RT info:eu-repo/semantics/article
T1 Coding Prony's method in MATLAB and applying it to biomedical signal filtering
A1 Fernández Rodríguez, Alfredo José
A1 Santiago Rodrigo, Luis de
A1 López Guillén, María Elena
A1 Rodríguez Ascariz, José Manuel
A1 Miguel Jiménez, Juan Manuel
A1 Boquete Vázquez, Luciano
K1 Prony"s method
K1 Matrix pencil
K1 Least squares
K1 Total least squares
K1 Multifocal evoked visual potentials
K1 Multiple sclerosis
K1 Electrónica
K1 Electronics
AB 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.
SN 1471-2105
YR 2018
FD 2018-11-26
LK http://hdl.handle.net/10017/37431
UL http://hdl.handle.net/10017/37431
LA eng
NO Universidad de Alcalá
DS MINDS@UW
RD 20-abr-2024