = 61) (D) biceps (age > 40y) normal 0.77 (Nc = 77), neurogenic 0.67 (Nc = 62). We conclude that T/Amax has a strong linear association with peak(T/A) and, therefore, the former may be further investigated as a potentially useful quantitative diagnostic marker, especially in cases where the latter is not available.