Differences

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--- heartbeats [2010/05/28 23:45] – Missing paranthesis jochen
+++ heartbeats [2010/06/22 21:18] – added instruction on how to load a MAT file to this page also jochen
@@ Line 8: / Line 8: @@
 The [[xff]] IO reading class now supports reading the following formats:
   * ACQ (up until version <= 3.9.7)
-  * TXT (use ''<nowiki>object = xff('*.ntt');</nowiki>'' to read!)
+  * TXT (use ''<nowiki>object = xff('*.ntt');</nowiki>'' or ''<nowiki>object = xff(filename, 'ntt');</nowiki>'' to read!)
 and further formats might be added based on request and urgency.
+In case the data is in a different format, you must ensure to first convert into one of the formats above or into a MAT file, which then can be read used like this:
+<code matlab heartbeats_readmat.m>% load a mat file (e.g. an ACQ->MAT converted file)
+load HPS1344_session1_ECG.mat;
+% create new NTT (used for methods on data!)
+ntt = xff('new:ntt');
+% store data from mat file in ntt
+ntt.Data = data;</code>
 ===== Reference (help) =====
@@ Line 29: / Line 40: @@
          .freq      data frequency in Hz (default: 1000)
          .plot      plot mean +/- std estimate of signal (default: false)
+         .plotfreq  samples per second to plot
          .plotwin   plot window size in seconds (default: 6)
          .segsize   segmentation size in seconds (default: 5)
          .segstep   stepping (window shift) in seconds (default: 1)
+         .skewdt    skewness detection threshold multiplier (default: 0.5)
          .windsor   windsorizing threshold in std's (default: 3)
@@ Line 110: / Line 123: @@
 Naturally, it is possible to script this function, save the pre-detected heartbeats (without manual interaction/plotting) and then, at a later time, revisit the inspection (for instance, if several subjects' datasets are to be examined, it usually is preferable to perform the extraction and detection of all datasets prior to engaging in the fine tuning).
+For instance, if the raw signal looks like this
+{{:heartbeats_crisp_example.png|crisp raw signal with very short spikes}}
+A two-pass detection scheme can be employed:
+<code matlab heartbeats_crisp_detection.m>% first, load the data
+data = xff('*.ntt');
+% then z-transform the third column (in our case) and take the 4th power
+pdata = ztrans(data.Data(:, 3)) .^ 4;
+% pre-detect beats
+% since we used the 4th power, the skew detection threshold must be lowered
+% and our signal has short spikes, so the detection length threshold also!
+bp = heartbeats(pdata, struct( ...
+    'skewdt',    0.05, ...
+    'detlength', 0.01, ...
+    'freq', 500));
+% then pass this along with the actual signal back in
+[bp, bs, bf, bv, cp, wgd, wd] = heartbeats(data.Data(:, 3), struct( ...
+    'bppre',   bp, ...
+    'cleanup', true, ...
+    'freq',    500, ...
+    'plot',    true));</code>