Lithuanian University of Health Sciences
Department of Physics, Mathematics and Biophysics
“Quantitative evaluation of shape of visually evoked potentials”
Antonio León Vivó
Algimantas Kriščiukaitis
Kaunas 2016/2017
TABLE OF CONTENTS
SUMMARY 3
ACKNOWLEDGEMENTS 4
CONFLICT OF INTEREST 5
CLEARANCE ISSUED BY ETHICS COMMITTEE 6
ABBREVIATIONS 7
INTRODUCTION 8
AIM AND OBJECTIVES OF THE THESIS 9
LITERATURE REVIEW 10
Waveform: 11
Basic technology: 12
Standard VEP stimulation: flash, and pattern. 13
Recording parameters: 14
Problems from averaging: 14
Sweeps: 15
Preliminary signal processing: 17
PCA - Principal Component Analysis: 19
Clinical significance and advantages of VEPs: 19
VEP in clinical ophthalmology and neuro-ophthalmology abnormalities: 21
Examples of VEP recordings: 24
RESEARCH METHODOLOGY AND METHODS 28
RESULTS 32
DISCUSSION OF THE RESULTS 36
CONCLUSIONS 37
SUMMARY
Antonio León Vivó. “Quantitative evaluation of shape of visually evoked potentials”.
Event-related potentials and particular case – visual evoked potentials are electrophysiological events reflecting sensory, cognitive or motor reaction. The observed electrical activity (brief deflections, and valleys) is concurrent with other background electrical activity registered from the brain. So, in reality we register the mixture of the signals and methods for separation of visual evoked potentials from background EEG should be elaborated. Quantitative evaluation of visual evoked potential parameters (latency and amplitude of the principal peaks and valleys) provides useful information for studying brain processes and is important for clinical diagnostics.
The aim of this work was to reveal most informative quantitative estimates of
single-sweep visually evoked potentials shape reflecting optical nerve function related pathologies.
Objectives: Theoretically analyze existing methods of visually evoked potentials
shape analysis, in particular, methods for single sweep analysis; Analyze visually evoked potentials recordings using MATLAB computational environment based-software and select informative signal shape reflecting parameters; Reveal most informative signal shape parameters reflecting pathologies related with optic nerve malfunction (neuritis, SD and other). A review of publications about VEP genesis, methods of their registration, preprocessing and evaluation revealed need of further development of methods, based on single sweep analysis.
Methods: Set of clinical recordings of VEPs registered in “Akiu Klinika" of Lithuanian
University of Health Sciences used for analysis supplemented by synthetic recordings generated to imitate selected pathologies caused changes in VEP shapes. Principal Component Analysis based method for extraction of VEPs from the background EEG was used for signal preprocessing. Wavelet transform based method was proposed to estimate time and frequency characteristics of pre-processed VEPs. Validation of the method was performed on simulated data.
Conclusions: Theoretical analysis and review of publications showed that VEPs and
other event related potentials carry important diagnostic information, so development of advanced methods for their recording and analysis, especially single trial analysis, is very important for clinical use. Analyzing VEP for reconstruction, it is shown that every single-sweep with finite amount of single-sweeps, is possible by means of multivariate analysis methods (e.g. PCA, KLT). We proposed to use wavelet transform for evaluation of specific changes in VEPs related with optic nerve malfunction.
ACKNOWLEDGEMENTS
This research is a part of my integrated studies in the Lithuanian University of Health Sciences as a Final Master Thesis, during my 6th year of medical studies.
I would like to thank personally Prof. Algimantas Kriščiukaitis who helped, taught and advised me throughout the whole research work.
CONFLICT OF INTEREST
CLEARANCE ISSUED BY ETHICS COMMITTEE
Title: Quantitative evaluation of shape of visually evoked potentials. Date of issue: 2016-12-13.
ABBREVIATIONS
EP - Evoked potentials.
VER - Visual evoked response. VEP - Visual evoked potentials.
SEPs - Short-latency somatosensory evoked potentials. BAEPs - Short-latency brainstem auditory evoked potentials. sVEP - sweep visual evoked potential.
EEG - Electroencephalogram.
ISCEV - International Society for Clinical Electrophysiology of Vision. ERG - Electroretinography.
SNR - signal-to-noise ratio.
KLT - Karhunen-Loève transform. MS - Multiple sclerosis.
mfVEP - Multifocal visual evoked potentials. FVEPs - Flash visual evoked potentials. PVEP - Patterned visual evoked potentials. PERG - Pattern electroretinogram.
CT - Computer tomography.
MRI - Magnetic resonance imaging. PCA - Principal Component Analysis. AR - Autoregressive.
CA - Conventional (coherent) averaging. TOC- Third-order correlation.
