CLASSIFICATION OF AMYOTROPHIC LATERAL SCLEROSIS AND HEALTHY ELECTROMYOGRAPHY SIGNALS BASED ON TRANSFER LEARNING

Year 2018, , 179 - 185, 29.12.2018

Abdulkadir Şengür , Ümit Budak , Yaman Akbulut

https://doi.org/10.36222/ejt.498095

Cited By: 2

Abstract

This paper investigates the usage of
transfer learning in amyotrophic lateral sclerosis (ALS) disease detection. ALS
is a dangerous disease which affects the nerve cells in brain and spinal cord.
Electromyogram (EMG) is an important measure for analysing of the electrical
level of the muscles. EMG based early ALS disease detection system helps the
physicians and patients. The proposed work uses EMG signals in discrimination
of the ALS and healthy persons. The EMG signals are initially segmented with a
overlapped window and each segment is converted to the spectrogram images. The
obtained spectrogram images are resized and fed into the pre-trained
convolutional neural networks model. The pre-trained model is fine-tuned with
the problem at hand. The R002 dataset which is obtained from www.emglab.net is
used during the experimental works. Accuracy, sensitivity and specificity
measures are used to evaluate the obtained achievement. According to these
measures, 97.70% accuracy, 97.97% sensitivity, and 97.29% specificity values
are recorded. We further compare the obtained results with some of the existing
results that were obtained on the same dataset. The comparisons show that
proposed method is outperformed.

Keywords

EMG signals, ALS disease, Transfer learning, Convolutional neural networks, Pre-trained models

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