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THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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A Multilinear LDA Method of Tensor Representation for ECG Signal Based Individual Identification

심전도 신호기반 개인식별을 위한 텐서표현의 다선형 판별분석기법

  • 임원철 (조선대학교 소프트웨어융합공학과) ;
  • 곽근창 (조선대학교 전자공학부)
  • Received : 2018.09.27
  • Accepted : 2018.11.21
  • Published : 2018.12.31
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Abstract

A Multilinear LDA Method of Tensor Representation for ECG Signal Based Individual Identification Electrocardiogram signals, included in the cardiac electrical activity, are often analyzed and used for various purposes such as heart rate measurement, heartbeat rhythm test, heart abnormality diagnosis, emotion recognition and biometrics. The objective of this paper is to perform individual identification operation based on Multilinear Linear Discriminant Analysis (MLDA) with the tensor feature. The MLDA can solve dimensional aspects of classification problems in high-dimensional tensor, and correlated subspaces can be used to distinguish between different classes. In order to evaluate the performance, we used MPhysionet's MIT-BIH database. The experimental results on this database showed that the individual identification by MLDA outperformed that by PCA and LDA.

심전도 신호는 기본적으로 심장의 전기적 활동에 포함되며 이를 통해 심박수 측정, 심장 박동의 리듬 검사, 심장 이상 진단, 정서 인식 및 생체 인식과 같은 다양한 목적으로 분석 및 활용된다. 본 논문의 목적은 다차원 데이터 배열인 텐서 특성을 가진 다선형 판별분석(MLDA: Multilinear Linear Discriminant Analysis) 기법을 이용하여 개인식별을 수행하고자 한다. MLDA는 상위 차원의 텐서를 포함하는 분류 문제에 대해서 차원 문제를 해결 할 수 있으며, 상호 연관된 부분 공간은 서로 다른 클래스를 구별하기 위해 사용될 수 있다. 제시된 방법의 성능을 검증하기 위해 Physionet의 MIT-BIH데이터베이스를 적용하였다. 이 데이터베이스에 대해 실험한 결과, MLDA는 기존 PCA와 LDA와 비교하여 개인식별 성능이 우수함을 확인하였다.

