Surface electromyography for testing motor dysfunction in amyotrophic lateral sclerosis

Abstract

Objectives: To investigate the use of a set of dynamical features, extracted from surface electromyography, to study upper motor neuron (UMN) degeneration in amyotrophic lateral sclerosis (ALS).

Methods: We acquired surface EMG signals from the upper limb muscles of 13 ALS patients and 20 control subjects and classified them according to a novel set of muscle activity features, describing the temporal and frequency dynamic behavior of the signals, as well as measures of its complexity. Using a battery of classification approaches, we searched for the most discriminating combination of those features, as well as a suitable strategy to identify ALS.

Results: We observed significant differences between ALS patients and controls, in particular when considering features highlighting differences between forearm and hand recordings, for which classification accuracies of up to 94% were achieved. The most robust discriminations were achieved using features based on detrended fluctuation analysis and peak frequency, and classifiers such as decision trees, random forest and Adaboost.

Conclusion: The current work shows that it is possible to achieve good identification of UMN changes in ALS by taking into consideration the dynamical behavior of surface electromyographic (sEMG) data.

Keywords: Amyotrophic lateral sclerosis; Classification; Diagnostic; Machine learning; Signal dynamics; Surface electromyography; Upper motor neuron degeneration.