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Physionet Folds

Acquire from: The PhysioNet/Computing in Cardiology Challenge 2021

@kaggle.soyabulislamlincoln_physionet_folds

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About this Dataset

Physionet Folds

The electrocardiogram (ECG) is a non-invasive representation of the electrical activity of the heart. Although the twelve-lead ECG is the standard diagnostic screening system for many cardiological issues, the limited accessibility of twelve-lead ECG devices provides a rationale for smaller, lower-cost, and easier to use devices. While single-lead ECGs are limiting [1], reduced-lead ECG systems hold promise, with evidence that subsets of the standard twelve leads can capture useful information [2], [3], [4] and even be comparable to twelve-lead ECGs in some limited contexts. In 2017 we challenged the public to classify AF from a single-lead ECG, and in 2020 we challenged the public to diagnose a much larger number of cardiac problems using twelve-lead recordings. However, there is limited evidence to demonstrate the utility of reduced-lead ECGs for capturing a wide range of diagnostic information.

In this year’s Challenge, we ask the following question: ‘Will two do?’ This year’s Challenge builds on last year’s Challenge [5], which asked participants to classify cardiac abnormalities from twelve-lead ECGs. We are asking you to build an algorithm that can classify cardiac abnormalities from twelve-lead, six-lead, four-lead, three-lead, and two-lead ECGs. We will test each algorithm on databases of these reduced-lead ECGs, and the differences in performances of the algorithms on these databases will reveal the utility of reduced-lead ECGs in comparison to standard twelve-lead EGCs.

Reyna MA, Sadr N, Perez Alday EA, Gu A, Shah AJ, Robichaux C, Rad AB, Elola A, Seyedi S, Ansari S, Ghanbari H, Li Q, Sharma A, Clifford GD. Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021. Computing in Cardiology 2021; 48: 1-4,

Reyna MA, Sadr N, Perez Alday EA, Gu Annie, Shah AJ, Robichaux C, Rad AB, Elola A, Seyedi S, Ansari S, Ghanbari H, Li Q, Sharma A, Clifford GD. Issues in the automated classification of multilead ECGs using heterogeneous labels and populations. Physiol. Meas. 2022.

N.B: PROBLEMATIC_INDICES : [3723, 8329, 8774, 8775, 8776, 10266, 10269, 10271, 10276, 10277, 10278, 10291, 10292, 10333, 10362, 10363, 10367, 10369, 10382, 10393, 10404, 10502, 10737, 17102, 17210, 17547, 17802, 20909, 21492, 22145, 22146, 22644, 23567, 23570, 23572, 23578, 23579, 23586, 23596, 23603, 23624, 23642, 23646, 23651, 23656, 23658, 23688, 23691, 23698, 23700, 26927, 29806, 30483, 30624, 30628, 30646, 30925, 30967, 34272, 35230, 35448, 36897, 36900, 36914, 36916, 36918, 36931, 36936, 36937, 36944, 36975, 36978, 37010, 37022, 37795, 39759, 43783, 43822, 48368, 48632, 50205, 50211, 50250, 50254, 50265, 50270, 50272, 50275, 50286, 50289, 50305, 50308, 50310, 50332, 50335, 51471, 54390, 54616, 54717, 57020, 57272, 61478, 61794, 61981, 61982, 62833, 63416, 63419, 63421, 63433, 63450, 63457, 63458, 63463, 63478, 63492, 63498, 63499, 63505, 63522, 63537, 63540, 63546, 8872, 9862, 10408, 13260, 17452, 17509, 30633, 30793, 36917, 36939, 36945, 37036, 37133, 48769, 50215, 50216, 50276, 50333, 51278, 63439, 63541, 63548]

Tables

Fold 1

@kaggle.soyabulislamlincoln_physionet_folds.fold_1
  • 641.49 MB
  • 13,333 rows
  • 5,008 columns
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CREATE TABLE fold_1 (
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);

Fold 2

@kaggle.soyabulislamlincoln_physionet_folds.fold_2
  • 641.73 MB
  • 13,333 rows
  • 5,008 columns
Loading... 
CREATE TABLE fold_2 (
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);

Fold 3

@kaggle.soyabulislamlincoln_physionet_folds.fold_3
  • 641.53 MB
  • 13,333 rows
  • 5,008 columns
Loading... 
CREATE TABLE fold_3 (
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);

Fold 4

@kaggle.soyabulislamlincoln_physionet_folds.fold_4
  • 641.37 MB
  • 13,332 rows
  • 5,008 columns
Loading... 
CREATE TABLE fold_4 (
  "signal_1" DOUBLE,
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);

Fold 5

@kaggle.soyabulislamlincoln_physionet_folds.fold_5
  • 641.53 MB
  • 13,332 rows
  • 5,008 columns
Loading... 
CREATE TABLE fold_5 (
  "signal_1" DOUBLE,
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);

Neurokit

@kaggle.soyabulislamlincoln_physionet_folds.neurokit
  • 4.81 MB
  • 66,518 rows
  • 17 columns
Loading... 
CREATE TABLE neurokit (
  "bpm" BIGINT,
  "mean_nn" DOUBLE,
  "sdnn" DOUBLE,
  "sdsd" DOUBLE,
  "rmssd" DOUBLE,
  "cvnn" DOUBLE,
  "cvsd" DOUBLE,
  "median_nn" DOUBLE,
  "mad_nn" DOUBLE,
  "mcv_nn" DOUBLE,
  "iqr_nn" DOUBLE,
  "sdrm_ssd" DOUBLE,
  "prc20_nn" DOUBLE,
  "prc80_nn" DOUBLE,
  "min_nn" DOUBLE,
  "max_nn" DOUBLE,
  "hti" DOUBLE
);

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