Comparison of four methods for premature ventricular contraction and normal beat clustering
- 1 January 2005
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 921-924
- https://doi.org/10.1109/cic.2005.1588258
Abstract
The learning capacity and the classification ability for normal beats and premature ventricular contractions clustering by four classification methods were compared: neural networks (NN), K-th nearest neighbour rule (Knn), discriminant analysis (DA) and fuzzy logic (FL). Twenty-six morphology feature parameters, which include information of amplitude, area, specific interval durations and measurement of the QRS vector in a VCG plane, were defined. One global and two local learning sets were used. The local classifiers achieved better accuracies because of their good adaptability to the patients, while the capacity of the global classifier to process new records without additional learning was expectedly balanced by lower accuracies. NN assure the best results (high and balanced indices for specificity and sensitivity) using one of the local learning set, while the Knn provides the best results with the other local learning set. Using the global learning set DA and the FL methods perform better than the NN and KnnKeywords
This publication has 18 references indexed in Scilit:
- Premature ventricular contraction classification by theKth nearest-neighbours rulePhysiological Measurement, 2005
- Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval FeaturesIEEE Transactions on Biomedical Engineering, 2004
- Adaptive feature extraction for QRS classification and ectopic beat detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Previous identification of QRS onset and offset is not essential for classifying QRS complexes in a single leadPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Comparing wavelet transforms for recognizing cardiac patternsIEEE Engineering in Medicine and Biology Magazine, 1995
- ANFIS: adaptive-network-based fuzzy inference systemIEEE Transactions on Systems, Man, and Cybernetics, 1993
- A fuzzy version of the K-NN methodFuzzy Sets and Systems, 1992
- Classification of electrocardiographic signals: a fuzzy pattern matching approachArtificial Intelligence in Medicine, 1991
- Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detectionIEEE Transactions on Biomedical Engineering, 1991
- The time-sequenced adaptive filterIEEE Transactions on Acoustics, Speech, and Signal Processing, 1981