SavedAdd your journal

Journals / European Journal of Technique / 2018 / Cilt: 8 - Sayı: 2

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

Abdulkadir ŞENGÜR
Ümit BUDAK
Yaman AKBULUT
Pages
179–185
Publication Date
2018-12-29
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The comparisons show that proposed method is outperformed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper investigates the usage of transfer learning in amyotrophic lateral sclerosis (ALS) disease detection.
📊
Key Findings & Data:The R002 dataset which is obtained from www.emglab.net is used during the experimental works.
💡
Conclusion & Impact:The comparisons show that proposed method is outperformed.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:EMG signals · ALS disease · 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.

🔖

Cite This Article

ŞENGÜR, A., BUDAK, Ü., AKBULUT, Y. (2018). CLASSIFICATION OF AMYOTROPHIC LATERAL SCLEROSIS AND HEALTHY ELECTROMYOGRAPHY SIGNALS BASED ON TRANSFER LEARNING. European Journal of Technique, Cilt: 8 - Sayı: 2, 179–185. https://ajindex.com/articles/201973

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Fuglsang‐Frederiksen, A., The utility of interference pattern analysis, Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine, 23(1), 2000, pp. 18-36.
  2. Fukuda, T. Y. et al., Root mean square value of the electromyographic signal in the isometric torque of the quadriceps, hamstrings and brachial biceps muscles in female subjects, Journal of Applied Research, 10(1), 2010, pp. 32-39.
  3. Fattah, S. A. et al., Evaluation of different time and frequency domain features of motor neuron and musculoskeletal diseases, Int. J. Comput. Appl., 43(23), 2012, pp. 34–40.
  4. Doulah, A. B. M. S. U., Fattah, S. A., Neuromuscular disease classification based on mel frequency cepstrum of motor unit action potential, In Electrical Engineering and Information & Communication Technology (ICEEICT), 2014 International Conference on IEEE, pp. 1-4.
  5. Mishra, V. K. et al., Analysis of ALS and normal EMG signals based on empirical mode decomposition, IET Science, Measurement & Technology, 10(8), 2016, pp. 963-971.
  6. Sengur, A. et al., DeepEMGNet: an application for efficient discrimination of ALS and normal EMG signals, T. Bvrezina, R. Jabłoński (Eds.), Mechatronics 2017
  7. Recent Technol. Sci. Adv., Springer International Publishing, Cham (2018), pp. 619-625, 10.1007/978-3-319-65960-2_77. Fattah, S. A. et al., Identification of motor neuron disease using wavelet domain features extracted from EMG signal, In Circuits and Systems (ISCAS), International Symposium on IEEE, 2013, pp. 1308-1311.
  8. Pal, P. et al., Feature extraction for evaluation of Muscular Atrophy, In Computational Intelligence and Computing Research (ICCIC), International Conference on IEEE, December 2010, pp. 1-4.
  9. Merlo, A. et al., A fast and reliable technique for muscle activity detection from surface EMG signals, IEEE Transactions on Biomedical Engineering, 50(3), 20113, pp. 316-323.
  10. Sengur, A. et al., Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm, Health information science and systems, 5(1), 2017, 9. Doi: 10.1007/s13755-017-0029-6
  11. Orenstein, E. C., Beijbom, O., Transfer Learning and Deep Feature Extraction for Planktonic Image Data Sets, In Applications of Computer Vision (WACV), Winter Conference on IEEE, March 2017, pp. 1082-1088.
  12. Krizhevsky, A. et al., Imagenet classification with deep convolutional neural networks, In Advances in neural information processing systems, 2012, pp. 1097-1105.
  13. Nikolic, M.