Type

Journal Article

Authors

Richard Reilly
Philip de Chazal

Subjects

Mathematics

Topics
electrocardiogram ecg diagnosis computer assisted adaptive classifier ecg electrocardiography heartbeat classifier global diagnosis heart rate classification arrhythmias cardiac morphology training sensitivity and specificity reproducibility of results algorithms physiopathology pattern recognition automated artificial intelligence humans

A patient adapting heartbeat classifier using ECG morphology and heart-beat interval features (2006)

Abstract An adaptive system for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats into one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard is presented. The heartbeat classification system processes an incoming recording with a global-classifier to produce the first set of beat annotations. An expert then validates and if necessary corrects a fraction of the beats of the recording. The system then adapts by first training a local-classifier using the newly annotated beats and combines this with the global-classifier to produce an adapted classification system. The adapted system is then used to update beat annotations. The results of this study show that the performance of a patient adaptable classifier increases with the amount of training of the system on the local record. Crucially, the performance of the system can be significantly boosted with a small amount of adaptation even when all beats used for adaptation are from a single class. This study illustrates the ability to provide highly beneficial automatic arrhythmia monitoring and is an improvement on previously reported results for automated heartbeat classification systems.
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Full list of authors on original publication

Richard Reilly, Philip de Chazal

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Richard Reilly
Trinity College Dublin
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