Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis.

As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients.

Diagnostic ability of multifocal electroretinogram in early multiple sclerosis using a new signal analysis method.

PURPOSE: To determine if a novel analysis method will increase the diagnostic value of the multifocal electroretinogram (mfERG) in diagnosing early-stage multiple sclerosis (MS). METHODS: We studied the mfERG signals of OD (Oculus Dexter) eyes of fifteen patients diagnosed with early-stage MS (in all cases < 12 months) and without a history of optic neuritis (ON) (F:M = 11:4), and those of six controls (F:M = 3:3). We obtained values of amplitude and latency of N1 and P1 waves, and a method to assess normalized root-mean-square error (FNRMSE) between model signals and mfERG recordings was used. Responses of each eye were analysed at a global level, and by rings, quadrants and hemispheres. AUC (area under the ROC curve) is used as discriminant factor. RESULTS: The standard method of analysis obtains further discrimination between controls and MS in ring R3 (AUC = 0.82), analysing N1 waves amplitudes. In all of the retina analysis regions, FNRMSE value shows a greater discriminating power than the standard method. The highest AUC value (AUC = 0.91) was in the superior temporal quadrant. CONCLUSION: By analysing mfERG recordings and contrasting them with those of healthy controls it is possible to detect early-stage MS in patients without a previous history of ON.

A signal-to-noise-ratio-based analysis of multifocal visual-evoked potentials in multiple sclerosis risk assessment.

OBJECTIVE: To study the value of using the signal-to-noise ratio (SNR) of multifocal visual-evoked potentials (mfVEPs) in assessment of subjects at risk of developing multiple sclerosis (MS). METHODS: MfVEP signals were obtained from 15 patients with radiologically isolated syndrome (RIS), from 28 patients with clinically isolated syndrome (CIS), from 28 with clinically definite MS and from 24 control subjects. The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). The mfVEPs' SNR was obtained for both the whole visual field and at various eccentric rings. The area under the curve (AUC) was calculated by comparing the control subjects' mfVEP SNR values with those of the RIS, CIS and MS groups. RESULTS: In whole visual field analysis, risk of developing MS increased as SNR decreased (SNRCONTROL=0.70, SNRRIS=0.62, SNRCIS-nonON=0.54, SNRCIS-ON=0.40, SNRMS-nonON=0.52, SNRMS-ON=0.40). Ring 5 (9.8°-15° eccentricity) was most affected by the SNR decrease, as indicated by its higher AUC values (AUCFULL_EYE=0.81, AUCRING_5=0.89). A significant relationship (Spearman correlation, ρRING_5=0.61) between SNR values and disability severity on the Expanded Disability Status Scale (EDSS) was observed in clinically definite MS patients. CONCLUSION: A new method based on analysis of the SNR of mfVEP signal amplitude improves assessment of patients at risk of developing MS. SIGNIFICANCE: Improved mfVEP assessment of MS-risk patients was achieved by using SNR values at 9.8°-15° eccentricity of the visual field.

Filtering multifocal VEP signals using Prony's method.

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.

Filtering mfVEP signals using Wiener filter.

The multifocal visual-evoked-potential (mfVEP) signals are filtered using the Wiener filter combined with a Fast Fourier Transform and their signal-to-noise ratios are compared against those of unfiltered signals (RAW data) and those of signals filtered using the traditional method (FFT data). The Wiener filter improves the original signals' SNR by 37.49%, while the FFT improves the SNR by 20.41%. This gain is achieved by selecting the best channel in each sector of the visual field. In conclusion, filtering using the Wieners method improves the quality of mfVEP signal pre-processing when compared against the original signals, or against filtering using the FFT.