Noise Detection for Biosignals Using Orthogonal Wavelet Packet Tree Denoising Algorithm

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)