SVD - Singular value decomposition. SNR - single-sweep Signal-to-noise ratio.
INTRODUCTION
Event-related potentials and particular case – visual evoked potentials are electrophysiological events reflecting sensory, cognitive or motor reaction. The observed electrical activity (brief deflections, and valleys) is concurrent with other background electrical activity registered from the brain. So, in reality we register the mixture of the signals and methods for separation of visual evoked potentials from background EEG should be elaborated.
Quantitative evaluation of visual evoked potential parameters (latency and amplitude of the principal peaks and valleys) provides useful information for studying brain processes and is important for clinical diagnostics.
AIM AND OBJECTIVES OF THE THESIS
Aim: To reveal most informative quantitative estimates of single-sweep visually
evoked potentials shape reflecting optical nerve function related pathologies.
Objectives: to theoretically analyze existing methods of visually evoked potentials
shape analysis, in particular, methods for single sweep analysis; to analyze visually evoked potentials recordings using MATLAB computational environment based-software and select informative signal shape reflecting parameters; to reveal most informative signal shape parameters reflecting pathologies related with optic nerve malfunction (neuritis, SD and other).
LITERATURE REVIEW
Visually evoked potentials (VEPs) can be defined as electrophysiological stimulation responses which can be patterned or un-patterned visual stimuli. This stimulation, which is at a low rate (up to 4/s), creates transient VEPs. If the stimulation reaches higher rates (10/s or higher), it is known as steady-state VEPs. The responses induced by patterned stimuli are know as pattern VEPs or PVEPs, and the ones by un-patterned stimuli are known as flash VEPs or FVEPs. [1]
In the occipital cortex, above the scalp, electrical signals known as visual evoked responses (VER), are recorded when we apply a light stimulus. [3] VEPs measure the conduction of the visual pathways from the optic nerve, optic chiasma, and optic radiations to the occipital cortex. EP quantification is difficult because they are incorporated in a electroencephalographic activity (EEG) with a much larger amplitude (e.g., µV vs. tenths of µV). [2]
The light-evoked signal is small in amplitude and invisible within the normal EEG signal, which is augmented by repetitive stimulation and time-locked, signal-averaging techniques. These techniques separate the light-evoked signal from the readings of the background EEG.
Basis of VEPs are not yet understood. Any damage along the visual pathway may decrease the signal because VEPs show the integrity of the afferent visual pathway. [3]
The most used EPs are:
(1) Visual evoked potentials (VEPs; including both flash and checkerboard types).
(2) Short-latency somatosensory evoked potentials (SEPs). (3) Short-latency brainstem auditory evoked potentials (BAEPs).
(4) Late evoked responses, used for studying higher cortical functions (e.g., P300 in Alzheimer disease). [4]
As sciences evolves, the use of EP procedure has diminished in the clinical practise because more advanced technology is available at the moment (such as magnetic resonance imagining (MRI)).
The main difference between VEPs and MRI procedures is that the information obtained in MRI is more accurate in structural problems, and EP procedures obtain information about physiology of a certain anatomic pathway. [4]
Waveform:
A normal VEP response to a pattern-reversal stimulus is a positive peak that occurs at a mean latency of 100 ms. [4]
The peculiarities of the P100 waveform, if there are normal latency and amplitude values, does not clinically mean the presence of any abnormality, but it should be convenient more tests with hemi- or partial-field stimulation.
To determine the clinical significance of the responses, we should run additional test. It is possible that none of the peaks are the P100 (e.g. central scotoma, displayed in Fig.1.). [1]
Fig. 1. PVEP to half-field stimulation. (Guideline 9B: Guidelines on Visual Evoked
Potentials: Recommended Standards for Visual Evoked Potentials. 2008, American Clinical Neurophysiology Society).
„The central half-field components N75, P100, and N145 are most consistently recorded over the lateral occipital lead ipsilateral to the half-field stimulated. The peripheral half-field components P75, N105, and P135 are most consistently recorded over the lateral temporal lead contralateral to the half-field stimulated. If the central half-field P100 peak is lost (for example, in association with a central scotoma), the peripheral half-field P75 and P135 may be apparent in all occipital leads. This may then be mistakenly identified as the P100. Passband, 1-250 Hz; rate, 1.88/s; field size, 19.8; check size, 50‟:400 stimuli per average.”
The shape of the stimulation resembles a “W”. Reducing the stimulation from 2 Hz to 1 Hz, this W will transform into the typical P100. Also, we can obtain a W or a regular P100 response if we change size and rate parameters. There are similarities between VEPs produced by flash stimulation and those induced by large checks VEP. [4]
VEP waveforms depend on age, sex, visual acuity, pupillary size, etc.
