By: Can Ozan Tan, J. Andrew
Taylor, Albert S. H. Ler, and Michael A. Cohen
IEEE Transactions on
Biomedical Engineering, 2009
Regional blood flow is primarily controlled by
sympathetic nervous outflow to the vasculature, and when dysregulation occurs
pathological conditions such as sleep apnea, hypertension, heart failure, and
metabolic syndrome can arise. Therefore, it is important to cardiovascular
research to quantify the sympathetic activity. Within traditional human
sympathetic nerve recordings, there are three stages: bandpass filtering,
full-wave rectification, and integration with a slow time constant. These are
all used to purify the signal and obtain action potentials. Bandpass filtering
removes low-frequency content of the signal that is possibly physiologically
relevant. Full-wave rectification yields artificial amplification of the
filtered recording, and integration averages the sympathetic activity over
time. In past years, the signals were read “by hand” and experts had to pick
out the specific action potentials themselves. To bypass potential problems
with this, wavelet decomposition methods have been designed to denoise
sympathetic activity recordings and improve quantification. The primary aim of
this study was to derive a generic, fully automated technique for restoring
raw, multifiber nerve signals that were buried in high levels of noise and
other artifacts.
To achieve this, researchers proposed an algorithm to
minimize signal degradation and it was sensitive to different firing modes of
multifiber signals. This algorithm followed the basic steps of 1. Removing line
noise and movement artifacts from the recording, 2. Using the characteristic
cardiac rhythmicity of sympathetic firing to identify action potential
candidates and create a mean action potential template (helped identify noise
spikes) and 3. Identify actual spikes and background noise. To validate the technique,
the researchers produced 5 minute long 10-KHz artificial datasets with various
signal-to-noise ratios (SNR). The two types of common artifacts within the raw
multifiber recordings were 60-Hz line noise and movement artifacts. The 60-Hz
line noise was removed by notch filtering and the movement artifacts were
removed by clipping cutoff points in certain quartiles of the normal
distribution to fit the time domain. In addition, they considered that bursts
within the recording that exceeded 2.5 standard deviations of the whole
neurogram were most likely the ones that contained the action potentials.
The results of the study showed that the new technique
produced performance improvement that was statistically significant. To further
test the algorithm, they applied to it to renal sympathetic nerve recordings
from freely moving rats under baseline conditions. High SNR ensured that there
was successful detection of nerve activity, despite the fact that they had a
low sampling rate. This showed that they could use the algorithm in species
other than humans, and also apply it to multifiber recordings from different
types of nerves.
The researchers successfully described a new algorithm
that is capable of restoring multifiber nerve signals buried within levels of
background noise. They did consider a few limitations: 1. When the rate goes
above 400 spikes per second, their methods tended to overestimate the number of
spikes and 2. The method requires a relatively high sampling rate for
recordings. Overall, the artificial datasets produced results that showed the
algorithm providing an accurate measure of nerve signal, and it actually
provided better quantification of the sympathetic signal compared to
traditional methods. The major improvements with the new algorithm include that
it is able to minimize signal degradation and retain more information about
nerve firing.
This
is important to our lab because the algorithm is capable of detecting nerve
signals in recordings that are heavily contaminated with background noise.
Sending our SSNA recordings to this lab will allow them to help us determine if
glutamate activation of the RVLM causes more neurons to fire within the nerve,
or if it causes the same neurons to fire faster.
-L. Matus
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