Ensemble of bagged tree classifier for the diagnosis of neuromuscular disorders

Intramuscular Electromyography (EMG) signal provides a significant source of information that plays an inevitable role in the diagnosis of neuromuscular disorders. The ensemble method represents a supervised machine learning algorithm that constructs a combination of classifiers to achieve accurate classification decision. In this respect, the aim of this study is to propose classification method for diagnosis of neuromuscular disorders including Amyotrophic lateral sclerosis (ALS) and myopathy diseases, through using publicly available EMG data, recorded by intramuscular single-channel EMG electrode. The ensemble of bagged tree algorithm generates and averages multiple versions of decision tree predicatore that are formed by producing a bootstrap replicate of the EMG learning data set. A set of ten time-domain features extracted from the intramuscular EMG recordings are classified by the ensemble of bagged tree algorithm. A classification accuracy rate of 92.8% was obtained with the proposed bagged tree classifier, which reports high classification accuracy. The outcome of this study can assist the clinicians to decide the correct diagnosis of neuromuscular disorders.