Noise Detection for Biosignals Using an Orthogonal Wavelet Packet Tree Denoising Algorithm

• Autorzy: Schimmack M. , Mercorelli P.

Identyfikatory

DOI

10.1515/eletel-2016-0002

• Języki publikacji: EN

Abstrakty

EN

This article deals with the noise detection of discrete biosignals using an orthogonal wavelet packet. In specific, it compares the usefulness of Daubechies wavelets with different vanishing moments for the denoising and compression of the digitalised biosignals in case of surface electromyography (sEMG) signals. The work is based upon the discrete wavelet transform (DWT) version of wavelet package transform (WPT). A noise reducing algorithm is proposed to detect unavoidable noise in the acquired data in a model independent way. The noise of a signal sequence will be defined by a seminorm. This method was developed for a possible observation during a fracture healing period. The proposed method is general for signal processing and its design was based upon the wavelet packet.

Słowa kluczowe

EN

noise detection; wavelet packet transform; wavelet analysis; Daubechies wavelet; linux-based embedded system; ARM processor platform

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2016
• Tom: Vol. 62, No. 1
• Strony: 15--21
• Opis fizyczny: Bibliogr. 15 poz., rys., wykr.

Twórcy

autor

Schimmack M.

• Institute of Product and Process Innovation, Leuphana University of Lueneburg, Volgershall 1, D-21339 Lueneburg, Germany

autor

Mercorelli P.

• Institute of Product and Process Innovation, Leuphana University of Lueneburg, Volgershall 1, D-21339 Lueneburg, Germany

Bibliografia

• [1] S. Neville and N. Dimopoulos. Wavelet denoising of coarsely quantized signals. IEEE Transactions on Instrumentation and Measurement, 55(3):892-901, 2006.
• [2] J. Tomaszewski, T. G. Amaral, O.P. Dias, A. Wolczowski, and M. Kurzynski. EMG signal classification using neural network with AR model coefficients methods and models in automation and robotics. 14th International Conference on Methods and Models in Automation and Robotics, 14(1):318-325, 2009.
• [3] M. Schimmack and P. Mercorelli. Linux-based embedded system for wavelet denoising and monitoring of semg signals using an axiomatic seminorm. In IFAC International Conference on Programmable Devices and Embedded Systems, pages 278-283, Cracow, 2015.
• [4] M. Schimmack, A. Hand, P. Mercorelli, and A. Georgiadis. Using a seminorm for wavelet denoising of sEMG signals for monitoring during rehabilitation with embedded orthosis system. In IEEE MeMeA - International Symposium on Medical Measurements and Applications, Italy, 2015.
• [5] S. Shahid, J. Walker, G. M. Lyons, C. A. Byrne, and A. V. Nene. Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential. IEEE Transactions on Biomedical Engineering, 52(7):1195-1209, 2005.
• [6] C.J.D. Luca. Physiology and mathematics of myoelectrical signals. IEEE Transactions on Biomedical Engineering, 26(6):313-325, 1979.
• [7] M. Unser and A. Aldroubi. A review of wavelets in biomedical applications. Proceedings of the IEEE, 84(4):626-638, 1996.
• [8] W. Rakowski. Prefiltering in wavelet analysis applying cubic B-splines. 14th International Conference on Methods and Models in Automation and Robotics, 60(4):331-340, 2014.
• [9] A. Frick and P. Mercorelli. System and methodology for noise level estimation by using wavelet basis functions in wavelet packet trees. European Patent Office under publication number: DE10225344, 2002.
• [10] P. Mercorelli and A. Frick. Noise Level Estimation Using Haar Wavelet Packet Trees for Sensor Robust Outlier Detection. Series: Lecture Note in Computer Sciences, Springer-Verlag publishers, 2006.
• [11] J. Buckheit, S. Chen, D. Donoho, I. Johnstone, and J. Scargle. About wavelab. Handbook of WaveLab Version .850 by Standford University and NASA-Ames Research Center, pages 1-37, 2005.
• [12] A. Phinyomark, A. Nuidod, P. Phukpattaranont, and C. Limsakul. Feature extraction and reduction of wavelet transform coefficients for emg pattern classification. Electronics and Electrical Engineering, 122(6):27-32, 2012.
• [13] C.-F. Jiang, Y.-C. Lin, and N.-Y. Yu. Multi-scale surface lectromyography modeling to identify changes in neuromuscular activation with myofascial pain. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(1):89-95, 2013.
• [14] D. K. Kumar, N.D. Pah, and A. Bradley. Wavelet analysis of surface electromyography to determine muscle fatigue. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(4):400-406, 2003.
• [15] I. Daubechies. Ten Lectures On Wavelets. SIAM: Society For Industrial And Applied Mathematics, 1992.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.