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The Long-Term ST Database

The Long-Term ST Database

The Long-Term ST Database:

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The Long-Term ST Database contains 86 lengthy ECG recordings of 80 human subjects, chosen to exhibit a multitude of events of ST segment switches, including ischemic ST scenes, axis-related non-ischemic ST gigs, gigs of slow ST level drift, and gigs containing mixtures of thesis phenomena. The database wasgoed created to support development and evaluation of algorithms capable of accurate differentiation of ischemic and non-ischemic ST events, spil well spil basic research into mechanisms and dynamics of myocardial ischemia.

Half (43) of thesis 86 recordings, indicating 42 of the 80 subjects, were contributed to PhysioNet by the creators of the database ter February 2003, and the remaining half of the database wasgoed contributed te May 2007. (A corrected version of s30801.dat wasgoed also posted together with the 2nd half of the database.) Detailed clinical notes and ST deviation trend plots are provided for all 86 records. The entire Long-Term ST Database is also available from its original huis pagina at the Laboratory for Biomedical Pc Systems and Imaging at the University of Ljubljana, Slovenia.

The individual recordings of the Long-Term ST Database are inbetween 21 and 24 hours ter duration, and contain two or three ECG signals. Each ECG signal has bot digitized at 250 samples vanaf 2nd with 12-bit resolution overheen a range of ±,Ten millivolts. Each record includes a set of meticulously verified ST gig and signal quality annotations, together with extra beat-by-beat QRS annotations and ST level measurements.

For each recording, the very first digit ter the record name (Two or Three) indicates the number of ECG signals. Records obtained from the same subject have names that differ ter the last digit only.

Each record is represented by 12 files, all with the same base name (the record name) and a suffix that identifies the verkeersopstopping type:

  • a (text) .hea (header) opstopping, containing detailed clinical information for the subject,
  • a (binary) .dat (signal) opstopping, containing the digitized ECG signals,
  • several (binary) annotation files, identifiable by suffix:
  • .ari (automatically-generated ritme annotations)
  • .atr (by hand corrected strike annotations)
  • .16a (automatically-generated, manually-corrected ST-segment measurements, based on 16-second moving averages, updated for each strike)
  • .sta (ST-segment scene annotations, Vmin = 75 µ,V, Tmin = 30 s, see below)
  • .stb (ST-segment scene annotations, Vmin = 100 µ,V, Tmin = 30 s)
  • .stc (ST-segment gig annotations, Vmin = 100 µ,V, Tmin = 60 s)
  • a (text) .cnt opstopping, containing counts of ST scenes ter the .sta , .stb , and .stc files
  • a (text) .stf verkeersopstopping, containing the ST level function, the linearly approximated baseline ST level function (ST level reference function), and the ST deviation function for each ECG lead,
  • a (binary) .tsr.zip verkeersopstopping, containing extra gegevens files needed by SEMIA (see below):
    • a (text) _fin.dmy opstopping, containing fine diagnostic and morphology time series
    • a (text) _raw.dmy verkeersopstopping, containing raw diagnostic and morphology time series
    • a (text) _1.sta opstopping, containing ST segment markers
    • (Before using SEMIA, download and unzip the .tsr.zip opstopping for the record(s) of rente into your current directory.)

    • a (binary) .klt.zip verkeersopstopping, which decompresses to a (text) .klt verkeersopstopping, containing
      • time series of ST segment Karhunen-Loè,ve convert coefficients (ST segment principal components)
      • time series of QRS complicated Karhunen-Loè,ve convert coefficients (QRS ingewikkeld principal components)
      • The measurements ter the .16a files were used to construct ST level and deviation functions for each signal, spil recorded te the .stf files. (Further details about the .stf , tsr.zip , and .klt.zip files are available here.) ST scenes were identified independently for each signal, based on its ST deviation function and on thesis criteria:

        1. An gig starts when the magnitude of the ST deviation function very first exceeds 50 µ,V,
        2. The deviation voorwaarde reach a magnitude of Vmin or more via a continuous interval of at least Tmin,
        3. The scene completes when the deviation becomes smaller than 50 µ,V, provided that it does not exceed 50 µ,V ter the following 30 seconds.

        Since differing criteria may be suitable depending on the application, three sets of ST scene annotations are provided. The annotation codes used ter the .sta , .stb , .stc , and .16a files are described here.

        For each record, the numbers of ST scenes spil determined by each of the three sets of criteria are summarized ter an extra text opstopping (with suffix .cnt ). The deviation functions and the locations of the scenes are introduced graphically ter a set of trend plots here. Each record is represented by a 24-hour plot ( _00-24.jpg ) and by five 6-hour plots which overlap by one hour ( _00-06.jpg , 05-11.jpg , etc.).

        Development of the Long-Term ST Database wasgoed an inter-institutional and international effort coordinated by Prof. Frank Jager of the Faculty of Laptop and Information Science, University of Ljubljana, Ljubljana, Slovenia. Other investigators include: Roman Dorn, PhD, and Ales Smrdel, MSc, of the Faculty of Rekentuig and Information Science, Ljubljana, Dr. Gorazd Antolic of the University Medical Center, Ljubljana, Slovenia, Drs. Alessandro Taddei and Michele Emdin of the CNR Institute for Clinical Physiology (the creators of the European ST-T Database European ST-T Database), Pisa, and Prof. Carlo Marchesi of the University of Firenze, Firenze, Italy, and Dr. Roger Mark and George Moody of the Massachusetts Institute of Technology (the creators of the MIT-BIH Arrhythmia Database), Cambridge, Mamma, USA, and the Beth Israel Deaconess Medical Center, Boston, Moe, USA. The project wasgoed supported by Medtronic, Inc. (Minneapolis, MN, USA) and Zymed, Inc. (Camarillo, CA, USA). Development of the Long-Term ST Database began ter 1995 and wasgoed ended ter 2002. Wij thank all who contributed to this project, further details are here.

