PcHD: Personalized classification of heartbeat types using a decision tree ?
We propose a new method for personalized arrhythmia classification of an individual?s heartbeats by Holter monitoring. We use a decision tree to classify beats. We demonstrate the efficacy of this classifier by means of experiments against the MIT-BIH arrhythmia database. Our classifier is very accurate; it reduces the number of false alarms and missing events.
personalization holter monitoring heartbeat classification decision tree model electrocardiogram pan-tompkins algorithm