The peak time is the time that a stimulus needs to reach the onset to the maximum positive or negative deflection of the VEP. Latency indicates the time from stimulus onset to the largest amplitude of a positive or negative deflection. [1]
Due to its usefulness, latency and amplitude of the peak are the components more frequently analyzed. [3] Clinical history and MRI are needed to reveal the damaged area, therefore, we can have more benefits from VEPs to test optic nerve function and less for assessing post-chiasmatic disorders. [4]
Basic technology:
- Electrodes: sintered silver–silver chloride, standard silver–silver chloride, or gold disc electrodes, impedances should be less than 5 kΩ, and to decrease electrical interference the frequency should be between 10 and 100 Hz. [2]
- Electrode placement: placed into bony landmarks (e.g. Fig. 2.).
- Standard pattern stimulus: high contrast black and white checkerboard that is placed between 50 and 150 cm, and can be adjusted. [2]
- Field and check size: patterned stimuli are defined by a visual angle extending under the side of a single check in degrees (º) or minutes of arc (min). One degree equals 60 min of arc. [2]
- Luminance and contrast: mean luminance is 50 cd m2 (40–60 cd m2) and contrast
Fig. 2. Electrode locations. ISCEV standard for clinical visual evoked potentials (2009
update).
„a. Location of active and reference electrodes for standard responses. The active electrode is located along the midline at Oz. The reference electrode is located at location Fz. The subscript z indicates a midline position. b. The locations of the lateral active electrodes, O1, O2, PO7, and PO8 are indicated along with the midline active electrode location, Oz
(J. Vernon Odom; Michael Bach; Mitchell Brigell; Graham E. Holder; Daphne L. McCulloch; Alma Patrizia Tormene Vaegan)”
Adopted from: [2]
Standard VEP stimulation: flash, and pattern.
- Flash VEP occurs close to a visual field up to 20º conferred in a vague illuminated room. The time-integrated luminance of this stimulus should be between 2.7–3.3 cd s m2.
The flash rate is 1 per s (1.0 Hz ± 10%). Flash VEP is useful when poor optics, poor cooperation or poor vision makes the use of pattern stimulation inappropriate. [2]
- Pattern-reversal VEPs: easily obtained by checkerboard the stimuli with large 1º (60 min of arc) and small 0.25º (15 min). They are less variable in waveform than the VEPs obtained by other stimuli, therefore, are the most used. [2]
- Pattern onset/offset VEPs are temporal patterns that prevent pattern onset responses being contaminated by pattern offset responses. Checkerboard pattern is rapidly transferred with a diffuse gray background. The pattern onset duration is 200 ms separated by 400 ms of diffuse background. The aspect of the stimulus should be expressed by the data acquisition system and at least two pattern element sizes are used: checks of 1º and 0.25º per side. [2]
Recording parameters:
- Amplification and filtering: Amplification by 20,000–50,000 times. Input impedance of preamplifiers is at least 100 MΩ. Analogue filter obliquely do not exceed 12 dB per octave in low frequencies and 24 dB per octave in high frequencies. [2]
- Analysis time: The duration of adult transient flash and pattern-reversal VEPs is 250 ms sweep. Infant VEP it is characterized for having longer peak latencies and sweep time, which is required to make a decent response. So the sweep duration is prolonged to 500 ms to make possible to analyze the responses obtained by on/offset stimuli. [2]
- Averaging and signal analysis: Is up to ratio VEP and EEG amplitudes how many number of sweeps are required per average. At least two averages are performed to make sure each VEP can be reproduced. In infants and young children, we use a fewer number of sweeps per average because the response obtained from them is usually more clear. [2]
Problems from averaging:
Sweep averaging is useful to obtain the VEP signals. A number of stimuli could length from some tens to some hundreds which will depend on VEP-to-EEG amplitude ratio. [5]
Sweep averaging assumes: EEG remains fixed during the recording, but results show that there is not a correlation with that, and ERPs do not change within registered sweeps, suggesting that there is not VEP changes during repetition of the stimuli. [5]
Neuronal/physiological responses, after various repetitions of the same stimulation pattern, tend to change. These changes can be systematic or unsystematic. In neurological
pathologies the unsystematic single-trial ERP changes could be observed (amplitude and latency shifts or jitters); when recordings that contain ERPs are varying in latency we are facing a problem, but processing each individual sweep and finding the suitable shift in time of generalized basis functions could be the solution. [5]
in every sVEP the time parameters have a specific importance because every single-sweep analysis technique it can be used to study neural responses associated with cognitive responses, i.e. in pediatric patients VEPs recording duration can be as long as 45 min or more, which explains that taking extra time can cause impaired attention and non-steady target fixation which will affect the appearance of recorded VEPs. False negative records are obtained when there is a poor target fixation, in multifocal VEPs. [5]
Sweeps:
Before starting the VEP test, different parameters of sVEP are set by the examiner: type (linear vs logarithmic sweep), range, direction (downward/upward sweeping), screen luminance, and temporal frequency. [6]
Pattern VEP needs extended recording time, but the sweep visual evoked potential (sVEP) assesses visual acuity and contrast sensitivity in a reduced recording time. SVEP can measure resolution acuity and hyperacuity. To determine the lowest contrast to which the visual system responds the contrast is swept with a fixed spatial frequency. [6]
Other parameters such as continuity or sampled/step-wise and the electrode placement. During the recording of a continuous sweep the contrast or spatial frequency of an acuity is changed continuously. Sampled sweep consists of presenting for a period of time a number of contrast or spatial frequency acuities all along the recording. [6]
Table. 1. Studies of visual acuity development using sVEP.
