Arrhythmia
Abstract
Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups.
Purpose
Additional 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.
| Name | Role | Type | Description | Missing |
|---|---|---|---|---|
| Variable 1 | Feature | Integer | - | No |
| Variable 2 | Feature | Integer | - | No |
| Variable 3 | Feature | Integer | - | No |
| Variable 4 | Feature | Integer | - | No |
| Variable 5 | Feature | Integer | - | No |
| Variable 6 | Feature | Integer | - | No |
| Variable 7 | Feature | Integer | - | No |
| Variable 8 | Feature | Integer | - | No |
| Variable 9 | Feature | Integer | - | No |
| Variable 10 | Feature | Integer | - | No |
Papers Citing this Dataset
19 papers found
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data
By Sangdon Park, James Weimer, Insup Lee.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Dataset Classification
By Mohammad Basir, Faudziah Ahmad.
Outlier Detection by Consistent Data Selection Method
By Utkarsh Porwal, Smruthi Mukund.
One-Class Classification Based on Extreme Learning and Geometric Class Information
By Alexandros Iosifidis, Vasileios Mygdalis, Anastasios Tefas, Ioannis Pitas.
Multi-Task Regularization with Covariance Dictionary for Linear Classifiers
By Fanyi Xiao, Ruikun Luo, Zhiding Yu.