Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm

Abdulkadir Sengur; Yaman Akbulut; Yanhui Guo; Varun Bajaj

doi:10.1007/s13755-017-0029-6

Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm

Health Inf Sci Syst. 2017 Oct 30;5(1):9. doi: 10.1007/s13755-017-0029-6. eCollection 2017 Dec.

Authors

Abdulkadir Sengur¹, Yaman Akbulut¹, Yanhui Guo², Varun Bajaj³

Affiliations

¹ Department of Electrical and Electronics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
² Department of Computer Science, University of Illinois at Springfield, Springfield, IL USA.
³ Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India.

Abstract

Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features. In it, Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The CNN architecture is trained with the reinforcement sample learning strategy. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.80% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method.

Keywords: Convolutional neural networks; Electromyogram (EMG) signals; Reinforcement sample learning and amyotrophic lateral sclerosis (ALS); Time–frequency representation.