Construction of wavelet dictionaries for ECG modeling

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24510%2F21%3A00009609" target="_blank" >RIV/46747885:24510/21:00009609 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2215016121001072?via=ihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2215016121001072?via=ihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.mex.2021.101314" target="_blank" >10.1016/j.mex.2021.101314</a>

Result language
angličtina
Original language name
Construction of wavelet dictionaries for ECG modeling
Original language description
Technical details, algorithms, and MATLAB implementation for a method advanced in the paper ``Wavelet Based Dictionaries for Dimensionality Reduction of ECG Signals‘‘, are presented. This work aims to be the companion of that publication, in which an adaptive mathematical model for a given ECG record is proposed. The method comprises two building blocks. The first one is a construction of a suitable redundant set, called ‘dictionary‘, for decomposing an ECG signal as a superposition of elementary components, called ‘atoms‘, selected from that dictionary. The second step is an implementation of the greedy strategy Optimized Orthogonal Matching Pursuit (OOMP) for selecting the atoms intervening in the signal decomposition. This paper gives the details of the algorithms for implementing stage (i), which is not fully elaborated in the previous publication. The proposed dictionaries are constructed from known wavelet families, but translating the prototypes with a shorter step than that corresponding to a wavelet basis. Stage (ii) is readily implementable by the available function OOMP. The use of the software and the power of the technique are illustrated by reducing the dimensionality of ECG records taken from the MIT-BIH Arrhythmia Database.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20602 - Medical laboratory technology (including laboratory samples analysis; diagnostic technologies) (Biomaterials to be 2.9 [physical characteristics of living material as related to medical implants, devices, sensors])

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)