A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Adaptive Electrocardiogram Feature Extraction on Distributed Embedded Systems
IEEE Transactions on Parallel and Distributed Systems
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
A switchable scheme for ECG beat classification based on independent component analysis
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
IEEE Transactions on Information Technology in Biomedicine
Heartbeat time series classification with support vector machines
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Expert Systems with Applications: An International Journal
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Technology in Biomedicine
An arrhythmia classification system based on the RR-interval signal
Artificial Intelligence in Medicine
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
A formal analysis of stopping criteria of decomposition methods for support vector machines
IEEE Transactions on Neural Networks
ECG beat classification using a cost sensitive classifier
Computer Methods and Programs in Biomedicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. Many algorithms have been proposed, however, the recognition rate is still unsatisfactory due to unreliable feature extraction in signal characteristic analysis or poor generalization capability of the classifier. In this paper, we propose a system for cardiac arrhythmia detection in ECGs with adaptive feature selection and modified support vector machines (SVMs). Wavelet transform-based coefficients and signal amplitude/interval parameters are first enumerated as candidates, but only a few specific ones are adaptively selected for the classification of each class pair. A new classifier, which integrates k-means clustering, one-against-one SVMs, and a modified majority voting mechanism, is proposed to further improve the recognition rate for extremely similar classes. The experimental results show that the proposed ECG analysis approach can obtain a higher recognition rate than the published approaches. By testing the system with more than 100,000 samples in MIT-BIH arrhythmia database, the average recognition rate is 98.92%, and the recognition rate for each class is kept above 92%.