Work

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

Year: 2025

Type: article

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.

Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering

DOI: 10.1109/tnsre.2025.3599103

Authors: Javier Navallas, Lucia Biurrun, Cristina Mariscal, Silvia Recalde-Villamayor, Armando Malanda, Javier Rodríguez-Falces

Institution: Universidad Pública de Navarra (UPNA)

Language: English

Topic: Muscle activation and electromyography studies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075