Coding Prony's method in MATLAB and applying it to biomedical signal filtering
Autores
Fernández Rodríguez, Alfredo José; Santiago Rodrigo, Luis de; López Guillén, María Elena; Rodríguez Ascariz, José Manuel; Miguel Jiménez, Juan Manuel; [et al.]Identificadores
Enlace permanente (URI): http://hdl.handle.net/10017/37431DOI: 10.1186/s12859-018-2473-y
ISSN: 1471-2105
Fecha de publicación
2018-11-26Patrocinadores
Universidad de Alcalá
Agencia Estatal de Investigación
Cita bibliográfica
Fernández Rodríguez, A., de Santiago Rodrigo, L., López Guillén, E., Rodríguez Ascariz, J. M., Miguel Jiménez, J. M. and Luciano Boquete (2018) Coding Prony’s method in MATLAB and applying it to biomedical signal filtering. BMC Bioinformatics 19:451.
Palabras clave
Prony"s method
Matrix pencil
Least squares
Total least squares
Multifocal evoked visual potentials
Multiple sclerosis
Proyectos
info:eu-repo/grantAgreement/UAH//GC2016-004
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-88438-R/ES/INVESTIGACION DE LA TECNICA DE POTENCIALES EVOCADOS VISUALES MULTIFOCALES. APLICACION EN ESTUDIOS DE EVOLUCION DE ESCLEROSIS MULTIPLE Y EVALUACION DE MEDICAMENTOS/
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Versión del editor
https://doi.org/10.1186/s12859-018-2473-yDerechos
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
Background:The response of many biomedical systems can be modelled using a linear combination of damped exponential functions. The approximation parameters, based on equally spaced samples, can be obtained using Prony's method and its variants (e.g. the matrix pencil method). This paper provides a tutorial on the main polynomial Prony and matrix pencil methods and their implementation in MATLAB and analyses how they perform with synthetic and multifocal visual-evoked potential (mfVEP) signals. This paper briefly describes the theoretical basis of four polynomial Prony approximation methods: classic, least squares (LS), total least squares (TLS) and matrix pencil method (MPM). In each of these cases, implementation uses general MATLAB functions. The features of the various options are tested by approximating a set of synthetic mathematical functions and evaluating filtering performance in the Prony domain when applied to mfVEP signals to improve diagnosis of patients with multiple sclerosis (MS). Results:The code implemented does not achieve 100%-correct signal approximation and, of the methods tested, LS and MPM perform best. When filtering mfVEP records in the Prony domain, the value of the area under the receiver-operating-characteristic (ROC) curve is 0.7055 compared with 0.6538 obtained with the usual filtering method used for this type of signal (discrete Fourier transform low-pass filter with a cut-off frequency of 35 Hz). Conclusions:This paper reviews Prony's method in relation to signal filtering and approximation, provides the MATLAB code needed to implement the classic, LS, TLS and MPM methods, and tests their performance in biomedical signal filtering and function approximation. It emphasizes the importance of improving the computational methods used to implement the various methods described above.
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