ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features

Authors:
M. R. Homaeinezhad;S. A. Atyabi;E. Tavakkoli;H. N. Toosi;A. Ghaffari;R. Ebrahimpour
Affiliations:
Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran and Cardio Vascular Research Group (CVRG), K.N. Toosi University of Technology, Tehran, Iran;Cardio Vascular Research Group (CVRG), K.N. Toosi University of Technology, Tehran, Iran and Department of Mechatronic Engineering, Islamic Azad University, South Tehran Branch, Iran and Young Res ...;Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran and Cardio Vascular Research Group (CVRG), K.N. Toosi University of Technology, Tehran, Iran;Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran and Cardio Vascular Research Group (CVRG), K.N. Toosi University of Technology, Tehran, Iran;Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran and Cardio Vascular Research Group (CVRG), K.N. Toosi University of Technology, Tehran, Iran and Department ...;Department of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 22
Cited 1

Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric classifier-independent feature selection

Pattern Recognition
A switchable scheme for ECG beat classification based on independent component analysis

Expert Systems with Applications: An International Journal
Atrial fibrillation classification with artificial neural networks

Pattern Recognition
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
Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier

Computers & Mathematics with Applications
Adaptive wavelet network for multiple cardiac arrhythmias recognition

Expert Systems with Applications: An International Journal
Integration of independent component analysis and neural networks for ECG beat classification

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
Automatic identification of cardiac health using modeling techniques: A comparative study

Information Sciences: an International Journal
Selection of significant independent components for ECG beat classification

Expert Systems with Applications: An International Journal
Review: Usage of class dependency based feature selection and fuzzy weighted pre-processing methods on classification of macular disease

Expert Systems with Applications: An International Journal
A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network

Expert Systems with Applications: An International Journal
Feature selection with dynamic mutual information

Pattern Recognition
Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components

Artificial Intelligence in Medicine
Invariant trajectory classification of dynamical systems with a case study on ECG

Pattern Recognition
Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system

Artificial Intelligence in Medicine
A fuzzy clustering neural network architecture for classification of ECG arrhythmias

Computers in Biology and Medicine
Statistical models of reconstructed phase spaces for signal classification

IEEE Transactions on Signal Processing - Part I
An arrhythmia classification system based on the RR-interval signal

Artificial Intelligence in Medicine
Block-Based Neural Networks for Personalized ECG Signal Classification

IEEE Transactions on Neural Networks

Real-time CHF detection from ECG signals using a novel discretization method

Computers in Biology and Medicine

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this study, a new supervised noise-artifact-robust heart arrhythmia fusion classification solution, is introduced. Proposed method consists of structurally diverse classifiers with a new QRS complex geometrical feature extraction technique. Toward this objective, first, the events of the electrocardiogram (ECG) signal are detected and delineated using a robust wavelet-based algorithm. Then, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Next, the curve length of each excerpted segment is calculated and is used as the element of the feature space. Discrimination power of proposed classifier in isolation of different Gold standard beats was assessed with accuracy 98.20%. Also, proposed learning machine was applied to 7 arrhythmias belonging to 15 different records and accuracy 98.06% was achieved. Comparisons with peer-reviewed studies prove a marginal progress in computerized heart arrhythmia recognition technologies.