Classification of heartbeats based on linear discriminant analysis and artificial neural network

M. H. Song; J. Lee; H. D. Park; K. J. Lee

doi:10.1109/iembs.2005.1616626

Classification of heartbeats based on linear discriminant analysis and artificial neural network

M. H. Song, J. Lee, H. D. Park, K. J. Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

In this paper, we proposed a heartbeat classification algorithm based on linear discriminant analysis and artificial neural network. For the input of classifier, we extracted 275 input features from the first derivative signal of ECG signal and RR interval information and it was reduced to be 6 by LDA. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm with that of fuzzy inference system classifier. MIT-BIH Arrhythmia database were used as test and learning data. The performance of the proposed algorithm was 97.49% for sensitivity, 97.91% for specificity and 96.36% for accuracy. For the extraction of features, the first derivative signal of ECG is used only so that the real-time implementation of this algorithm was possible. And, on account of the reduction of feature dimensionality, the time cost for learning and testing can be expected.

Original language	English (US)
Title of host publication	Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1151-1153
Number of pages	3
ISBN (Print)	0780387406, 9780780387409
DOIs	https://doi.org/10.1109/iembs.2005.1616626
State	Published - 2005
Externally published	Yes
Event	2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China Duration: Sep 1 2005 → Sep 4 2005

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	7 VOLS
ISSN (Print)	0589-1019

Other

Other	2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Country/Territory	China
City	Shanghai
Period	9/1/05 → 9/4/05

Keywords

Artificial neural network
ECG
Heartbeats classification
Linear discriminant analysis

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/iembs.2005.1616626

Cite this

Song, M. H., Lee, J., Park, H. D., & Lee, K. J. (2005). Classification of heartbeats based on linear discriminant analysis and artificial neural network. In Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 (pp. 1151-1153). Article 1616626 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 7 VOLS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iembs.2005.1616626

Classification of heartbeats based on linear discriminant analysis and artificial neural network. / Song, M. H.; Lee, J.; Park, H. D. et al.
Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. Institute of Electrical and Electronics Engineers Inc., 2005. p. 1151-1153 1616626 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 7 VOLS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Song, MH, Lee, J, Park, HD & Lee, KJ 2005, Classification of heartbeats based on linear discriminant analysis and artificial neural network. in Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005., 1616626, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, vol. 7 VOLS, Institute of Electrical and Electronics Engineers Inc., pp. 1151-1153, 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, Shanghai, China, 9/1/05. https://doi.org/10.1109/iembs.2005.1616626

Song MH, Lee J, Park HD, Lee KJ. Classification of heartbeats based on linear discriminant analysis and artificial neural network. In Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. Institute of Electrical and Electronics Engineers Inc. 2005. p. 1151-1153. 1616626. (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). doi: 10.1109/iembs.2005.1616626

Song, M. H. ; Lee, J. ; Park, H. D. et al. / Classification of heartbeats based on linear discriminant analysis and artificial neural network. Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. Institute of Electrical and Electronics Engineers Inc., 2005. pp. 1151-1153 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings).

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title = "Classification of heartbeats based on linear discriminant analysis and artificial neural network",

abstract = "In this paper, we proposed a heartbeat classification algorithm based on linear discriminant analysis and artificial neural network. For the input of classifier, we extracted 275 input features from the first derivative signal of ECG signal and RR interval information and it was reduced to be 6 by LDA. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm with that of fuzzy inference system classifier. MIT-BIH Arrhythmia database were used as test and learning data. The performance of the proposed algorithm was 97.49% for sensitivity, 97.91% for specificity and 96.36% for accuracy. For the extraction of features, the first derivative signal of ECG is used only so that the real-time implementation of this algorithm was possible. And, on account of the reduction of feature dimensionality, the time cost for learning and testing can be expected.",

keywords = "Artificial neural network, ECG, Heartbeats classification, Linear discriminant analysis",

author = "Song, {M. H.} and J. Lee and Park, {H. D.} and Lee, {K. J.}",

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N2 - In this paper, we proposed a heartbeat classification algorithm based on linear discriminant analysis and artificial neural network. For the input of classifier, we extracted 275 input features from the first derivative signal of ECG signal and RR interval information and it was reduced to be 6 by LDA. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm with that of fuzzy inference system classifier. MIT-BIH Arrhythmia database were used as test and learning data. The performance of the proposed algorithm was 97.49% for sensitivity, 97.91% for specificity and 96.36% for accuracy. For the extraction of features, the first derivative signal of ECG is used only so that the real-time implementation of this algorithm was possible. And, on account of the reduction of feature dimensionality, the time cost for learning and testing can be expected.

AB - In this paper, we proposed a heartbeat classification algorithm based on linear discriminant analysis and artificial neural network. For the input of classifier, we extracted 275 input features from the first derivative signal of ECG signal and RR interval information and it was reduced to be 6 by LDA. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm with that of fuzzy inference system classifier. MIT-BIH Arrhythmia database were used as test and learning data. The performance of the proposed algorithm was 97.49% for sensitivity, 97.91% for specificity and 96.36% for accuracy. For the extraction of features, the first derivative signal of ECG is used only so that the real-time implementation of this algorithm was possible. And, on account of the reduction of feature dimensionality, the time cost for learning and testing can be expected.

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Classification of heartbeats based on linear discriminant analysis and artificial neural network

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