Keywords

References

  1. S. Gutta, Q. Cheng, "Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition," IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 2, pp. 460-468, 2015. https://doi.org/10.1109/JBHI.2015.2402199
  2. S. I Safie, J. J. Soraghan, L. Petropoulakis, "ECG biometric authentication using Pulse Active Width (PAW)," Biometric Measurements and Systems for Security and Medical Applications(BIOMS), pp. 1-6, Nov. 2011.
  3. S. Y, Kim, S. J. Kim, I. J. Joe, "Enhanced Password Based User Authentication Mechanism Using Mobile Storage Medium/Channel," Journal of the Korea Contents Association, vol. 6, no.1, pp. 144-151, Jan. 2006.
  4. 조주희, 조병준, 이대종, 전명근, "주성분 분석기법을 이용한 심전도 기반 개인인증," 전기학회논문지, 제66권, 제4호, 258-262쪽, 2017년 12월 https://doi.org/10.5370/KIEEP.2017.66.4.258
  5. F. G. S. Teodoro, S. M. Peres. C. A. M. Lima, "Feature selection for biometric recognition based on electrocardiogram signals," 2017 International Joint Conference on Neural Networks(IJCNN), pp.14-19, May 2017.
  6. J. J. Wu, Y. Zhang, "ECG identification based on neural networks," 2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP), pp. 92-96, Dec. 2014.
  7. I. Daubechies, "The wavelet transform, time-frequency localization and signal analysis," IEEE Transactions on Information Theory, vol. 36, no. 5, pp. 961-1005, 1990. https://doi.org/10.1109/18.57199
  8. H. Abdi, L. J. Williams, "Principal component analysis," WIREs Computational Statistics, vol. 2, no. 4, pp. 433-459, 2010. https://doi.org/10.1002/wics.101
  9. T. Alaaa, G. Tarekc, I. Abdelhameed, H. A. Ellae, "Linear discriminant analysis: A detailed tutorial," AI Communications, vol. 30, no. 2, pp. 169-190, 2017. https://doi.org/10.3233/AIC-170729
  10. JG. Dy, CE, Brodley, "Feature Selection for Unsupervised Learning," Journal of Machine Learning Research, vol. 5, pp.845-889, 2004.
  11. R. R. Perez, A. Marques, F. Mohammadi, "The application of supervised learning through feed-forward neural networks for ECG signal classification," Electrical and Computer Engineering, pp. 15-18, 2016.
  12. 오강한, 김수형, 나인섭, 김영철, 문창협, "스마트장치에서 비접촉식 전위계차 센서 신호를 이용한 동작 인식 기법," 스마트미디어저널, 제3권, 제2호, 14-19쪽, 2014년 6월
  13. H. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "A survey of multilinear subspace learning for tensor data," Pattern Recognition, vol. 44, no. 7, pp. 1540-1551, 2011. https://doi.org/10.1016/j.patcog.2011.01.004
  14. H. Lu, K. N. Plataniotis, A.N. Venetsanopoulos, "Boosting LDA with Regularization on MPCA atures for Gait Recognition," 2007 Biometrics Symposium, pp. 1-6, Sept. 2017.
  15. S. M. Hosseyninia, F. Roosta, A. A. S. Baboli. G. R. Rad, "Improving the performance of MPCA+MDA for face recognition," 2011 19th Iranian Conference on Electrical Engineering, pp.17-19, May 2011.
  16. S. Yan, D. Xu, Q. Yang, L. Zhang, X. Tang, H. J. Zhang, "Multilinear Discriminant Analysis for Face Recognition," IEEE Transactions on Image Processing, vol. 16, no. 1, pp. 212-220, 2007. https://doi.org/10.1109/TIP.2006.884929
  17. 문해민, 박진원, 반성범, "역전파가 제거된 CNN과 LDA를 이용한 얼굴 영상 해상도별 얼굴 인식률 분석, LDA, CNN," 스마트미디어저널, 제5권, 제1호, 1-6쪽, 2016년 3월
  18. P. Comon, "Independent component analysis, A new concept?," Signal Processing, vol. 36, no. 3, pp. 287-314, 1994. https://doi.org/10.1016/0165-1684(94)90029-9
  19. S. Ji, J, Ye, "Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection," IEEE Transactions on Neural Networks, vol. 19, no. 10, pp. 1768-1782, 2008. https://doi.org/10.1109/TNN.2008.2002078
  20. J. Huang, K. Su, J. El-Den, T. Hu, J. Li, "An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition," Mathematical Problems in Engineering, pp. 1-12, 2014.
  21. H. Lu ; K. N. Plataniotis, A. N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects," IEEE Transactions on Neural Networks, vol. 19, no. 1, pp. 18-39, 2008. https://doi.org/10.1109/TNN.2007.901277
  22. G.B. Moody. R.G. Mark, "The impact of the MIT-BIH Arrhythmia Database," IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45-50, May-June. 2001. https://doi.org/10.1109/51.932724
  23. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full];2000 (accessed 11, 07, 2018).
  24. H. Abbaspour, S. M. Razavi, N. Mehrshad, "Electrocardiogram Based Identification using a New Effective Intelligent Selection of Fused Features," Journal of Medical Signal and Sensors, vol. 5, pp. 30-39, 2015.
  25. M. M. Bassiouni, E. S. A. E. Dahshan, W. Khalefa, A. M. Salem, "Intelligent hybrid approaches for human ECG signals identification," Signal, Image and Video Processing-Springer, vol. 12, no. 5, pp. 941-949, 2018. https://doi.org/10.1007/s11760-018-1237-5