        Several sources contributed recordings to the Long-Term ST Database:

        • Eleven of the recordings included ter the Long-Term ST Database are from the initial Long-Term ST Database developed under a snaak U.S.-Slovenian research project inbetween 1995 and 1998.
        • Ten extra recordings of the Long-Term ST Database are from the collection originally gathered by the Pisa group for the European ST-T Database, which contains two-hour excerpts of some of thesis same recordings. The original analog recordings were redigitized for the Long-Term ST Database, since the signals have bot rescaled spil a result, ongezouten comparison of the annotations te the European ST-T Database records with those for the corresponding portions of the Long-Term ST Database records is not possible. The inclusion of thesis recordings te the Long-Term ST Database permits explore of the dynamics of ischemic ST switches overheen a much longer period ter thesis previously well-studied subjects. Among the samples available here, record s20021 includes the two-hour segment that wasgoed previously digitized to produce record e0113 of the European ST-T Database.
        • Another Eighteen of the LTSTDB recordings, those containing recordings with three ECG signals, were contributed to the project by Zymed, Inc.

        The annotation of the Long-Term ST Database wasgoed performed using SEMIA, a program written by the group te Ljubljana for this purpose. SEMIA provides an interactive graphical user interface to a semi-automated algorithm for measurement of ST levels. Sources for SEMIA, and a precompiled version for GNU/Linux, are available here (spil individual files), and spil a gzip-compressed tar archive.

        Each recording wasgoed reviewed independently by experienced annotators using SEMIA at each of the three sites (Ljubljana, Pisa, and Cambridge). Participants met several times annually to obtain the overeenstemming reference annotations.

        A series of SEMIA screenshots illustrates the annotation process. (Use your browser’s Back button to come back to this pagina after following the linksom to thesis screenshots ter the next paragraph. If you have problems viewing the screenshots ter your browser, please read this note.)

        The very first task faced by the pro annotators wasgoed to mark the locations of the PQ junction (the isoelectric level) and the J point, based on 16-second averaged cardiac cycles chosen at frequent intervals via the recordings. Thesis marks serve spil guideposts for the automated ST level measurement algorithm that performs the next step. The experts then examine the time series of ST level measurements ter order to locate and to mark a set of local reference points (marked spil LR ter the upper panel of the figure). Thesis are used to construct a piecewise linear baseline ST level function, which may vary overheen time spil a result of bod position switches or other factors unrelated to ischemia, especially te subjects with prior myocardial infarctions. Axis shifts reflect bod position switches, and are usually most apparent ter the QRS complexes (note the switches ter the QRS principal components, KL1 – KL5, ter the lower panel of the figure). By tegenstelling, when ischemic ST switches occur, they are most apparent ter the principal components of the ST segment (see the lower panel ter this screenshot). Local references are placed before and after each such scene, and the scenes are annotated next. During this process, the pro annotators have the option of viewing either the ST level time series or the ST deviation time series (formed by subtracting the baseline ST level function from the uncorrected ST level time series), spil shown ter the upper panels of the two screenshots. For further details, see reference Four below.

        Software for producing printed documentation of the Long-Term ST Database is available for Linux or Unix. The software produces klein trend plots of the ST level and ST deviation time series, with indicators of ischemic and non-ischemic ST scenes.


        Frank Jager and Miha Amon have contributed extra sets of time series computed from the ST segments of each normal and non-noisy ritme ter the database. Te each case, they provided time series computed separately for each ECG lead.

        • Te 2009, Miha and Rechttoe calculated coefficients of normalized and non-normalized Legendre orthonormal poot functions. The Legendre orthonormal-transform coefficient time series are ter the legendre subdirectory.
        • Te 2011, Miha and Frank derived fresh single-lead KL voet functions for the ST segments, and used them to compute normalized and non-normalized KL coefficients. The KL calculated coefficients are centralized by their mean values. The single-lead KL coefficient time series are te the kl-single subdirectory.
        • Te 2015, Miha and Rechttoe derived another fresh set of single lead KL poot functions for the ST segments, and their subsequent normalized and non-normalized KL coefficients. This time, the KL coefficients are not mean-centered. The single-lead KL coefficient time series are ter the kl-single-uncentralized subdirectory.

        Derivation of the Legendre orthonormal-transform normalized and non-normalized coefficient time series, derivation of fresh single-lead KL poot functions for the ST segments, and derivation of normalized and non-normalized KL coefficient time series is described te reference Five below.

        The kl-single and kl-single-uncentralized projects use different mechanisms (time domain and KL based respectively) to eliminate noisy heartbeats. Therefore the KL-Transform is applied on two different covariance matrices derived from two different sets of ST sections, which results ter two slightly different sets of poot functions. More importantly however is that only the subsequent kl-single coefficients are centralized by their mean values.


        For further information, please voeling:

        Laboratory of Biomedical Rekentuig Systems and Imaging

        University of Ljubljana

        Faculty of Rekentuig and Information Science

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