M-sequences in ophthalmic electrophysiology

Müller, Philipp L.; Meigen, Thomas

doi:10.1167/16.1.15

Cited by 11 publications

(10 citation statements)

References 128 publications

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“…The hexagons alternated black and white in a pseudorandom binary m-sequence. 24 The luminance of the gray background surrounding the stimulus was 108 cd/m 2 and that of the dark and bright hexagons were 2.8 and 184 cd/m 2 , respectively. The global flash stimulus was inserted between successive focal flashes such that the stimulation sequence consisted of a multifocal flash frame, a dark frame, a full screen global flash and a dark frame in each cycle (Fig.…”

Section: Electrophysiology Proceduresmentioning

confidence: 99%

The Effect of Atropine on Human Global Flash mfERG Responses to Retinal Defocus

Khanal

Turnbull

Lee

et al. 2019

Invest. Ophthalmol. Vis. Sci.

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PURPOSE. To investigate the action of atropine on global flash multifocal electroretinogram (gmfERG) responses to retinal defocus. METHOD. gmfERG recordings were made monocularly in 19 healthy adults under three lensimposed defocus conditions (2 diopters myopic, 2 diopters hyperopic, and no defocus) before and 24 hours after instillation of 1 drop of 0.1% atropine. Signals reflecting activity from the outer and inner retina (direct [DC] and induced [IC] components respectively) were analyzed. Responses were grouped into either a central (08-68) or peripheral (68-248) retinal zone. The gmfERG responses were compared relative to the no defocus, no atropine condition. RESULTS. Within the central zone, atropine had no effect on the amplitudes and peak times of DC or IC responses to defocus. For IC responses in the peripheral zone, there was a significant interaction effect of atropine and defocus (F 2,36 ¼ 6.050, P ¼ 0.011) with greater post-atropine amplitudes under myopic defocus (mean increase ¼ 15.5%, 95% confidence interval [CI] ¼ 5.6%-25.4%, P ¼ 0.004). Atropine also had a significant main effect of increasing IC peak times (F 1,18 ¼ 9.722, P ¼ 0.006). For DC responses, atropine had a significant main effect of increasing DC amplitudes (F 1,18 ¼ 7.821, P ¼ 0.012) and peak times (F 1,18 ¼ 15.406, P ¼ 0.001) regardless of sign of defocus. CONCLUSIONS. Our results imply that atropine acts in the inner layers of the peripheral retina to affect neuronal responses to myopic defocus, raising the possibility that atropine may potentiate the effects of myopic defocus in inhibiting eye growth.

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Section: Electrophysiology Proceduresmentioning

confidence: 99%

The Effect of Atropine on Human Global Flash mfERG Responses to Retinal Defocus

Khanal

Turnbull

Lee

et al. 2019

Invest. Ophthalmol. Vis. Sci.

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“…60) of sectors of the visual field. Generation of the visual stimulus is governed by the same pseudorandom sequence [ 29 ] used to separate the individual responses of each sector from the continual EEG recording obtained using electrodes. Analysis of mfVEP signals is employed in diagnosis and study of patients with glaucoma, amblyopia, nerve optic drusses, optic neuritis, multiple sclerosis and other pathologies.…”

Section: Resultsmentioning

confidence: 99%

Coding Prony’s method in MATLAB and applying it to biomedical signal filtering

et al. 2018

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BackgroundThe 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).ResultsThe 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).ConclusionsThis 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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“…This technique divides the retina into hexagonal regions or sectors scaled with eccentricity (typical values are 61 or 103 sectors, according to the ISCEV (International Society for Clinical Electrophysiology of Vision) standard for clinical multifocal electroretinography [11]). It takes a quasi-simultaneous recording of each of sector using a visual stimulation pattern governed by a pseudo-random white/black sequence (m-sequence) [12].…”

Section: Introductionmentioning

confidence: 99%

“…The response most commonly used in mfERG is the first-order kernel, which generates a recording consisting of a negative wave (N1) followed by a maximum positive (P1), and occasionally, another negative wave (N2). N1 seems to represent the contribution from the cells that generate the a-wave, while P1 and N2 represent the cells generating the b-wave and oscillatory potentials in the light-adapted full-field ERG [11,12].…”

Section: Introductionmentioning

confidence: 99%

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

Santiago

Castillo

García‐Martín

et al. 2019

Sensors

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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. Author Contributions: Conceptualization, E.G.-M., M.J.R., E.M.S.M., and L.B.; methodology, L.d.S., M.O.d.C., C.C., J.M.M., A.L., and B.C.; software, L.d.S., M.O.d.C., C.C., J.M.M., and A.L.; validation, L.d.S., M.O.d.C., C.C., J.M.M., and A.L.; formal Analysis, M.O.d.C. and J.M.M.; investigation, E.G.-M., M.J.R., E.M.S.M., and L.B.; resources, L.B. and E.G.-M.; data Curation, L.d.S., M.O.d.C., C.C., J.M.M., A.L., and B.C.; writing-original draft preparation, L.B., E.G.-M., and M.J.R.; writing-review and editing, L.B., E.G.-M., and M.J.R.; visualization, L.B. and E.G.-M.; supervision, L.B. and E.G.-M.; project administration, L.B. and E.G.-M.; funding acquisition, L.B. and E.G.-M. All authors have read and agreed to the published version of the manuscript.

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M-sequences in ophthalmic electrophysiology

Cited by 11 publications

References 128 publications

The Effect of Atropine on Human Global Flash mfERG Responses to Retinal Defocus

The Effect of Atropine on Human Global Flash mfERG Responses to Retinal Defocus

Coding Prony’s method in MATLAB and applying it to biomedical signal filtering

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

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