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Simultaneous Detection and Discrimination of Subsequences Which Are Nonlinearly Extended Elements of the Given Sequences Alphabet in a Quasiperiodic Sequence

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Optimization and Applications (OPTIMA 2020)

Abstract

We consider a posteriori approach to the problem of noise-proof simultaneous detection and discrimination of subsequences-fragments having some given properties in a quasiperiodic sequence. The solution to the problem is stated for the case when the quantity of sought subsequences is unknown. We assume that 1) a finite alphabet of reference sequences is given; 2) a set of permissible deformations is defined for the alphabet, this set gathers all possible extensions of its elements (by duplicating their components); 3) every subsequence-fragment in the quasiperiodic sequence belongs to the set of permissible deformations; 4) subsequences-fragments do not intersect each other, and the difference between the initial positions of two neighboring fragments is limited from above by a given value.

We show that in the framework of a posteriori approach, the problem of simultaneous detection and discrimination reduces to solving an unexplored discrete optimization problem. A polynomial-time algorithm that guarantees the optimal solution to this optimization problem is proposed. The results of the numerical simulation are presented.

The study was supported by the Russian Foundation for Basic Research, projects 19-07-00397 and 19-01-00308, by the Russian Academy of Science (the Program of basic research), project 0314-2019-0015.

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References

  1. Rajni, R., Kaur, I.: Electrocardiogram signal analysis – an overview. Int. J. Comput. Appl. 84(7), 22–25 (2013)

    Google Scholar 

  2. Al-Ani, M.S.: ECG waveform classification based on P-QRS-T wave recognition. UHD J. Sci. Technol. 2(2), 7–14 (2018)

    Article  Google Scholar 

  3. Shelley, K., Shelley, S.: Pulse oximeter waveform: photoelectric plethysmography. In: Lake, C., Hines, R., Blitt, C. (eds.) Clinical Monitoring, pp. 420–428. W.B. Saunders Company, Philadelphia (2001)

    Google Scholar 

  4. Elgendy, M.: On the analysis of fingertrip photoplethysmogram signals. Curr. Cardiol. Rev. 8(1), 14–25 (2012)

    Article  Google Scholar 

  5. Anderson, B.D., Moore, J.D.: Optimal Filtering. Prentice Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  6. Sparks, T., Chase, G.: Filters and Filtration Handbook. Butterworth-Heinemann, Oxford (2015)

    Google Scholar 

  7. Polunchenko, A., Tartakovsky, A.: State-of-the-art in sequential change-point detection. Methodol. Comput. Appl. Probab. 14(3), 649–684 (2012). https://doi.org/10.1007/s11009-011-9256-5

    Article  MathSciNet  MATH  Google Scholar 

  8. Poor, H.V., Hadjiliadis, O.: Quickest Detection. Cambridge University Press, Cambridge (2008)

    Book  Google Scholar 

  9. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic, New York (1990)

    MATH  Google Scholar 

  10. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2000)

    MATH  Google Scholar 

  11. Kel’manov, A., Khamidullin, S., Mikhailova, L., Ruzankin, P.: Polynomial-time solvability of one optimization problem induced by processing and analyzing quasiperiodic ECG and PPG signals. In: Jaćimović, M., Khachay, M., Malkova, V., Posypkin, M. (eds.) OPTIMA 2019. CCIS, vol. 1145, pp. 88–101. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38603-0_7

    Chapter  Google Scholar 

  12. Kel’manov, A.V., Okol’nishnikova, L.V.: A posteriori simultaneous detection and discrimination of subsequences in a quasiperiodic sequence. Pattern Recogn. Image Anal. 11(3), 505–520 (2001)

    Google Scholar 

  13. Kel’manov, A.V., Khamidullin, S.A.: A posteriori joint detection and discrimination of a given number of subsequences in a quasiperiodic sequence. Pattern Recogn. Image Anal. 10(3), 379–388 (2000)

    MATH  Google Scholar 

  14. Kel’manov, A.V., Khamidullin, S.A.: A posteriori concurrent detection and identification of quasiperiodic fragments in a sequence from their pieces. Pattern Recogn. Image Anal. 16(4), 599–613 (2006)

    Article  Google Scholar 

  15. Kel’manov, A.V., Khamidullin, S.A.: Simultaneous a posteriori detection and identification of a predetermined number of quasiperiodic fragments in a sequence based on their segments. Pattern Recogn. Image Anal. 16(3), 344–357 (2006)

    Article  Google Scholar 

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Correspondence to Liudmila Mikhailova .

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Mikhailova, L., Khamdullin, S. (2020). Simultaneous Detection and Discrimination of Subsequences Which Are Nonlinearly Extended Elements of the Given Sequences Alphabet in a Quasiperiodic Sequence. In: Olenev, N., Evtushenko, Y., Khachay, M., Malkova, V. (eds) Optimization and Applications. OPTIMA 2020. Lecture Notes in Computer Science(), vol 12422. Springer, Cham. https://doi.org/10.1007/978-3-030-62867-3_16

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