Notice of RetractionECG Beat Classification Based on Mirrored Gauss Model and Cluster Template

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Accurate electrocardiogram (ECG) beat classification is essential for automated detection of arrhythmias. A practical classification algorithm of the ECG beats, combining Mirrored Gauss Model (MGM) and cluster template had been proposed in this paper. High classification accuracy of normal beats and premature ventricular contraction (PVC) beats could be reached using MGM with an expense of huge time cost. Cluster template based on correlation matching and template queue could reduce fitting times of MGM a lot effectively. It was proved by experiment carrying out using all of ECG records in MIT-BIH Arrhythmia Database that this algorithm combining cluster template with Mirrored Gauss Model could guarantee both accuracy and speed well. Hence it is an effective and practical classification algorithm of ECG beat.