Table. 2. Studies of contrast sensitivity development using sVEP.
Adopted from [6]
Preliminary signal processing:
Quantitative evaluation of visual evoked potential parameters (latency and amplitude of the principal peaks and valleys) provides useful information for studying brain processes and is important for clinical diagnostics as for many aspects of cognitive neurophysiology. The P300 component can be detected in an ERP waveform if the stimulus is task-relevant and/or if it is uncommon, after the stimulus shows a positive peak at about 300ms. Its latency measures the stimulus classification speed. [7]
Conventional (coherent) averaging (CA) consists of certain number of sweeps time-locked to a number of exact stimuli to the subject being averaged. Counting on the class of ERP and on the single-sweep signal-to-noise ratio (SNR), this number is up to some tens to
some thousands. CA is very simple to implement and is used in research and clinical practice, on-line applications, e.g., intra-operative monitoring and brain computer interface. [7] [19]
Disadvantages of CA:
(i) it assumes that the EEG noise is stationary during the recording of the N sweeps (but it is false).
(ii) it does not exploit any information a priori available on ERP and EEG.
(iii) it assumes that the ERP does not change within the N sweeps (adaptation phenomena may occur).
By exploiting a non-parametric Bayesian approach it is possible to create a procedure to extract single trial ERPs from ongoing activity. The method consists of a two-stage technique procedure, as C. D’Avanzo describes in her article: [7]
“- Stage 1. Estimation of an average ERP: Adopted from [7]
yi = ui + vi (1) The a priori covariance matrix of vi is computed as: 𝛴vi = 𝜎i2(ATiAi)−1 (2) The covariance matrix of ui is computed as:
𝛴ui = 𝜆2i(FTF)−1 (3)
According to linear minimum mean square estimation, the optimally filtered sweep is:
ûi = (ATiAi + 𝛾iFTF)−1ATiAiyi (4)
Once the filtered sweeps have been obtained, the estimate of the mean ERP is computed as weighted averaging:
𝜇 = 𝛴Ni = 1wi ûi / 𝛴Ni = 1wi (5)
where each weight wi is inversely proportional to the expected value of the squared
norm of the filter error, given by the trace of the covariance matrix of the estimation error ūi:
cov(ūi) = 𝜎i2(ATiAi +𝛾iFTF)−1 (6) - Stage 2. Single trial estimation: Adopted from [7]
In the second stage, the “average” 𝜇 profile , obtained by Eq. (5), is exploited as if it were an a priori available information on the expected ERP. By using this information, the estimate ŭi of the ith single-trial ERP is given by the following equation:
ŭi = 𝜇 + (ATiAi + 𝛏iLTL)−1 ATiAi (yi - 𝜇)” (7)
PCA - Principal Component Analysis:
PCA it is known as the technique which use practical underlying mathematical principles to transform a number of possibly corresponding variables into a smaller number of variables known as principal components.
PCA most common use is trying to analyze large data sets. Some of the other common applications include; de-noising signals, blind source separation, and data compression.
Some PCA usage/applications: - Interest Rate Derivatives Portfolios. - Neuroscience.
- Data compression. - Image processing.
- Visualization, exploratory data analysis, pattern recognition, and time series prediction.
PCA summarizes the variation in correlated multivariate attributes to a set of non-correlated components. The objective of PCA is to reduce dimensionality extracting the least number of components and multivariate analysis with less loss information. [8]
Clinical significance and advantages of VEPs:
Normal VEP is obtained when there are no lesions all along the visual system, but it should be noted that gaps anywhere throughout the visual system can produce abnormal VEPs. [9]
VEPs are useful in:
- Testing visual sensory function when clinical examination is not reliable.
- Investigates subjective symptoms and detecting if they are from an organic origin. - Evaluates properly th causative mechanism of neurologic deficits and functional recovery.
- Monitoring cerebral functions.
- Allows to quantify and objectively follow up a known lesion.
