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心律失常数据集

心律失常数据集

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Life Classification

Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups.......

数据结构 ? 0M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Original Owners of Database:

    1. H. Altay Guvenir, PhD.,
    Bilkent University,
    Department of Computer Engineering and Information Science,
    06533 Ankara, Turkey
    Phone: +90 (312) 266 4133
    Email: guvenir '@' cs.bilkent.edu.tr

    2. Burak Acar, M.S.,
    Bilkent University,
    EE Eng. Dept.
    06533 Ankara, Turkey
    Email: buraka '@' ee.bilkent.edu.tr

    3. Haldun Muderrisoglu, M.D., Ph.D.,
    Baskent University,
    School of Medicine
    Ankara, Turkey

    Donor:

    H. Altay Guvenir
    Bilkent University,
    Department of Computer Engineering and Information Science,
    06533 Ankara, Turkey
    Phone: +90 (312) 266 4133
    Email: guvenir '@' cs.bilkent.edu.tr


    Data Set Information:

    This database contains 279 attributes, 206 of which are linear valued and the rest are nominal.

    Concerning the study of H. Altay Guvenir: "The aim is to distinguish between the presence and absence of cardiac arrhythmia and to classify it in one of the 16 groups. Class 01 refers to 'normal' ECG classes 02 to 15 refers to different classes of arrhythmia and class 16 refers to the rest of unclassified ones. For the time being, there exists a computer program that makes such a classification. However there are differences between the cardiolog's and the programs classification. Taking the cardiolog's as a gold standard we aim to minimise this difference by means of machine learning tools."

    The names and id numbers of the patients were recently removed from the database.


    Attribute Information:

    -- Complete attribute documentation:
    1 Age: Age in years , linear
    2 Sex: Sex (0 = male; 1 = female) , nominal
    3 Height: Height in centimeters , linear
    4 Weight: Weight in kilograms , linear
    5 QRS duration: Average of QRS duration in msec., linear
    6 P-R interval: Average duration between onset of P and Q waves in msec., linear
    7 Q-T interval: Average duration between onset of Q and offset of T waves in msec., linear
    8 T interval: Average duration of T wave in msec., linear
    9 P interval: Average duration of P wave in msec., linear
    Vector angles in degrees on front plane of:, linear
    10 QRS
    11 T
    12 P
    13 QRST
    14 J

    15 Heart rate: Number of heart beats per minute ,linear

    Of channel DI:
    Average width, in msec., of: linear
    16 Q wave
    17 R wave
    18 S wave
    19 R' wave, small peak just after R
    20 S' wave

    21 Number of intrinsic deflections, linear

    22 Existence of ragged R wave, nominal
    23 Existence of diphasic derivation of R wave, nominal
    24 Existence of ragged P wave, nominal
    25 Existence of diphasic derivation of P wave, nominal
    26 Existence of ragged T wave, nominal
    27 Existence of diphasic derivation of T wave, nominal

    Of channel DII:
    28 .. 39 (similar to 16 .. 27 of channel DI)
    Of channels DIII:
    40 .. 51
    Of channel AVR:
    52 .. 63
    Of channel AVL:
    64 .. 75
    Of channel AVF:
    76 .. 87
    Of channel V1:
    88 .. 99
    Of channel V2:
    100 .. 111
    Of channel V3:
    112 .. 123
    Of channel V4:
    124 .. 135
    Of channel V5:
    136 .. 147
    Of channel V6:
    148 .. 159

    Of channel DI:
    Amplitude , * 0.1 milivolt, of
    160 JJ wave, linear
    161 Q wave, linear
    162 R wave, linear
    163 S wave, linear
    164 R' wave, linear
    165 S' wave, linear
    166 P wave, linear
    167 T wave, linear

    168 QRSA , Sum of areas of all segments divided by 10, ( Area= width * height / 2 ), linear
    169 QRSTA = QRSA + 0.5 * width of T wave * 0.1 * height of T wave. (If T is diphasic then the bigger segment is considered), linear

    Of channel DII:
    170 .. 179
    Of channel DIII:
    180 .. 189
    Of channel AVR:
    190 .. 199
    Of channel AVL:
    200 .. 209
    Of channel AVF:
    210 .. 219
    Of channel V1:
    220 .. 229
    Of channel V2:
    230 .. 239
    Of channel V3:
    240 .. 249
    Of channel V4:
    250 .. 259
    Of channel V5:
    260 .. 269
    Of channel V6:
    270 .. 279


    Relevant Papers:

    H. Altay Guvenir, Burak Acar, Gulsen Demiroz, Ayhan Cekin "A Supervised Machine Learning Algorithm for Arrhythmia Analysis." Proceedings of the Computers in Cardiology Conference, Lund, Sweden, 1997.
    [Web Link]


    Papers That Cite This Data Set1:

    Krista Lagus and Esa Alhoniemi and Jeremias Seppa and Antti Honkela and Arno Wagner. INDEPENDENT VARIABLE GROUP ANALYSIS IN LEARNING COMPACT REPRESENTATIONS FOR DATA. Neural Networks Research Centre, Helsinki University of Technology. [View Context].

    Gisele L. Pappa and Alex Alves Freitas and Celso A A Kaestner. AMultiobjective Genetic Algorithm for Attribute Selection. Computing Laboratory Pontificia Universidade Catolica do Parana University of Kent at Canterbury. [View Context].

    Shay Cohen and Eytan Ruppin and Gideon Dror. Feature Selection Based on the Shapley Value. School of Computer Sciences Tel-Aviv University. [View Context].


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