Electromyography Signal Classification Using Deep Learning
Mekia Shigute Gaso Selcuk Cankurt Abdulhamit Subasi
We have implemented a deep learning model with L2 regularization and trained
it on Electromyography (EMG) data. The data comprises of EMG signals collected
from control group, myopathy and ALS patients. Our proposed deep neural network
consists of eight layers; five fully connected, two batch normalization and one
dropout layers. The data is divided into training and testing sections by
subsequently dividing the training data into sub-training and validation
sections. Having implemented this model, an accuracy of 99 percent is achieved
on the test data set. The model was able to distinguishes the normal cases
(control group) from the others at a precision of 100 percent and classify the
myopathy and ALS with high accuracy of 97.4 and 98.2 percents, respectively.
Thus we believe that, this highly improved classification accuracies will be
beneficial for their use in the clinical diagnosis of neuromuscular disorders.