- Assess the functional integrity of the visual pathway whereas imaging techniques such as MRI evaluate mostly their anatomical and structural basis.
monitoring the functionality of the visual pathways in situations that EEG is useless.
Pattern-reversal VEPs it was first used in the diagnosis of patients with optic neuritis and in the assessment of cases having suspected multiple sclerosis (MS). Also, pattern-induced VEPs are much more sensitive to optic nerve lesions than flash VEPs.
Demyelinated plaques can be recognized in the absence of clinical symptoms as measured by perimetry, visual acuity, or color tests. [9]
With an abnormal VEP, the differential diagnostic considerations include the following: [10]
Table. 3. Visual-Evoked Potential Abnormalities. Adopted from [10]
Optic neuropathy Optic disc drusen Vitamin B12 deficiency
Cortical blindness
Optic neuritis Papilledema Congenital nystagmus Occipital lobe lesion Ocular hypertension Diabetes Parkinson’s disease Aluminum neurotoxicity Optic nerve hypoplasia
Toxic amblyopia Migraine Manganese
intoxication Glaucoma Leber hereditary
optic neuropathy
Down syndrome Retrobulbar neuritis Ischemic optic neuropathy MS Dominant optic atrophy Optic nerve gliomas Meningiomas Craniopharyngio mas
Giant aneurysms Pituitary tumors
Huntington’s chorea Friedreich’s ataxia Hereditary spastic ataxia Charcot-Marie-Tooth
VEP in clinical ophthalmology and neuro-ophthalmology abnormalities:
VEP has the ability to separate among retinal, optic nerve, and cortical diseases. Damage anywhere along the visual pathways results in waveform abnormalities. Unilateral abnormalities could indicate optic neuropathy and could help to diagnose lesions in the absence of definite fundal abnormalities. [11]
Demyelination results in increased latency of the P100 waveform, without significant effect on amplitude; ischemic, compressive, and toxic damage reduce amplitude, with less effect on latency. [2]
Unfortunately, VEP has little usefulness. Factors such as uncorrected refractive error, media opacity, amblyopia, fatigue… may contribute to an abnormal waveform when there is no visual pathway damage. [12]
There are 2 situations iwhich are of clinically usefulness:
(1) evaluation of the visual pathway in infants or inarticulate adults.
(2) confirmation of intact visual pathways in patients suspected of non-organic disease. [2]
What to expect: [12]
- A consistently abnormal flash response in the infant/inarticulate adult shows gross deterioration.
- An abnormal pattern response, it may indicate damage or may be a false-negative. - Multiple sclerosis: Initially one nerve yields normal range EP. [12]
- Tumors: children with neurofibromatosis type 1 (NF1) are vulnerable to develop optic nerve gliomas. [12]
- Trauma: It is usual that compression of optic pathways immediately after severe trauma results in no recordable VEPs. VEPs may be recordable days later when inflammation subsides. [12]
Table. 4. Optic nerve and visual pathway disorders. Adopted from [11]
Table. 5. Neurologic disorders. Adopted from [11]
N = Normal; A = Abnormal; ? = information not available; + / — = mild increase / mild decrease; ++ / — — = moderate increase / moderate decrease; +++ / — — — = severe increase / severe decrease.
Disorder Amplitude Latency Morphology Optic neuritis A / N +++ N Ischemic optic neuropathy — — ++ A Toxic amblyopia — N A Dominant optic atrophy — N / + ? Leber’s optic atrophy — — + A Optic nerve hypoplasia — — + A Glaucoma N / — N / — N
Optic disc drusen — + A
Papilledema N / — N / + N / A
Tumors (anterior pathway)
— + A
Disorder Amplitude Latency Morphology Multiple sclerosis N / — +++ N / A Vitamin B12 def. N + N Cong. nystagmus — + N / A Parkinson N / — N / ++ N Migraine N + N / A Down syndrome — N ? Cortical blindness N / — N N
Occiptal lobe lesion — + A
Huntington’s — N A
Table. 6. The clinical applications of the different electrofunctional tests. Adopted from
[13]
Examination Generators Indications Flash ERG
External retina
(Pigmented epithelium, Photoreceptors, Bipolar and Amacrine cells) Retinitis pigmentosa; Detachment; Thrombosis CRV; Occlusion CRA; Diabetes; AIDS; Hypertension; Cone dystrophy; Albinism
Pattern ERG Internal retina
(ganglion cells and fibers)
Glaucoma; Diabetes; Multiple Sclerosis;
Disthyroidisms; Connective tissue diseases; Parkinson’s disease; Toxic, traumatic, compressive, inflammatory diseases of the optic nerve.
Focal ERG Macula Congenital maculopathies (Strargadt, Best); Cystoid macular oedema; Central serous chororetinopathy; AMD; Macular dystrophy.
