KaydedilenlerDerginizi ekleyin

Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2015 / Cilt: 23 - Sayı: 4

A comparative study of denoising sEMG signals

ULVİ BAŞPINAR
VOLKAN YUSUF ŞENYÜREK
BARIŞ DOĞAN
HÜSEYİN SELÇUK VAROL
Hüseyin Selçuk VAROL
Sayfa
931–944
Yayın Tarihi
2015-08-13
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Finally, 3 methods with the optimum parameters are applied to the real sEMG signal acquired from the flexor carpi radialis muscle and the visual results are presented.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Denoising of surface electromyography (sEMG) signals plays a vital role in sEMG-based mechatronics applications and diagnosis of muscular diseases.
📊
Temel Bulgular & Veri:In this study, 3 different denoising methods of sEMG signals, empirical mode decomposition, discrete wavelet transform (DWT), and median filter, are examined.
💡
Sonuç & Katkı:Finally, 3 methods with the optimum parameters are applied to the real sEMG signal acquired from the flexor carpi radialis muscle and the visual results are presented.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Surface electromyography · sEMG · empirical mode decomposition

Özet

Denoising of surface electromyography (sEMG) signals plays a vital role in sEMG-based mechatronics applications and diagnosis of muscular diseases. In this study, 3 different denoising methods of sEMG signals, empirical mode decomposition, discrete wavelet transform (DWT), and median filter, are examined. These methods are applied to 5 different levels of noise-added synthetic sEMG signals. For the DWT-based denoising technique, 40 different wavelet functions, 4 different threshold-selection-rules, and 2 threshold-methods are tested iteratively. Three different window-sized median filters are applied as well. The SNR values of denoised synthetic signals are calculated, and the results are used to select DWT and median filter method parameters. Finally, 3 methods with the optimum parameters are applied to the real sEMG signal acquired from the flexor carpi radialis muscle and the visual results are presented.

Abstract

Denoising of surface electromyography (sEMG) signals plays a vital role in sEMG-based mechatronics applications and diagnosis of muscular diseases. In this study, 3 different denoising methods of sEMG signals, empirical mode decomposition, discrete wavelet transform (DWT), and median filter, are examined. These methods are applied to 5 different levels of noise-added synthetic sEMG signals. For the DWT-based denoising technique, 40 different wavelet functions, 4 different threshold-selection-rules, and 2 threshold-methods are tested iteratively. Three different window-sized median filters are applied as well. The SNR values of denoised synthetic signals are calculated, and the results are used to select DWT and median filter method parameters. Finally, 3 methods with the optimum parameters are applied to the real sEMG signal acquired from the flexor carpi radialis muscle and the visual results are presented.

🔖

Bu Makaleye Atıf Yap

BAŞPINAR, U., ŞENYÜREK, V. Y., DOĞAN, B., VAROL, H. S., VAROL, H. S. (2015). A comparative study of denoising sEMG signals. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 23 - Sayı: 4, 931–944. https://ajindex.com/articles/187577

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (12)

  1. O. Fukuda, J. Kim, I. Nakai, Y. Ichikawa, “EMG control of a pneumatic 5-fingered hand using a Petri net”, Artificial Life and Robotics, Vol. 16, pp. 90–93, 2011. G.G. Guti´errez, C.B. L´opez, F. Navacerrada, A.M. Mart´ınez, “Use of electromyography in the diagnosis of inflam- matory myopathies”, Reumatolog´ıa Cl´ınica (English edition), Vol. 8, pp. 195–200, 2012.
  2. U. Baspinar, H.S. Varol, V.Y. Senyurek, “Performance comparison of artificial neural network and Gaussian mixture model in classifying hand motions by using sEMG signals”, Biocybernetics and Biomedical Engineering, Vol. 33, pp. 33–45, 2013. B. Karlık, “Differentiating type of muscle movement via AR modeling and neural network classification”, Turkish Journal of Electrical Engineering, Vol. 7, pp. 45–52, 1999.
  3. E. Huigen, A. Peper, C. Grimbergen, “Investigation into the origin of the noise of surface electrodes”, Medical and Biological Engineering and Computing, Vol. 40, pp. 332–338, 2002.
  4. C.J. De Luca, L. Donald Gilmore, M. Kuznetsov, S.H. Roy, “Filtering the surface EMG signal: movement artifact and baseline noise contamination”, Journal of Biomechanics, Vol. 43, pp. 1573–1579, 2010.
  5. M.B.I. Reaz, M. Hussain, F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications”, Biological Procedures Online, Vol. 8, pp. 11–35, 2006.
  6. N. Gallagher, G. Wise, “A theoretical analysis of the properties of median filters”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 29, pp. 1136–1141, 1981.
  7. T. Nodes, N. Gallagher, “Median filters: some modifications and their properties”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 30, pp. 739–746, 1982.
  8. C.F. Jiang, S.L. Kuo, “A comparative study of wavelet denoising of surface electromyographic signals”, 29th Annual International IEEE Conference on Engineering in Medicine and Biology Society, pp. 1868–1871, 2007.
  9. A.O. Andrade, S. Nasuto, P. Kyberd, C.M. Sweeney-Reed, F. Van Kanijn, “EMG signal filtering based on empirical mode decomposition”, Biomedical Signal Processing and Control, Vol. 1, pp. 44–55, 2006.
  10. A. Karagiannis, P. Constantinou, “Noise-assisted data processing with empirical mode decomposition in biomedical signals”, IEEE Transactions on Information Technology in Biomedicine, Vol. 15, pp. 11–18, 2011.
  11. M. Kania, M. Fereniec, R. Maniewski, “Wavelet denoising for multi-lead high resolution ECG signals”, Measurement Science Review, Vol. 7, pp. 30–33, 2007.
  12. N. Chatlani, J.J. Soragha