Personalized long-term ECG classification: A systematic approach

This paper presents a personalized long-term electrocardiogram (ECG) classification framework, which addresses the problem within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is automatically extracted from a time frame of homogeneous (similar) beats. We tested the system on a benchmark database where beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and thus we used exhaustive K-means clustering in order to find out (near-) optimal number of key-beats as well as the master key-beats. T...

• Type: Journal
• Year: 2011
• Where: ESWA
• Authors: Serkan Kiranyaz, Turker Ince, Jenni Pulkkinen, Moncef Gabbouj
• Keywords: Database, ESWA 2011, Holter Register, Long-term Ecg Signal, Personalized Long-term Electrocardiogram