VEP Visual pathway Congenital maculopathies;
Optic neuritis; Trauma; Amblyopia; Neoplastic compressions; Degenerative and vascular diseases.
Examples of VEP recordings: (1) Optic nerve demyelination:
Fig. 3. 37-year-old female complained of blurred vision in her left eye following exercise or a hot bath.
Electrophysiological assessment of optic nerve disease.
„Other than questionable pallor of the left optic disc, examination was unremarkable. Visual acuities were 6/5 bilaterally. The symptomatic left eye shows VEP evidence of marked optic nerve conduction delay; here is also reduction in the left eye PERG N95 component with shortening of P50 component latency. Note the marked subclinical delay in right optic nerve conduction, presumably explaining the lack of a relative afferent pupillary defect in relation to the left eye.
(G E HolderMoorfields Eye Hospital, London, UK)”
Fig. 4. Delayed VEPs 7 months following left optic neuritis in a 39-year-old male.
Electrophysiological assessment of optic nerve disease.
„Note that the delay is present in both pattern and flash VEPs, and that there is selective reduction in the N95 component from the affected eye, in keeping with retrograde degeneration to the retinal ganglion cells. Right eye findings show no significant abnormality. Visual acuities were 6/6 right; 6/12 left.
(G E HolderMoorfields Eye Hospital, London, UK)”
(2) Optic nerve compression:
Fig. 5. VEP and PERG findings from three patients with dominantly inherited optic atrophy. Electrophysiological assessment of optic nerve disease.
„Patient A (VA 6/18) has a markedly abnormal PERG, with clear N95 component reduction and shortening of P50 component latency, but a VEP that falls within the normal range. Patient B (VA 6/9) shows clear VEP delay, despite the better visual acuity, and has PERG abnormality confined to N95. Patient C (VA HM), with end-stage disease, had no detectable PVEP. PERG shows loss of N95 with shortening of P50 latency and additional P50 amplitude abnormality (<2 𝜇V).
(G E HolderMoorfields Eye Hospital, London, UK)”
(3) Albinism:
Fig. 6. Pattern appearance (150 ms onset) and flash VEPs in a 7.5-year-old girl with the intracranial misrouting of albinism.
Electrophysiological assessment of optic nerve disease.
„Note the pronounced contralateral predominance of the VEPs to both pattern and flash stimuli (arrows). Pattern appearance VEPs only show the positive component at 100 ms in the contralateral hemisphere traces. The flash VEPs show little activity in the first 100 ms in the ipsilateral hemisphere traces, but a clear FVEP in the contralateral traces.
(G E HolderMoorfields Eye Hospital, London, UK)”
Adopted from [14]
Toxic or nutritional optic nerve dysfunction involves amplitude reduction and latency delay, and there may be associated involvement of the N95 component of the PERG. To mention fe examples: ethambutol toxicity and tobacco–alcohol-related optic neuropathy. [14]
RESEARCH METHODOLOGY AND METHODS
The aim of this work was to reveal most informative quantitative estimates of single-sweep VEP shape reflecting optical nerve function related pathologies. Our systematic review of publications revealed serious problems, which face all researchers working in this field – lack of good signal quality recordings and absence of any “Golden Standard” method to provide reference information on analyzed signal properties. So we decided to use the published information on specific VEP signal properties related to optical nerve function related pathologies and simulate test signals for evaluation of specific analysis methods. Our experimental work was consisting of following steps:
1) Preparation of simulated signal sets: Real signal traces registered during VEP
analysis contain background EEG and VEP “buried” in it because of comparatively weak VEP energy. So, extraction of VEP component from real registered signal is an important step in whole analysis and we decided to construct simulated ensemble of signals as combination of background EEG and pure VEP, mixing them at various ratios. Background EEG signal was registered from the healthy volunteer with the standard VEP registration equipment during the standard protocol, but with switched off monitor, i.e. showing no visual stimulus to the patient. The example of such signal ensemble is presented on the left of Fig. 7.
Fig. 7. Example of background EEG recording (Left): EEG - traces of signals were recorded during the standard procedure, but showing no visual stimulus to the patient. (Right): VEP - Example of typical pure VEP used for simulation.
Test VEP used for simulation was obtained averaging 100 signal traces recorded from healthy volunteer during standard recording protocol. Example of such signal is presented on the right side graph in Fig. 7.
EPs registered from patients with certain optical nerve function related pathologies have noticeable reduction in amplitude and/or delay in appearance of typical peak P100, or even prolongation of duration of the whole VEP. The delay in time of P100 peak we modeled shifting back and forth in time pure VEP before adding it to background EEG.
