SEMIA, version 3.0.1, is a tool for viewing time series of diagnostic and morphology parameters of long-term ambulatory recordings, and ST segment annotations with their corresponding ECG waveforms of the Long-Term ST Database (LTST DB). SEMIA was created during the development of the LTST DB, a project supported by Medtronic, Inc. (Minneapolis, MN, USA) and Zymed, Inc. (Camarillo, CA, USA).
Compiling SEMIA
If you have not already installed the WFDB software package and the XView toolkit, do so now.
If you are running under GNU/Linux, you may not need to compile SEMIA. Try running the precompiled version of SEMIA available here. (You will still need to have installed WFDB and XView in order to do this successfully.)
If you need to compile SEMIA, make a directory for the SEMIA sources, then copy all files from this directory into it. (For convenience, you may download this gzip-compressed tar archive of all files in this directory rather than downloading the individual files.)
Enter your directory of SEMIA sources and type 'make'.
You may encounter many warnings related to improperly formatted comments in XView #include files. These warnings may be ignored. If you wish to eliminate them, download and apply this patch (read and follow the instructions at the beginning of the patch to see how to apply it).
When 'make' finishes, copy semia into a directory in your PATH, and copy semia.opt and semia.hlp into the directory where you wish to use SEMIA.
Using SEMIA
To run SEMIA successfully, the following files of the LTST DB are needed in the current working directory:
- snnnnn.hea (the header file for record snnnnn). This file also contains comments of human annotators, diagnostic data of the patient, and technical data of the record.
- snnnnn.dat (digitized ECGs for the record)
- snnnnn.cnt (numbers of annotated ST events and their durations according to annotation protocols A, B, and C, and numbers of normal and abnormal heart beats)
- snnnnn_fin.dmy (time series of "fine" diagnostic and morphology parameters obtained on average heart beats)
- snnnnn_raw.dmy (time series of "raw" (resampled - 0.5 samples/sec - and smoothed) diagnostic and morphology parameters)
- snnnnn_1.sta (ST annotation markers according to annotation protocol A)
The last three of these files are contained within the .tsr.zip files in the LTST DB directory on PhysioNet (or on the "Subset" CD-ROM). Download the .tsr.zip file and unpack it using a command such as:
unzip s20011.tsr.zip
In addition, you will need copies of semia.opt (a text file containing SEMIA's options) and semia.hlp (a text file containing on-line help for SEMIA) in the current working directory, as noted above.
Run SEMIA by typing "semia". Click on the "Help" button to view SEMIA's on-line manual. To open a record, click on "Open", then enter the record name (snnnnn only; do not include .hea or any other suffix in the record name). Depending on the speed of your system, it may take up to a minute for SEMIA to load the data after you click "OK".
Once the data have been loaded, several buttons that were initially inactive become available for use. Each of them ( "Lead0", "Lead1", "Lead2", "Data", and "KL") opens a window when clicked. ("Lead2" remains inactive unless you have opened a record with 3 ECG signals.)
- The "Lead" windows display time series of diagnostic parameters
(raw and fine ST level functions, raw and fine ST slope functions,
raw linearly interpolated ST reference functions, raw ST deviation
functions, raw and fine heart rate functions, and annotated ST
episodes) and ST segment annotations according to annotation
protocol A of the LTST DB.
Fine time series or functions are those obtained on average heart beats after noise detection during preprocessing phase. Raw time series or functions are those after resampling and smoothing of fine functions.
ST segment annotations are: global reference, local references that define the ST reference function, ST segment annotations indicating ischemic and heart-rate related ST episodes (beginnings, extrema, ends), axis shift annotations, conduction change annotations, noise annotations, and annotations indicating unreadable intervals (beginnings, ends).
Three different display modes for "Lead" windows are possible:
- Unsubtracted mode displaying ST level function (linearly interpolated ST reference function is not subtracted from the ST level function). In this mode, the active reference to compare ST segment waveforms is the global reference annotation.
- Unsubtracted mode displaying ST level function (linearly interpolated ST reference function is not subtracted from the ST level function). In this mode, the active reference to compare ST segment waveforms is a local reference annotation.
- Subtracted mode displaying ST deviation function (linearly interpolated ST reference function is subtracted from the ST level function to form the ST deviation function). In this mode, the active reference to compare ST segment waveforms is the global reference annotation
The ECG signal corresponding to current active reference annotation is displayed in the upper part of the "Data" window.
Time series displayed in the "Lead" windows can be examined by pointing them using mouse cursor or setting the Marker at the time instance of interest. The corresponding ECG signal is displayed in the lower part of the "Data" window.
ST segment annotations can be examined using the Exm option. The examined ST annotation becomes the currently active annotation, and its corresponding ECG signal is displayed in the lower part of the "Data" window as well.
The time scale may be adjusted to permit between 1 minute and 96 hours of data to be seen at once. Time series from "other" ECG leads may be overlaid in each "Lead" window.
- The "Data" window displays the ECG signal corresponding to the time
of the active reference annotation in the upper portion of the window,
and that corresponding to the time of the current active annotation or
marker in the lower portion of the window. These two ECG waveforms may be
overlaid. A variety of scales may be selected for comparing ST segment
morphologies. The time scale may be adjusted to permit between 1 and 60
seconds of data to be seen at once.
The center heart beat in each ECG trace is the beat average over the chosen time window. ECG signals from "other" ECG leads may be overlaid.
- The "KL" window displays time series of morphology (waveform shape)
parameters. Two sets of parameters (for the ST segment and the QRS complex)
can be displayed; the parameters are coefficients of a Karhunen-Loève
transform (principal components).
The ST segment or QRS complex distance function (Mahalanobis distance function) can be overlaid in the KL window.
References:
- Franc Jager, George B. Moody, Alessandro Taddei, Gorazd Antolic, Michele Emdin, Ales Smrdel, Boris Glavic, Carlo Marchesi, and Roger G. Mark. A Long-Term ST Database for Development and Evaluation of Ischemia Detectors. Computers in Cardiology 1998, pp. 301-304, IEEE Press. ISSN 0276-6547.
- Franc Jager, Alessandro Taddei, Michele Emdin, Gorazd Antolic, Roman Dorn, George B. Moody, Boris Glavic, Ales Smrdel, M Varanini, Mitja Zabukovec, Simone Bordigiago, Carlo Marchesi, and Roger G. Mark. The Long-Term ST Database: A Research Resource for Algorithm Development and Physiologic Studies of Transient Myocardial Ischemia. Computers in Cardiology 2000, pp. 841-844. IEEE Press. ISSN 0276-6547.
Name Last modified Size Description
Parent Directory - semia.opt 2002-05-11 07:07 81 configuration file semia 2002-08-01 22:57 250K SEMIA, precompiled for x86/Linux Makefile 2002-08-02 13:55 475 'make' description file COPYING 2003-03-11 06:29 18K semia.hlp 2003-03-11 06:29 16K How to use SEMIA semia_stubs.cc 2003-03-11 06:29 334K C++ source file semia_ui.cc 2003-03-11 06:29 65K C++ source file semia_ui.h 2003-03-11 06:29 9.8K C++ include file
If you would like help understanding, using, or downloading content, please see our Frequently Asked Questions. If you have any comments, feedback, or particular questions regarding this page, please send them to the webmaster. Comments and issues can also be raised on PhysioNet's GitHub page. Updated Friday, 28 October 2016 at 16:58 EDT |
PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09.
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