Benchmarking beat classification algorithms

• Published in: Computers 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
• Language: English
• Published: IEEE 2001
• Subjects: Cardiology; Classification algorithms; Data visualization; Electrocardiography; Feature extraction; Heart rate variability; Histograms; Pattern recognition; Principal component analysis; Testing
• ISBN: 0780372662; 9780780372665
• ISSN: 0276-6547
• DOI: 10.1109/CIC.2001.977709

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.