Abstract

Motor unit (MU) decomposition, generally, requires a time-consuming and labor-intensive manual inspection/editing process from human operators to ensure high accuracy. In this study, we propose and validate a rule-based auto-editing method that could potentially substitute the manual process. Methods: The proposed auto-editing framework (autoeditor) consists of four main rules for adding and removing spikes based on the height of the innervation pulse train (IPT) and the regularity of the firing rate of the identified motor unit. The rules were optimized and validated based on an open-source database including raw MU spike trains estimated from the convolution kernel compensation method and the manually edited MU spike trains from eight human operators. Results: Across 110 motor units, the average rate of agreement between the auto-editor and human operators reached 99.2% after the auto-editor corrected more than 10 edits for each motor unit on average from the raw spike trains. More importantly, the characteristics of the motor unit behaviors, including the MU firing rate and recruitment threshold, were consistent across human operators and the proposed auto-editor. Conclusion: With a simple but effective rulebased auto-editing framework, comparable performance in MU refinement was achieved as human operators. Significance: The proposed auto-editing framework has the potential to standardize the MU editing practice, lower the requirements for expert knowledge and specialized training for MU decomposition, and provide an expandable framework allowing contributions from the community.