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

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.