Benchmarking beat classification algorithms

This study compares the accuracy of a range of advanced and classical pattern recognition algorithms for beat and arrhythmia classification from ECG using a principled statistical framework. These are to be used in an application where no patient-specific adaptation of the features or of the model i...

Full description

Saved in:
Bibliographic Details
Published inComputers in Cardiology 2001. Vol.28 (Cat. No.01CH37287) pp. 529 - 532
Main Authors Nabney, I.T., Evans, D.J., Tenner, J., Gamlyn, L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2001
Subjects
Cardiology
Classification algorithms
Data visualization
Electrocardiography
Feature extraction
Heart rate variability
Histograms
Pattern recognition
Principal component analysis
Testing
Online AccessGet full text

Cover

More Information
Summary:This study compares the accuracy of a range of advanced and classical pattern recognition algorithms for beat and arrhythmia classification from ECG using a principled statistical framework. These are to be used in an application where no patient-specific adaptation of the features or of the model is possible, which means that models must be able to generalise across subjects. Our results demonstrate that non-linear classification models offer significant advantages in ECG beat classification and that, with a principled approach to feature selection, pre-processing and model development, it is possible to get robust inter-subject generalisation even on ambulatory data.
ISBN:0780372662
9780780372665
ISSN:0276-6547
DOI:10.1109/CIC.2001.977709