SVM-Based Tool to Detect Patients with Multiple Sclerosis Using a Commercial EMG Sensor

Multiple sclerosis (MS) is a major auto-immune disease that is the leading cause of non-traumatic impairment of the central nervous system (CNS) in young adults. Successful treatment of MS patients depends on accurate tools for both the MS diagnosis and the disability progression. In current and upcoming studies the authors aim to explore the capabilities of applying a commercial electromyographic and inertial sensor (MYO Armband by Thalmic Labs Inc.), coupled with a multichannel signal processing tool, to standard neurological examination of MS progression. In this pilot study we formulate a two-class “healthy control” - “having MS” classification problem. A dataset of electromyographic signals and inertial sensor measurements from 71 individuals (31 MS patients and 40 healthy controls) was acquired during standard neurological examination routine. Temporal and spectral features of the signals were extracted in order to train and validate a classification model. Finally, a Support Vector Machine classifier was obtained giving AUROC = 0.94, 95% CI = [0.88, 0.99]. We propose a set of signal descriptors that correlate with objective components of the neurological examination. The proposed signal acquisition and processing technique, being easy to integrate into the traditional neurological exam, may have high potential for aiding in quantifying MS progression.