Wavelet Project

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Analysis of Muscle Sympathetic Nerve Activity with Wavelet method

 

 

Muscle Sympathetic Nerve Activity (MSNA) can be recorded using microneurography of the peroneal nerve. A thin tungsten needle will be inserted into the nerve. The signal will be amplified and filtered by a high precision analog amplifier (factor 999,999) and bandpass filter (0.7-2kHz). The Filtered Muscle Sympathetic Activity (FMSNA) signal consists of noise and neural spike events. Episodes of more frequent spikes with higher amplitude (bursts) characterize the level of sympathetic activation of the nervous system. The FMSNA signal will still not analyze digitally, because of necessary high sample frequency of this signal (greater equal 5000 Hz) and required high computional requirements. A common method for analyzing Muscle Sympathetic Activity is to integrate the FMSNA signal by an analog integrator with a time window length of 0.1 sec and detect the burst in the Integrated Muscle Nerve Activity (IMSNA) signal by visually criteria or by computerized automatic burst detection algorithms. The analysis of IMSNA was developed intuitively and arbitrarily and has limitations.

 

Wavelets are mathematical function that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They have advantages over traditional Fourier methods. Discontinuities and function with sharp spikes usually take substantially fewer basic functions than sine-cosine based functions to achieve a comparable approximation. The computational complexity is much less using Wavelets than Fast Fourier Transform (n operations instead of n*log2(n) ). Wavelets were used for de-noising signals and data extraction. Application of wavelets in medicine can be found in image processing, ECG analysis, etc.

 

The aim of this project is to use a wavelet method for data extraction and estimation of the sympathetic nervous activity level in the filtered Muscle Sympathetic Nerve signal with the purpose to use this algorithm in a real time digital signal processing system.

 

 

 

References:

  1. Coifman, R. R. and M. V. Wickerhausen. Wavelets and Adapted Waveform Analysis. 1997. Source: WWW
  2. Perrier, V. and T. Philopovitch. Wavelet Spectra compared to Fourier Spectra. 1993. Source: WWW.
  3. Vidakovic, B. and P. Müller. Wavelets for Kids. AMS Subject Classification 42A06,41A05,65D05: 1991. Source WWW.