Benchmarking beat classification algorithms

I. T. Nabney, D. J. Evans, J. Tenner, L. Gamlyn

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)


The aim of this study is to compare 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 features or 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.
Original languageUndefined/Unknown
Pages (from-to)529-532
Number of pages4
JournalComputers in Cardiology
Publication statusPublished - 1 Dec 2001

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