Abstract:
Parkinson’s disease (PD) is the second most common neurodegenerative disease
that affects a wide range of productive individuals worldwide. The common approach to
diagnose PD is through clinical assessment of the patient, which is highly subjective and time
consuming. Electromyography (EMG) can be taken as a cheap way of PD diagnosis. However,
highly experienced experts are required to interpret the signals. The manual procedures are
complex, time-consuming, and prone to error resulting in misdiagnosis. In this research, an
automatic system for detection and classification of PD stages using EMG signals acquired
from different upper limb movements is proposed. In addition, effective upper limb movement
for the identification of PD has been investigated. The data required for training and testing
the system was collected from flexor carpi radialis and biceps brachii muscles of 15 PD
patients and 10 healthy control subjects at Jimma University Medical Center. The raw EMG
signal was preprocessed and frequency and time-domain features were extracted. A multiclass
support vector machine model was then trained for four-class classification (normal, early,
moderate, and advanced PD levels). The performance of the system was evaluated using
different performance evaluators and a promising result has been obtained. 90%, 91.7%, 95%,
and 96.6% overall classification accuracies were obtained for elbow flexion by 90-degrees
without load, elbow flexion by 90-degrees with load, touching the shoulder, and wrist
pronation, respectively. A user-friendly interface has been also developed for ease of use of
the automatic PD classification system.
