Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network

Authors:
Sung-Nien Yu;Ying-Hsiang Chen
Affiliations:
Department of Electrical Engineering, National Chung Cheng University, 168 University Road, Ming-Hsiung, Chia-Yi 621, Taiwan;Department of Electrical Engineering, National Chung Cheng University, 168 University Road, Ming-Hsiung, Chia-Yi 621, Taiwan
Venue:
Pattern Recognition Letters
Year:
2007

Citing 4
Cited 24

Automatic Detection of Premature Ventricular Contraction Using Quantum Neural Networks

BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
ECG beat classification using neuro-fuzzy network

Pattern Recognition Letters
A modified mixture of experts network structure for ECG beats classification with diverse features

Engineering Applications of Artificial Intelligence
Adaptive probabilistic neural networks for pattern classification in time-varying environment

IEEE Transactions on Neural Networks

A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network

Expert Systems with Applications: An International Journal
Is taking natural log superior to not? - Using a characteristics oriented fuzzy Hopfield neural network to identify probability density functions

Expert Systems with Applications: An International Journal
Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks

Expert Systems with Applications: An International Journal
PVC discrimination using the QRS power spectrum and self-organizing maps

Computer Methods and Programs in Biomedicine
Arif Index for Predicting the Classification Accuracy of Features and Its Application in Heart Beat Classification Problem

PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components

Artificial Intelligence in Medicine
A two-stage mechanism for registration and classification of ECG using Gaussian mixture model

Pattern Recognition
A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification

Expert Systems with Applications: An International Journal
A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification

Expert Systems with Applications: An International Journal
Clustering MIT--BIH arrhythmias with Ant Colony Optimization using time domain and PCA compressed wavelet coefficients

Digital Signal Processing
Enhanced probabilistic neural network with local decision circles: A robust classifier

Integrated Computer-Aided Engineering
Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier

Expert Systems with Applications: An International Journal
Fuzzy Clustered Probabilistic and Multi Layered Feed Forward Neural Networks for Electrocardiogram Arrhythmia Classification

Journal of Medical Systems
Supervised Learning Probabilistic Neural Networks

Neural Processing Letters
Classification of Arrhythmia Using Hybrid Networks

Journal of Medical Systems
Selection of effective features for ECG beat recognition based on nonlinear correlations

Artificial Intelligence in Medicine
A Neuro-Fuzzy Identification of ECG Beats

Journal of Medical Systems
Sparse Representation-Based Heartbeat Classification Using Independent Component Analysis

Journal of Medical Systems
Removal and interpolation of missing values using wavelet neural network for heterogeneous data sets

Proceedings of the International Conference on Advances in Computing, Communications and Informatics
ECG arrhythmia classification based on optimum-path forest

Expert Systems with Applications: An International Journal
Rule extraction from DEWNN to solve classification and regression problems

SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
ECG beat classification using a cost sensitive classifier

Computer Methods and Programs in Biomedicine
Evaluating the use of ECG signal in low frequencies as a biometry

Expert Systems with Applications: An International Journal
Neural network and wavelet average framing percentage energy for atrial fibrillation classification

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.11

Visualization

Abstract

In this paper, an electrocardiogram (ECG) beat classification system based on wavelet transformation and probabilistic neural network (PNN) is proposed to discriminate six ECG beat types. The ECG beat signals are first decomposed into components in different subbands using discrete wavelet transformation. Three sets of statistical features of the decomposed signals as well as the AC power and the instantaneous RR interval of the original signal are exploited to characterize the ECG signals. A PNN follows to classify the feature vectors. The result shows a promising accuracy of 99.65%, with equally well recognition rates of over 99% throughout all type of ECG beats. Only 11 features are required to attain this high accuracy, which is substantially smaller in quantity than that in other methods. These observations prove the effectiveness and efficiency of the proposed method for computer-aided diagnosis of heart diseases based on ECG signals.