Domain adaptation methods for ECG classification
- 1 January 2013
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-4
Abstract
The detection and classification of heart arrhythmias using Electrocardiogram signals (ECG) has been an active area of research in the literature. Usually, to assess the effectiveness of a proposed classification method, training and test data are extracted from the same ECG record. However, in real scenarios test data may come from different records. In this case, the classification results may be less accurate due to the statistical shift between these samples. In order to solve this issue, we investigate, in this paper, the capabilities of two domain adaption methods proposed recently in the literature of machine learning. The first is known as domain transfer SVM, whereas the second is the importance weighted kernel logistic regression method. To assess the effectiveness of both methods, the MIT-BIH arrhythmia database is used in the experiments.Keywords
This publication has 16 references indexed in Scilit:
- A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor NetworksSensors, 2012
- Domain Transfer Multiple Kernel LearningIEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
- DAML: Domain Adaptation Metric LearningIEEE Transactions on Image Processing, 2011
- Semi-supervised speaker identification under covariate shiftSignal Processing, 2009
- Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval FeaturesIEEE Transactions on Biomedical Engineering, 2006
- A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval FeaturesIEEE Transactions on Biomedical Engineering, 2006
- Integrating structured biological data by Kernel Maximum Mean DiscrepancyBioinformatics, 2006
- Domain adaptation with structural correspondence learning2006
- ECG data compression using truncated singular value decompositionIEEE Transactions on Information Technology in Biomedicine, 2001
- Detecting ventricular fibrillationIEEE Engineering in Medicine and Biology Magazine, 1995