Accurate Classification of Motor Unit Discharges From Dynamic Surface Emg Signals

Abstract

In order to correctly identify the motor unit action potential trains (MUAPTs) in estimated discharges from dynamic surface electromyogram (EMG), an approach for accurate classification of motor unit (MU) discharges is presented. First, the estimated discharges are obtained manually, then the estimated discharges are classified as MUAPTs based on the MU location, which combines the MU depth with the MU plane position. During verification in dynamic muscle contractions, the advanced tripole model is introduced. At SNRs of 10, 20 and 30[Formula: see text]dB, the MUAPTs were identified with true positive rate (TPR) of 91.1[Formula: see text]5.5%, 95.2[Formula: see text]3.7% and 96.1[Formula: see text]2.9%. The results also show that the MU location can be used as a simple method for identifying MUAPT from estimated discharges and selecting reliably decomposed discharges. The newly introduced method is a robust and reliable indicator of MUAPT identification accuracy.