Classification between upper and lower motor neurons in amyotrophic lateral sclerosis patients by electromyographic processing and analysis

Amyotrophic Lateral Sclerosis (ALS) is a specific disease that causes the death of neurons controlling voluntary muscles. The death affects Upper Motor Neurons (UMN) and Lower Motor Neurons (LMN). In this paper, we aim to identify UMN, LMN ALS patients and healthy subjects by studying their Electromyography (EMG) signals. More specifically, the Right Anterior Tibialis (RAT) muscle, responsible of dorsiflexing and inverting the foot during walking is studied. The solution is mainly composed of two blocks: features extraction and classification. A large feature vector, combining statistics, time, frequency and time-frequency domains features is extracted and their relevance is checked. Then, many classification techniques are tested in order to identify the most powerful ones. Finally, a dimensionality reduction, using both Principal Component Analysis and Sequential Forward Selection technique is carried out in order to select the most relevant features. Simulation results achieved 97% of overall accuracy for healthy/LMN/UMN separation and more than 99% for healthy/ALS and LMN/UMN classification.