Neural and traditional techniques in diagnostic ECG classification
- 22 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (15206149) , 123-126
- https://doi.org/10.1109/icassp.1997.599566
Abstract
Neural and traditional techniques have been compared for the particular task of automatic ECG analysis. A large validated ECG database has been used. Statistical methods, neural architectures with supervised and unsupervised learning, and a neuro fuzzy architecture have been considered. The results from the connectionist approach are always at least comparable with those coming from more traditional classification methods. But the best performances have been obtained by the combination of the connectionist with the fuzzy approach.Keywords
This publication has 9 references indexed in Scilit:
- Neural Networks For Pattern Recognition In Medical DiagnosisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- ECG classification with neural networks and cluster analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Fuzzy pattern classification and the connectionist approachPattern Recognition Letters, 1996
- Classification of Arrhythmic Events in Ambulatory Electrocardiogram, Using Artificial Neural NetworksComputers and Biomedical Research, 1995
- An approach to cardiac arrhythmia analysis using hidden Markov modelsIEEE Transactions on Biomedical Engineering, 1990
- Self-Organization and Associative MemoryPublished by Springer Nature ,1989
- Comparison of the classification ability of the electrocardiogram and vectorcardiogramThe American Journal of Cardiology, 1987
- Comparison of multigroup logistic and linear discriminant ECG and VCG classificationJournal of Electrocardiology, 1987
- Computer system for analysis of ST segment changes on 24 hour Holter monitor tapes: Comparison with other available systemsJournal of the American College of Cardiology, 1984