Example of such pure signals is presented on the left side graph of Fig. 8. Prolongation of whole VEP we modeled by stretching pure VEP signal by different scaling factor. Reduction of amplitude of the VEP we modeled by changing amplitude scaling of pure VEP before adding it to background EEG. Example of such pure signals is presented on right side graph of Fig. 8.
Fig. 8. Modeling of shift in time of peak P100 of the VEP (Left) and modeling of the prolongation of VEP duration and reduction of amplitude (Right).
Fig. 9. Examples of simulated VEP containing traces of test signals with added background EEG: Modeled VEP shifts in time (Left), modeled prolongation of duration and reduction in amplitude (Right).
2) Selection of signal analysis method:
Analysis of VEP containing signals should be performed in two steps.
First step is extraction of VEP trace out of the mixture with background EEG.
The most promising results give advanced signal processing methods exploiting a
priori information about VEP. One of such methods was developed in department of Biophysics and Bioinformatics of Neuroscience Institute of LUHS. It is based on optimal
representation of every single trace by means of Karhunen-Loève transform using matrix X. Same method as described in the article of A. Kybartaite-Ziliene, et al.: “A method for
reconstruction of visually evoked potentials from limited amount of sweeps (2015)”. [5]
Second step of analysis contains evaluation of VEP signal shape. As mentioned
before, in aim to evaluate VEP shape changes, related to optical nerve malfunction, we need to estimate time and frequency characteristics of the signal. The VEP signal is appearing only once in whole trace, i.e. it is so called “energy” signal, which has finite integral in range from -∞ till +-∞. (nota bene: The alternative could be so called “power” signals, which have periodic structure and infinite integral in range from -∞ till +∞. We repeat stimulus and get repeated responses, but each stimulus, evokes only one VEP. More details in [15]). Usually used Fourier transform based methods for frequency characteristic’s determination could be used to analyze “power” signals, however they fail in case of “energy” signals. So we proposed to use Wavelet transform, which similarly to Fourier transform, uses inner products to measure the similarity between a signal and an analyzing function. In Wavelet transform, the analyzing function is a wavelet, ψ. Unlikely Fourier transform, where only harmonic functions sin and
cos used, here are numerous families of standard wavelets, corresponding to various types
of the signals analyzed. The method compares analyzed signal to shifted and compressed or stretched versions of a wavelet. Stretching or compressing a function is collectively referred to as dilation or scaling and corresponds to the physical notion of scale. By comparing the signal to the wavelet at various scales and positions, you obtain a function of two variables. For a scale parameter, a>0, and position, b, the Wavelet transform is:
) (1)
So we are getting the 2-D redundant representation of a 1-D signal as a 2-D array, the maximal values of which indicate best fit of the signal with used wavelet ψ when appropriate scaling (a) and translation (b) factors used. The factors are indicated as positions of these maximal values in the array. The best results we got using “Morlet” wavelet family.
YW(c, τ) = 1 c ∫ +∞ −∞ y(t) ⋅ Ψ ( t − τ c )dt
Scaling factor was indicating central frequency of the scaled wavelet best fitting to the analyzed signal. For this frequency we used as duration parameter, reflecting shape of the VEP. The principle of central frequency determination is shown on the left of Fig. 10. Example of wavelet transform of the pure VEP signal used for simulation (presented on the right of Fig. 7.) is presented on the right of Fig. 10.
Fig. 10. Central frequency determination of scaled and translated wavelet (black trace) best fitting analyzed VEP (blue trace) (Left). Wavelet transform of typical pure VEP signal: Maximal values indicate scaling factor 3.868, what corresponds to central frequency of 15 Hz, and translation factor 0.6957 indicates a wavelet position, shifted by 0.6957 s. in time.
RESULTS
The results were obtained analyzing signals from 50 clinical recordings performed in
“Akiu Klinika" of Lithuanian University of Health Sciences. Population of the sample was 8
subjects and it consisted of six females from 7 to 49 years old, and two males of 12 and 17 years old. Sweeps were taken from every individual to calculate the basis functions for Karhunen-Loève transform based pre-processing of the signals. The patients were placed in a conformable manner while sitting, looking with eyes opened at a fixed point in the screen. Any of them were previously diagnosed with an ophthalmological or near degenerative problem. Example of signal pre-processing is given in Fig. 11. Typical raw registered sweeps containing VEPs are on the left graph. Right graph presents result of Karhunen-Loève transform based preprocessing – filtered ensemble of sweeps with clearly visible VEPs.
Fig. 11. Typical raw registered sweeps containing VEPs (Left). Filtered ensemble of sweeps with clearly visible VEPs. (Time scale is in sample numbers. Real time one can get by multiplying number by 0.5871).
