[med-svn] [SCM] aghermann branch, master, updated. 06bda7dfaa687aaf0708a024d192024e2cd58421

[andrei zavada]

The following commit has been merged in the master branch:
commit 93906b085294aa00cb49d7fff757ba0819702471
Author: andrei zavada <jh at johnhommer.com>
Date:   Wed Jan 23 19:14:30 2013 +0000

    update docs and ChangeLog

diff --git a/ChangeLog b/ChangeLog
index e6244ed..393e756 100644
--- a/ChangeLog
+++ b/ChangeLog
@@ -1,10 +1,14 @@
 v.0.8.0 (2013-01-xx)
 	* edfcat: better handling of overshooting signal portions.
 	* phasic events detection groundwork.
-	* Automatically mark flat signal portions as artifacts.
-	* SF: Find dialog now uses band-pass for target frequency
-	  matching.
-	* 
+	* SF: mark flat signal portions as artifacts.
+	* SF: reingeneered Patterns dialog:
+	  o use band-pass for target frequency matching;
+	  o system, user, experiment and subject-wide patterns:
+	  o obtain match index once, for instantaneous finding
+	    against variable criteria;
+	  o mark matches as annotations;
+	* Fix envelope function.
 
 v.0.7.6 (2012-12-20)
 	* Don't forget to save some .expdesign.conf settings.
diff --git a/doc/org/aghermann.org.in b/doc/org/aghermann.org.in
index b2eb2ee..7a66bd2 100644
--- a/doc/org/aghermann.org.in
+++ b/doc/org/aghermann.org.in
@@ -55,12 +55,14 @@ long sleepers, early /vs/ late, etc.
 
 *** Artifact detection
 
-   Artifact detection comes as a side-effect in the computation of [[http://www.ncbi.nlm.nih.gov/pubmed/11008419][EEG
+   *Artifact detection* comes as a side-effect in the computation of [[http://www.ncbi.nlm.nih.gov/pubmed/11008419][EEG
    Microcontinuity]], and may or may not meet your expectations.  Most
    parameters affecting artifact detection are exposed for your
    tuning. Run with default settings, it tends to be a little too
    eager at the task, though.
 
+   You can detect *flat signal* portions, too.
+
 *** Filters
 
    Butterworth Low-pass, high-pass and band-pass filters are
@@ -85,15 +87,16 @@ long sleepers, early /vs/ late, etc.
 
 *** Pattern finding
 
-    A pattern is characterized by its low-frequency component
-    (adjustable cutoff and filter order), its envelope (a tangential
-    line connecting local extrema, with adjustable 'tightness'), and a
-    density function of zerocrossings of signal derivative (with
-    variable sigma and sampling interval, interpolated).  Using these
-    criteria and some tuning, one can find occurrences of a pattern
-    (say, a K-complex) in the signal.
+    A *pattern* is characterized by a certain, band-pass filtered,
+    /target frequency component/, its /envelope/ (a tangential line
+    connecting local extrema over a certain range), and a /density
+    function of zerocrossings of signal derivative/ (with variable
+    sigma and sampling interval, interpolated).  Using these criteria
+    and some tuning, one can find occurrences of a pattern (say, a
+    K-complex) in the signal.
 
-    Patterns can be saved, and searched for in other subjects.
+    Patterns can be saved, and searched for in other subjects, and/or
+    in other experiments.
 
 *** Phase difference between channels
 

-- 
Sleep experiment manager