On the Prediction of Motor Unit Filter Changes in Blind Source Separation of High-Density Surface Electromyograms During Dynamic Muscle Contractions

We study the changes of Motor Unit (MU) filters in MU identification from high-density surface electromyograms recorded during isokinetic dynamic contractions of biceps brachii muscle. We demonstrate that these changes can be predicted for limited changes of the joint angle by linearly extrapolating previously recorded changes, allowing for the linear prediction-correction paradigm of MU filter updating. We then demonstrate the efficiency of this paradigm by implementing MU filter updating by the Kalman filter and integrating it into the previously published Convolution Kernel Compensation (CKC) MU identification method. When compared to the original CKC method and the previously published cyclostationary CKC method devoted to MU identification in repeated dynamic contractions, the Kalman based MU filter prediction yielded a superior precision of MU firing tracking in dynamic contractions. In the case of relatively fast biceps brachii contractions with full elbow flexion and extension in 2s, the Kalman based MU filter prediction tracked 21.3 +/- 1.8 MUs with an average sensitivity of 95.6 +/- 7.0% and precision of 96.5 +/- 3.5%. In the same conditions, the original CKC method identified 7.1 +/- 2.0 MUs with an average sensitivity of 62.7 +/- 20.1% and precision of 98.1 +/- 3.9%, whereas cyclostationary CKC tracked 18.9 +/- 2.0 MUs with an average sensitivity of 91.8 +/- 12.2% and precision of 94.7 +/- 5.1%.

Automated summary

The study demonstrated that using the Kalman filter for MU filter prediction results in more precise tracking of MU firing during dynamic contractions compared to the original CKC method and cyclostationary CKC method. The Kalman-based approach showed superior sensitivity and precision in identifying MUs during fast biceps brachii contractions with full elbow flexion and extension.