Results of tests of Wavelet transform based VEP signal shape evaluation method using simulated prolongation of duration and reduction in VEP amplitude containing traces (see right graphs of Fig. 8. and Fig. 10) are presented on fig. 12. Two graphs represent results of original duration/amplitude VEP (left) and maximally prolonged and reduced amplitude VEP (right). Two wavelet transform parameters of these signals – translation time and central frequency (calculated from scaling factor) were respectively 0.09451s; 14,601Hz and 0.13501s; 7.8251Hz.
Fig. 12. Results of tests of Wavelet transform based VEP signal shape evaluation method.
Left graph: Wavelet transform of original duration/amplitude VEP. Right graph: maximally prolonged and reduced amplitude VEP.
(To get real translation time 0.60119s should be subtracted from values presented on the graphs due to calculation method reasons).
We modeled linear increase of duration of analyzed VEP, so Wavelet transform parameters here should follow the same tendency. Graphs of translation times and central frequencies are presented on Fig. 13.
Both correlation coefficients were statistically significant and equal to 0.9703 (p<0.001) and 0.9774 (p<0.001).
Another set of simulated VEP containing traces was with sinusoidal shift in time of the VEP (see left graphs on Fig. 8. and Fig. 9.). In this case central frequency remained constant and translation time followed sinusoidal changes.
Fig. 13. Changes of estimated Wavelet transform Central Frequency and Translation time in VEPs with modeled linearly increasing prolongation (left graph) and delay (P100) in time (middle graph) and translation time in VEPs with modeled sinusoidal shift in time (right graph).
Fig. 14. shows dependency of Central Frequency and Translation time correlation coefficients from ratio of VEP amplitude and added background EEG amplitude shown in logarithmic scale in [dB]. This ratio was calculated as following:
! (2)
Here AEEG and AVEP are amplitudes of background EEG and VEP. For example, ratio
of -20 dB means that background EEG amplitude is 10 times weaker then VEP. Right side graph in Fig. 14. shows translation time values at various VEP to background EEG ratios.
We see, that clear linear tendency at -50 dB ratio, step by step disappears when ratio level increases till -8 dB. Quantitative estimate of this phenomena it is seen on the left graph from Fig. 14., where correlation coefficients stepwise decrease from nearly 1 till 0.4. Further increase of background EEG made correlation coefficients statistically insignificant. We observe the same situation with changes of R2 (estimate of goodness of fit with linear
regression) of Translation time.
A(d B) = 20 * log10AEEGA
Fig. 14. Investigation of sensitivity of the method to value of background noise: Central frequency (RCF) and translation time (RTtr) correlation coefficients together with coefficients of determination (R_2) as dependency on ratio of VEP amplitude and added background EEG amplitude (Left graph). Translation time values at various VEP to background EEG ratios (Right graph).
DISCUSSION OF THE RESULTS
Wavelet transform-based method for the evaluation of VEP signals shape, gives us time and frequency estimates of the signal. Those estimates, reflect expected changes of the signals in case of optic nerve pathology, giving a valuable diagnostic information. Investigated method’s behavior in case of added background noise (background EEG) revealed essential need of signal preprocessing.
The method seems to work properly when background EEG and VEP ratio is more than -12 dB, but in real signals this ratio could be opposite – background EEG could be much stronger than VEP, therefore, only Karhunen-Loève transform-based preprocessing could aid to get suitable signals for further analysis.
The truncated expansion of post-stimulus EEG recording intervals reveals VEPs and enables additional quantitative evaluation of their shape and amplitude on beat-to-beat basis making possible to evaluate the dynamics of VEPs for diagnostics.
It is important to note that the usage of generalized basis functions could be useful for comparison of data from several different recordings.
The Karhunen-Loève transform-based truncated representation of the ERP signals is considered as a powerful tool for separation of visual evoked potentials and background EEG. Proposed method is able to cope with known problem of underestimation of the peak amplitude in ERP jittering cases.
In future, analysis of the single sweeps may potentially reveal more information about event-related brain dynamics, which arises from complex interactions between subject state, unpredictable changes within and experimental events, than simple response of averaging.
CONCLUSIONS
- Theoretical analysis and systematic review of publications showed that VEPs and other event related potentials carry important diagnostic information, so development of advanced methods for their recording and analysis is very important for clinical use.
- The study of trial-to-trial variations during recording sessions, supported by the use of new and powerful methods of signal analysis, and the study of single trial VEPs and their correlation to different behavioral processes seems one of the most interesting directions of future research.
- The reconstruction of VEP in every single sweep in the recordings with limited amount of sweeps is possible by means of multivariate analysis methods (e.g. Principal Component Analysis, Karhunen-Loève transform).
- We propose to use wavelet transform for evaluation of specific changes in VEPs related with optic nerve malfunction.
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