Noise Detection for Biosignals Using Orthogonal Wavelet Packet Tree Denoising Algorithm

Manuel Schimmack, Paolo Mercorelli

Abstract


This article deals with the noise detection of discrete biosignals using orthogonal wavelet packet. More specifically, it compares the usefullness of Daubechies wavelets with different vanishing moments for the denoising and compression of the digitalized surface electromyography (sEMG). 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. With the help of a seminorm the noise of a sequence is defined. Using this norm it is possible to rearrange the wavelet basis, which can illuminate the differences between the coherent and incoherent parts of the sequence, where incoherent refers to the part of the signal that has either no information or contradictory information. In effect, the procedure looks for the subspace characterised either by small components or by opposing components in the wavelet domain. This method was developed for the monitoring during rehabilitation. The proposed method is general for signal processing and was built based on the wavelet packet from the WaveLab 850 library of the Stanford University (USA).

Full Text:

PDF

References


P. Mercorelli. Biorthogonal wavelet trees in the classification of

embedded signal classes for intelligent sensors using machine learning

applications. Journal of the Franklin Institute, 344(6):813–829, 2007.

W. Rakowski. Prefiltering in wavelet analysis applying cubic B-splines.

th International Conference on Methods and Models in Automation

and Robotics, 60(4):331–340, 2014.

S. Neville and N. Dimopoulos. Wavelet denoising of coarsely quantized

signals. IEEE Transactions on Instrumentation and Measurement,

(3):892–901, 2006.

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.

C.J.D. Luca. Physiology and mathematics of myoelectrical signals. IEEE

Transactions on Biomedical Engineering, 26(6):313–325, 1979.

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.

th International Conference on Methods and Models in Automation

and Robotics, 14(1):318–325, 2009.

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.

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.

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.

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.

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,

(6):27–32, 2012.

C.-F. Jiang, Y.-C. Lin, and N.-Y. Yu. Multi-scale surface electromyo-

graphy modeling to identify changes in neuromuscular activation with

myofascial pain. IEEE Transactions on Neural Systems and Rehabilita-

tion Engineering, 21(1):89–95, 2013.

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.

I. Daubechies. Ten Lectures On Wavelets. SIAM: Society For Industrial

And Applied Mathematics, 1992.


Refbacks

  • There are currently no refbacks.


International Journal of Electronics and Telecommunications
is a periodical of Electronics and Telecommunications Committee
of Polish Academy of Sciences

eISSN: 2300-1933