A Fernández1, L de Santiago2, R Blanco3, C Pérez-Rico3, J M Rodríguez-Ascariz1, R Barea1, J M Miguel-Jiménez1, J R García-Luque1, M Ortiz del Castillo1, E M Sánchez-Morla4, L Boquete1. 1. Department of Electronics, University of Alcalá, Plaza de S. Diego, s/n, 28801 Alcalá de Henares, Spain. 2. Department of Electronics, University of Alcalá, Plaza de S. Diego, s/n, 28801 Alcalá de Henares, Spain. Electronic address: luis.desantiago@uah.es. 3. Department of Surgery, University of Alcalá, Plaza de S. Diego, s/n, 28801 Alcalá de Henares, Spain. 4. Department of Psychiatry, University Hospital of Guadalajara, Guadalajara, Spain.
Abstract
BACKGROUND: This paper describes use of Prony's method as a filter applied to multifocal visual-evoked-potential (mfVEP) signals. Prony's method can be viewed as an extension of Fourier analysis that allows a signal to be decomposed into a linear combination of functions with different amplitudes, damping factors, frequencies and phase angles. METHOD: By selecting Prony method parameters, a frequency filter has been developed which improves signal-to-noise ratio (SNR). Three different criteria were applied to data recorded from control subjects to produce three separate datasets: unfiltered raw data, data filtered using the traditional method (fast Fourier transform: FFT), and data filtered using Prony's method. RESULTS: Filtering using Prony's method improved the signals' original SNR by 44.52%, while the FFT filter improved the SNR by 33.56%. The extent to which signal can be separated from noise was analysed using receiver-operating-characteristic (ROC) curves. The area under the curve (AUC) was greater in the signals filtered using Prony's method than in the original signals or in those filtered using the FFT. CONCLUSION: filtering using Prony's method improves the quality of mfVEP signal pre-processing when compared with the original signals, or with those filtered using the FFT.
BACKGROUND: This paper describes use of Prony's method as a filter applied to multifocal visual-evoked-potential (mfVEP) signals. Prony's method can be viewed as an extension of Fourier analysis that allows a signal to be decomposed into a linear combination of functions with different amplitudes, damping factors, frequencies and phase angles. METHOD: By selecting Prony method parameters, a frequency filter has been developed which improves signal-to-noise ratio (SNR). Three different criteria were applied to data recorded from control subjects to produce three separate datasets: unfiltered raw data, data filtered using the traditional method (fast Fourier transform: FFT), and data filtered using Prony's method. RESULTS: Filtering using Prony's method improved the signals' original SNR by 44.52%, while the FFT filter improved the SNR by 33.56%. The extent to which signal can be separated from noise was analysed using receiver-operating-characteristic (ROC) curves. The area under the curve (AUC) was greater in the signals filtered using Prony's method than in the original signals or in those filtered using the FFT. CONCLUSION: filtering using Prony's method improves the quality of mfVEP signal pre-processing when compared with the original signals, or with those filtered using the FFT.
Authors: A Fernández Rodríguez; L de Santiago Rodrigo; E López Guillén; J M Rodríguez Ascariz; J M Miguel Jiménez; Luciano Boquete Journal: BMC Bioinformatics Date: 2018-11-26 Impact factor: 3.169
Authors: Luis de Santiago; Eva Sánchez-Morla; Román Blanco; Juan Manuel Miguel; Carlos Amo; Miguel Ortiz Del Castillo; Almudena López; Luciano Boquete Journal: PLoS One Date: 2018-04-20 Impact factor: 3.240
Authors: Luis de Santiago; E M Sánchez Morla; Miguel Ortiz; Elena López; Carlos Amo Usanos; M C Alonso-Rodríguez; R Barea; Carlo Cavaliere-Ballesta; Alfredo Fernández; Luciano Boquete Journal: PLoS One Date: 2019-04-04 Impact factor: 3.240