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Weighted Conditional Random Fields for Supervised Interpatient Heartbeat Classification

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4 Author(s)
de Lannoy, G. ; Machine Learning Group, Univ. Catholique de Louvain, Louvain-La-Neuve, Belgium ; Francois, D. ; Delbeke, J. ; Verleysen, M.

This paper proposes a method for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as time dependences between observations and a strong class unbalance, a specific classifier is proposed and evaluated on real ECG signals from the MIT arrhythmia database. This classifier is a weighted variant of the conditional random fields classifier. Experiments show that the proposed method outperforms previously reported heartbeat classification methods, especially for the pathological heartbeats.

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Biomedical Engineering, IEEE Transactions on  (Volume:59 ,  Issue: 1 )