https://doi.org/10.1051/epjconf/201922403004
Oscillatory Models for Biological Signal Processing and Pattern Recognition
1
Univ. Grenoble Alpes, CEA, LETI, CLINATEC, F-38000 Grenoble, France
2
Plekhanov Russian University of Economics, RU-117997, Moscow, Russia
* e-mail: tetiana.aksenova@cea.fr
Published online: 9 December 2019
Among biomedical signals, repetitive or quasi-periodic signals are particularly widespread. While the periodic component is still presented these signals are characterized by period variations (fundamental frequency, amplitude, etc.). The lack of synchronization or phase shifts results in variations in similar segments’ durations, nominally identical signals demonstrate a variation at peak retention times, etc. The inverse methods of oscillation theory were proposed recently as a tool to solve the problems of modelling of repetitive signals with phase shift. In the article, the inverse method of oscillation theory is considered as a tool to solve the problems of supervised and non-supervised classification, and filtering of repetitive signals with phase shift. Examples of application are presented.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
