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#+TITLE: EEG Micrcontinuity lite
#+AUTHOR: Andrei Zavada
#+EMAIL: johnhommer@gmail.com
#+LANGUAGE: en
#+OPTIONS: toc:nil num:nil LaTeX:t
#+LINK_UP:
#+LINK_HOME: aghermann.html
* EEG Microcontinuity
The concept of EEG microcontinuity, as described in the [[http://www.ncbi.nlm.nih.gov/pubmed/11008419][original
paper]] by Kemp et al (2000), proposes a new SWA metric which is,
citing from the Abstract, "the fraction (0%-100%) of the current
slow wave which continues in the near-future (0.02 s later) EEG".
Ths metric in Aghermann is implemented by translating relevant bits
from the C# code [[http://code.google.com/p/neuroloopgain/][published]] by Marco Roessen, who in turn did the
coding of the original concept in collaboration with Bob Kemp.
The algorithm, following the logic and comments in said C# code,
proceeds like so:
1. Perform SU and SS reduction;
2. Compute PiB value;
3. Detect artifacts;
4. Smooth SU and SS;
5. Detect events;
6. Re-smooth signals and detecting jumps;
7. Compute final gains.
In doing my part, I got stuck in the C# thicket just after step 2.
Eventually, as part of a debugging effort, I noticed that the
intermediate results coming out after step 1, bore remarkable
semblance to the ultimate course of "SW%" as it appears on fig. 2
(chart 2).
Step 1, thus, produces the "lite" SW% metric, at each page p, as
SS[p] - SU[p], where SS and SU are computed as shown in eq. 22 of
the cited paper. Note that these values are computed over the
entire page length (typically, 30 sec) rather than the shorter 1-sec
intervals used in the paper.
For what it is, the SWA (or SW%) profiles of EEG Microcontinuity do
look handsome to me, nicely following the ultradian cycle and
expressing the SWA swing with clear emphasis. And it even elicits a
novel feature, which is a steadier buildup of the metric towards the
end of a slow-wave hump where PSD yields a more steep increase early
on into the period.
Please take this with a pinch of salt.
* Artifact detection
The SS-SU difference is used for *artifact detection*.
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