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heartbeats [2010/05/28 05:14]
jochen typo
heartbeats [2010/06/29 19:17] (current)
jochen updated help
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 ===== Requirements ===== ===== Requirements =====
 The raw signal (ECG channel recording) must be available in a Matlab variable. For the purpose of this manual, this variable is called **data**. The raw signal (ECG channel recording) must be available in a Matlab variable. For the purpose of this manual, this variable is called **data**.
 +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>''​ 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) ===== ===== Reference (help) =====
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 <​file> ​ heartbeats ​ - detect heart beats and frequency in physio data <​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:   Input fields:
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         sig         Sx1 numeric signal         sig         Sx1 numeric signal
         opts        optional settings         opts        optional settings
 +         ​.badt ​     t-threshold for detecing irregularity (default: 25)
          ​.bppre ​    ​pre-defined positions (no detection, only inspection)          ​.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)          ​.cleanup ​  ​interactive cleanup (default: false)
          ​.detlength detection length threshold in seconds (default: 0.05)          ​.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)          ​.pflength ​ pre-filter length in seconds (default: 0.025)
          ​.pfreps ​   pre-filter repetitions (default: 2)          ​.pfreps ​   pre-filter repetitions (default: 2)
-         ​.freq ​     data frequency in Hz (default: 1000) 
          ​.plot ​     plot mean +/- std estimate of signal (default: false)          ​.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)          ​.segsize ​  ​segmentation size in seconds (default: 5)
          ​.segstep ​  ​stepping (window shift) in seconds (default: 1)          ​.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:   Output fields:
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         wgd         guess whether window is good or not         wgd         guess whether window is good or not
         wd          windowed data (in 100Hz resolution, interpolated)         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 ===== ===== Usage overview =====
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     heartbeats(data,​ struct('​cleanup',​ true, '​plot',​ true));</​code>​     heartbeats(data,​ struct('​cleanup',​ true, '​plot',​ true));</​code>​
  
-This will perform the following steps:+This will perform the following steps (if no pre-defined beat positions are given):
   * windowed z-transform (in segment windows with configured stepsize, also works as low-pass filter!)   * windowed z-transform (in segment windows with configured stepsize, also works as low-pass filter!)
   * windsorizing (cutting off over-large peaks)   * windsorizing (cutting off over-large peaks)
   * filtering (low-pass, to get rid of very high frequency noise)   * filtering (low-pass, to get rid of very high frequency noise)
-  * skew-flipping (so that peaks always are on the positive tail of the distribution)+  * possible ​skew-flipping (to ensure ​that peaks always are towards ​the positive tail of the distribution)
   * segment-wise beat detection (maximum value position within each sub-segment where value > mean + 0.5 * skew)   * segment-wise beat detection (maximum value position within each sub-segment where value > mean + 0.5 * skew)
 +  * if not enough beats are found (currently less than 6 per minute overall) the best general fit over the auto-correlation is computed and beats are completely re-estimated
  
-Next, the detected (or provided) beats will be analyzed, and any irregularities ​(that is to say, places where either the distance ​between beats is too small or too large or the shape of the detected beat does not match the mean signature) will be displayed, one by one, in a small dialog:+Next, the detected (or provided) beats will be analyzed ​as follows: 
 +  * the robust mean over the distance of beats (heartrate) is computed 
 +  * any beat closer together than 1/3 of that distance ​is removed 
 +  * any gap larger than 3 times that distance is automatically filled up (at located within segments) 
 +  * each segment surrounding any given beat will be resampled to 101 values (fixed resolution) 
 +  * the mean over segments with a suitable length will be taken as the reference 
 +  * a t-statistic is computed for how much variance in each of the segments is accounted for (better scaled than r or F) 
 +  * all segments below the specified threshold (default t=25) will be marked as irregular
  
-{{:heartbeats_cleanup.png|heartbeats cleanup dialog}}+Any of these irregularities (that is to say, places where either the distance between beats is too small or too large or the shape of the detected beat does not match the mean signature) will be displayed, one by one, in a small dialog: 
 + 
 +{{:heartbeats_cleanup_v2.png|heartbeats cleanup dialog}} 
 + 
 +Some info on the controls: 
 +  * the larger time course plot shows an excerpt of the provided signal (data) around the detected irregularity 
 +  * the smaller time course plot gives the entire signal (noise estimate) along with indicators of irregularities (blue lines) and the current position (green line); this can be used to directly visit a problematic area by clicking into this smaller time course plot 
 +  * the list box contains the positions of detected (accepted) beats in the window of the larger time course excerpt 
 +  * the buttons below can be used to remove beats from the list and to navigate to the previous or following irregularity 
 +  * the checkbox below the buttons switches between raw and filtered timecourse (only available if filtering was performed initially)
  
 The operation is relatively simple: The operation is relatively simple:
   * to add a beat in the stream, simply press the mouse button close to the desired peak location (which will be determined in a +/- 100ms window around the clicked spot)   * to add a beat in the stream, simply press the mouse button close to the desired peak location (which will be determined in a +/- 100ms window around the clicked spot)
   * to remove a beat (or several beats), select those in the dropdown box and press the "​Remove selected"​ button   * to remove a beat (or several beats), select those in the dropdown box and press the "​Remove selected"​ button
-  * once all beats in this window are correct, ​press the accept button+  ​* to remove all beats in the current window, press the "​Remove all" button 
 +  ​* once all beats in this window are correct, ​you can either move backward (<<) or forward (>>) in time, or click into the overview timecourse
  
-If no more irregularities remain to be checked, the dialog will close automatically. If you wish, you can also manually close the dialog (prematurely) to go on to the next stage.+If no more irregularities remain to be checked ​(after clicking next, >>, when the last irregularity was displayed), the dialog will close automatically. If you wish, you can also manually close the dialog (prematurely) to go on to the next stage.
  
 In case the plotting has been enabled, the following two displays appear: In case the plotting has been enabled, the following two displays appear:
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 {{:​ecg_and_hr_zoomed.png|heartbeats plot figure 2 - zoomed}}. {{:​ecg_and_hr_zoomed.png|heartbeats plot figure 2 - zoomed}}.
 +
 +==== Problems ====
 +Sometimes, the data is **very** noisy (like this):
 +
 +{{:​heartbeats_cleanup_problem.png|Noisy ECG example}}
 +
 +At this point, the function (heartbeats) does not yet have any pre-processing algorithms. After consultation with colleagues, I might decide to add those functions in a future version.
  
 ===== Scripting ===== ===== Scripting =====
 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). 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>​
heartbeats.1275016480.txt.gz · Last modified: 2010/05/28 05:14 by jochen