Motor Neuron Loss Detection Based on EMG Probability Density Function Shape Descriptors

Abstract

EMG interference pattern analysis is routinely used in the assessment of motor neuron loss. We propose systematizing interference pattern analysis by recording an isometric ramp contraction of a muscle, from minimum to maximum activation level. Three EMG probability density function (PDF) shape descriptors are then employed to quantify the PDF evolution assessing EMG filling through contraction: filling factor, negentropy, and kurtosis. The three filling curves are fitted with an exponential model, and the decay constant parameters are employed to obtain a feature vector that characterizes the EMG filling behavior of the muscle. Results show a tendency of the filling curves to shorten and not reach saturation when neuropathy is simulated, and a subsequent dependency of the decay constant parameters with neuropathy progression. We demonstrate, with a set of real signals and through simulation experiments, the ability of the features to be used by a classification system to detect motor neuron loss. With the set of real signals (from 40 subjects with L5 radiculopathy and 40 healthy controls), results show a 0.86 sensibility and 0.84 specificity, indicating a promising performance when incorporated into clinical decision support systems.