An arrhythmia classification system based on the RR-interval signal

Authors:
M. G. Tsipouras;D. I. Fotiadis;D. Sideris
Affiliations:
Deparment of Computer Science, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina Campus, P.O. Box 1186, GR 45110 Ioannina, Greece and Biomedical Research Insti ...;Deparment of Computer Science, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina Campus, P.O. Box 1186, GR 45110 Ioannina, Greece and Biomedical Research Insti ...;Division of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece and Michaelidion Cardiology Center, GR 45110 Ioannina, Greece
Venue:
Artificial Intelligence in Medicine
Year:
2005

Citing 1
Cited 17

Time series: theory and methods

Time series: theory and methods

A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree

Artificial Intelligence in Medicine
Evolving a Bayesian classifier for ECG-based age classification in medical applications

Applied Soft Computing
Ontological fuzzy agent for electrocardiogram application

Expert Systems with Applications: An International Journal
Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal

Artificial Intelligence in Medicine
PVC discrimination using the QRS power spectrum and self-organizing maps

Computer Methods and Programs in Biomedicine
Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components

Artificial Intelligence in Medicine
Discriminating between V and N Beats from ECGs Introducing an Integrated Reduced Representation along with a Neural Network Classifier

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A new method for classification of ECG arrhythmias using neural network with adaptive activation function

Digital Signal Processing
A multi-stage automatic arrhythmia recognition and classification system

Computers in Biology and Medicine
ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features

Expert Systems with Applications: An International Journal
Selection of effective features for ECG beat recognition based on nonlinear correlations

Artificial Intelligence in Medicine
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

Computer Methods and Programs in Biomedicine
Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines

Expert Systems with Applications: An International Journal
Automated cardiac event change detection for continuous remote patient monitoring devices

Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief
On the Performance of Virtualized Infrastructures for Processing Realtime Streaming Data

UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Combining multiple views: Case studies on protein and arrhythmia features

Engineering Applications of Artificial Intelligence
Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Objective: This paper proposes a knowledge-based method for arrhythmic beat classification and arrhythmic episode detection and classification using only the RR-interval signal extracted from ECG recordings. Methodology: A three RR-interval sliding window is used in arrhythmic beat classification algorithm. Classification is performed for four categories of beats: normal, premature ventricular contractions, ventricular flutter/fibrillation and 2^o heart block. The beat classification is used as input of a knowledge-based deterministic automaton to achieve arrhythmic episode detection and classification. Six rhythm types are classified: ventricular bigeminy, ventricular trigeminy, ventricular couplet, ventricular tachycardia, ventricular flutter/fibrillation and 2^o heart block. Results: The method is evaluated by using the MIT-BIH arrhythmia database. The achieved scores indicate high performance: 98% accuracy for arrhythmic beat classification and 94% accuracy for arrhythmic episode detection and classification. Conclusion: The proposed method is advantageous because it uses only the RR-interval signal for arrhythmia beat and episode classification and the results compare well with more complex methods.