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Computational implementation of the Cascade Hilbert-Zero Decomposition and perspectives of its applications for biophysical signal processing. / Postnikov, Eugene B.; Lebedeva, Elena A.; Zyubin, Andrey Yu; Lavrova, Anastasia I.

In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 12194, 1219404, 01.01.2022.

Research output: Contribution to journal › Article › peer-review

Harvard

Postnikov, EB, Lebedeva, EA, Zyubin, AY & Lavrova, AI 2022, 'Computational implementation of the Cascade Hilbert-Zero Decomposition and perspectives of its applications for biophysical signal processing', Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12194, 1219404. https://doi.org/10.1117/12.2625864

APA

Postnikov, E. B., Lebedeva, E. A., Zyubin, A. Y., & Lavrova, A. I. (2022). Computational implementation of the Cascade Hilbert-Zero Decomposition and perspectives of its applications for biophysical signal processing. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 12194, [1219404]. https://doi.org/10.1117/12.2625864

Vancouver

Postnikov EB, Lebedeva EA, Zyubin AY, Lavrova AI. Computational implementation of the Cascade Hilbert-Zero Decomposition and perspectives of its applications for biophysical signal processing. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022 Jan 1;12194. 1219404. https://doi.org/10.1117/12.2625864

Author

Postnikov, Eugene B. ; Lebedeva, Elena A. ; Zyubin, Andrey Yu ; Lavrova, Anastasia I. / Computational implementation of the Cascade Hilbert-Zero Decomposition and perspectives of its applications for biophysical signal processing. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022 ; Vol. 12194.

BibTeX

@article{4cd308ffdb954ffa83312a43f1e48f7c,
title = "Computational implementation of the Cascade Hilbert-Zero Decomposition and perspectives of its applications for biophysical signal processing",
abstract = "We present a detailed description of the numerical implementation (for either MATLAB or GNU Octave) of a novel method for processing data with serial localized peaks intended to distinguish between individual components even when they form a mono-modal but complicatedly shaped structure [E.B. Postnikov et al. Mathematics 9 (2021) 2802]. The essence of the method consists of a cascade of local non-linear approximations by the Gaussian function at the vicinity of the zero-crossings for the signal{\textquoteright}s Hilbert transform. At the first level, this procedure is applied to the processed signal directly; at the next level, it is applied to residuals between the signal and approximations on the previous levels. As a practical example, we consider the decomposition of Raman spectra recorded from different strains of Mycobacterium tuberculosis. Finally, we discuss other areas of applicability for the proposed method of signal processing.",
keywords = "Mycobacterium tuberculosis, Raman spectroscopy, signal processing",
author = "Postnikov, {Eugene B.} and Lebedeva, {Elena A.} and Zyubin, {Andrey Yu} and Lavrova, {Anastasia I.}",
year = "2022",
month = jan,
day = "1",
doi = "10.1117/12.2625864",
language = "English",
volume = "12194",
journal = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
issn = "1605-7422",
publisher = "SPIE",

}

RIS

TY - JOUR

T1 - Computational implementation of the Cascade Hilbert-Zero Decomposition and perspectives of its applications for biophysical signal processing

AU - Postnikov, Eugene B.

AU - Lebedeva, Elena A.

AU - Zyubin, Andrey Yu

AU - Lavrova, Anastasia I.

PY - 2022/1/1

Y1 - 2022/1/1

N2 - We present a detailed description of the numerical implementation (for either MATLAB or GNU Octave) of a novel method for processing data with serial localized peaks intended to distinguish between individual components even when they form a mono-modal but complicatedly shaped structure [E.B. Postnikov et al. Mathematics 9 (2021) 2802]. The essence of the method consists of a cascade of local non-linear approximations by the Gaussian function at the vicinity of the zero-crossings for the signal’s Hilbert transform. At the first level, this procedure is applied to the processed signal directly; at the next level, it is applied to residuals between the signal and approximations on the previous levels. As a practical example, we consider the decomposition of Raman spectra recorded from different strains of Mycobacterium tuberculosis. Finally, we discuss other areas of applicability for the proposed method of signal processing.

AB - We present a detailed description of the numerical implementation (for either MATLAB or GNU Octave) of a novel method for processing data with serial localized peaks intended to distinguish between individual components even when they form a mono-modal but complicatedly shaped structure [E.B. Postnikov et al. Mathematics 9 (2021) 2802]. The essence of the method consists of a cascade of local non-linear approximations by the Gaussian function at the vicinity of the zero-crossings for the signal’s Hilbert transform. At the first level, this procedure is applied to the processed signal directly; at the next level, it is applied to residuals between the signal and approximations on the previous levels. As a practical example, we consider the decomposition of Raman spectra recorded from different strains of Mycobacterium tuberculosis. Finally, we discuss other areas of applicability for the proposed method of signal processing.

KW - Mycobacterium tuberculosis

KW - Raman spectroscopy

KW - signal processing

UR - http://www.scopus.com/inward/record.url?scp=85132004562&partnerID=8YFLogxK

U2 - 10.1117/12.2625864

DO - 10.1117/12.2625864

M3 - Article

AN - SCOPUS:85132004562

VL - 12194

JO - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

JF - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

SN - 1605-7422

M1 - 1219404

ER -

ID: 104111144