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--- heartbeats [2010/05/28 23:44] – corrected for linebreak jochen
+++ heartbeats [2010/06/29 17:17] (current) – updated help 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 15: / Line 26: @@
 <file>  heartbeats  - detect heart beats and frequency in physio data
-  FORMAT:       [bp, bs, bf, bv, cp, wgd, wd] = heartbeats(sig [, opts])
+  FORMAT:       [bp, bs, bf, bv, cp, wgd, wd, hrv] = heartbeats(sig [, opts])
   Input fields:
@@ Line 23: / Line 34: @@
          .badt      t-threshold for detecing irregularity (default: 25)
          .bppre     pre-defined positions (no detection, only inspection)
+         .calc      preprocessing calculcation, one of
+                    {'none'} - don't to anything (default)
+                    {'absdiff'} - use abs(diff(data))
+                        if .detlength is not given, will be set to 0.02
+                        if .skewdt is not given, will be set to 0.1
+                    {'diffsq'} - square the diff of the data
+                        if .detlength is not given, will be set to 0.01
+                        if .skewdt is not given, will be set to 0.05
+                    {'fourthz'} - fourth power of the z-transformed data
+                        if .detlength is not given, will be set to 0.02
+                        if .skewdt is not given, will be set to 0.04
+                    {'squarez'} - square the z-transformed data
+                        if .detlength is not given, will be set to 0.03
+                        if .skewdt is not given, will be set to 0.1
+                    {'thirdz'} - third power of the abs z-transformed data
+                        if .detlength is not given, will be set to 0.02
+                        if .skewdt is not given, will be set to 0.06
          .cleanup   interactive cleanup (default: false)
          .detlength detection length threshold in seconds (default: 0.05)
+         .freq      data frequency in Hz (default: 1000)
          .pflength  pre-filter length in seconds (default: 0.025)
          .pfreps    pre-filter repetitions (default: 2)
-         .freq      data frequency in Hz (default: 1000)
          .plot      plot mean +/- std estimate of signal (default: false)
+         .plotfreq  samples per second to plot (default: 50)
          .plotwin   plot window size in seconds (default: 6)
+         .resfreq   resample data prior to detection (default: [])
          .segsize   segmentation size in seconds (default: 5)
          .segstep   stepping (window shift) in seconds (default: 1)
-         .windsor   windsorizing threshold in std's (default: 3)
+         .skewdt    skewness detection threshold multiplier (default: 0.5)
+         .winsor    winsorizing threshold in std's (default: 3)
   Output fields:
@@ Line 43: / Line 74: @@
         wgd         guess whether window is good or not
         wd          windowed data (in 100Hz resolution, interpolated)
+        hrv         output of computehrv(bp)
-  Note: this function is still preliminary</file>
+  Note: this function is still preliminary, other options passed on to
+        computehrv (if 8th output is requested), with .hrvrfreq being
+        set to .resfreq</file>
 ===== Usage overview =====
@@ Line 110: / Line 144: @@
 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>