Patient Independent Heart Beat Classification using Reconstructed Phase Space

Nejadgholi, Isar; Moradi, Mohammad Hasan; Abdol Ali, Fateme

doi:10.22041/ijbme.2011.13196

Document Type : Full Research Paper

Authors

¹ Ph.D Student, Faculty of Biomedical Engineering, Amirkabir University of Technology

² Associate Professor, Faculty of Group, iomedical Engineering, Amirkabir University of Technology

³ M.Sc Student, Faculty of Biomedical Engineering, Amirkabir University of Technology

10.22041/ijbme.2011.13196

Abstract

Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively little number of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, Reconstructed Phase Space (RPS) theory is used to classify five heartbeat types (Normal, PVC, LBBB, RBBB and PB). In the first and second method, RPS is modeled by the Gaussian mixture model (GMM) and bins, respectively and then classified by classic Bayesian classifier. In the third method, RPS is directly used to train predictor time-delayed neural networks (TDNN) and classified based on minimum prediction error. All three methods highly outperform the results reported before for patient independent heartbeat classification. The best result is achieved using GMM-Bayes method with 92.5% accuracy for patient independent classification.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

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Iranian Journal of Biomedical Engineering

Patient Independent Heart Beat Classification using Reconstructed Phase Space

References

References

Volume 4, Issue 4 - Serial Number 4
June 2010
Pages 279-292

Patient Independent Heart Beat Classification using Reconstructed Phase Space

References

References

Volume 4, Issue 4 - Serial Number 4June 2010Pages 279-292

Volume 4, Issue 4 - Serial Number 4
June 2010
Pages 279-292