Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT05123469 |
| Other study ID # |
2017110124 |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
January 2022 |
| Est. completion date |
March 2023 |
Study information
| Verified date |
November 2021 |
| Source |
University of Texas at Austin |
| Contact |
Zoltan Nadasdy, Ph.D. |
| Phone |
323-697-6714 |
| Email |
zoltan[@]utexas.edu |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
The purpose of this study is to compare the standard clinical electroencephalography (EEG)
device with a new portable wireless EEG device, further referred to as zEEG, made by ZETO®.
zEEG was designed to make EEG studies simpler, safer, more comfortable, faster, and less
obstructive for the patient, also easier to set up for technicians. Wireless and battery
powered, it uses the latest mobile technology. Contrary to the clinical EEG, this headset
does not use any glue between the skin and the electrodes. Minor skin irritation may still
occur but much less likely than from the collodion glue used in the clinical electrodes. In
addition, the zEEG system does not need any gel to be applied to the skin. The zEEG
electrodes are dry and disposable. They have never been used on any other head before. No
additional risk is involved with setting it up. In addition to the clinically necessary EEG
electrodes or intracranial electrodes for long term monitoring, we will place zEEG on the
head to compare the sensitivity of the new device to the traditional device. zEEG is proven
to meet the standard of clinical system and received an FDA clearance in 2018. If further
clinical tests validate its technical parameters and comfort, it may replace traditional
clinical EEG systems.
Description:
Research Hypothesis:
The hypothesis is that signals recorded by zEEG do not differ significantly from signals
recorded by a standard clinical EEG system (clinical EEG). In this study we will use the
Xltek® (Natus, Inc.) clinical-grade FDA-approved long-term video monitoring EEG system and
the Nicolet ® (Natus, Inc.) clinical-grade FDA approved ambulatory EEG system. zEEG will be
compared against both systems. Minor difference in signal is expected due to the
non-overlapping positions of the 6 electrodes. The 1-2 cm position difference between the
electrodes is expected to generate discernible difference between the two signals.
Background:
The purpose of the study is to validate the zEEG® by ZETO, Inc. (zEEG), a wearable wireless
headset and data acquisition (DAQ) system on both pediatric and adult patient populations
diagnosed with epilepsy. The study compares (i) the technical parameters of the two systems,
(ii) the sensitivity and specificity of the zEEG against standard clinical EEG DAQ systems
used for detecting abnormal epileptiform discharges, (iii) the correlation with intracranial
recordings, (iv) and the discriminability of the two systems by trained neurologists in a
double-blind paradigm. Since zEEG device has received a 510(k) clearance from FDA in 2018,
the study does not concern with the safety of the device. The device poses no significant
risk, as evaluated by the FDA, and qualifies as Class II category. The goal of the study is
to evaluate the utility of a dry, easy to use, wireless system in clinical electrography by a
quantitative assessment of the overall quality of the signal relative to a traditional
clinical EEG system.
Design and methodology:
Spec Aim 1: Quantify and compare the signal quality between the zEEG and clinical EEG system
in simultaneous recording sessions performed for diagnostic purpose in patients under
clinical setting, one session per patient. Specifically, we will be testing signal overlap
(amplitude, sampling frequency, alpha waves, sharp waves, focal slowing, spike and wave
patterns, and sleep spindles) and spectral correlation between the two systems. We will use a
multi-factorial ANOVA design, where the independent variables are arranged into 4 factors:
age-group (pediatric, adult), system (zEEG, cEEG), task (1-5), and recording sites (10-20
system). The dependent variable is the spectral signal to noise ratio (SNR) computed as the
spectral mean (divided by the spectral standard deviation.
Spec Aim 2: Determine the sensitivity and specificity of the two systems for epileptiform
activity in simultaneous recordings. Although Spec Aim 1 will provide us an estimate of the
sensitivity of zEEG relative to the clinical EEG system, it does not provide an estimate on
the specificity of the parameters for electrographic events that are relevant for diagnosis.
For example, if the zEEG with its high-impedance dry electrodes turns out to be a lot more
sensitive to electric potential changes than the standard clinical device, that might
increase its sensitivity for movement artifacts too, which would contaminate neuronal
signals. Sensitivity without specificity does not mean improvement. Therefore, we will test
whether the difference in sensitivity is associated with a difference in specificity as well.
A dry electrode-based system will surpass the standard wet electrode clinical systems if it
provides at least the same sensitivity and specificity for the representation of clinically
relevant EEG events. Therefore, we will compare the separation between myogenic artifacts,
teeth clenching, head movements, eye movements, and EEG between the two systems, zEEG and the
clinical EEG system. In this Specific Aim, we will only compare Xltek® (Natus, Inc.) a
clinical-grade FDA approved long-term monitoring EEG system with zEEG, as opposed to using
two different clinical EEG systems (Nicolet® and Xltek) to ensure the homogeneity of samples
(Appendix, Figure 2A). Most importantly, we will compare the separation between baseline and
epileptiform activity. Instances of epileptiform activity will be identified by trained
neurologists among our key co-investigators. We will compare the magnitude of amplitude- and
power spectra-change from baseline EEG to movement artifacts and epileptiform activity
between the two systems. Spectral density statistics will be computed between 1 and 180 Hz.
The recordings for Spec Aim 1 and Spec Aim 2 will be performed on different subjects using
different clinical amplifiers as comparator systems: Nicolet® designed for ambulatory data
acquisition, and an Xltek® designed for long term monitoring. Each patient will participate
in the study only once.
Spec Aim 3: Testing the concordance of clinical evaluations made by 16 expert neurologists
based on EEG records obtained by the two systems. We will compare the clinical conclusions
made about relevant features of epileptiform activity captured in simultaneous zEEG and
clinical EEG samples.
The purpose of this aim is to evaluate the clinical validity of the records made by using
zEEG relative to the records made by using standard clinical EEG systems. We are testing
whether the information conveyed by samples of the two different systems are concordant when
evaluated by trained neurologists. This aim compares how clinically informative the two
systems are, regardless of their technical parameters. Ultimately, the new EEG system is
informative only if the clinical conclusions drawn from the two types of samples are
concordant. Note, that this Aim does not concern which system is better. Instead, it
addresses whether the zEEG is good enough to replace the clinical EEG system, given its
advantage of ease of use, set up time, wearability and comfort. Nor does the Aim assume that
the two systems are indistinguishable. Expert neurologists will not compare the two systems
directly to each other. Instead, they will provide clinical assessments based on the samples.
Those samples will be pre-selected by other expert neurologists (the investigators of this
study). There will be no overlap between CO-Investigators and expert subject neurologists.
The clinical assessments provided by the subject neurologists will be blindly scored by a
computer program, and the PI and the author of the study will evaluate the concordance of
those scores. Based on the concordance, the investigators conclude whether the two EEG
systems convey the same clinical information or not. This procedure ensures the double-blind
paradigm and minimizes biases.
Data Analysis:
Spec Aim 1: For the statistical evaluation we segment the data into equal blocks of 256
points (512 ms) and perform FFT (Fast Fourier Transform). We will use the sliding-window
multi-taper spectral decomposition to avoid edge effects between the blocks. We will perform
these analyses in each channel separately. Using multiple blocks, we will be able to compute
statistics on 351 samples from 3 m recording. The spectral difference will be estimated with
a confidence of 0.05 at different frequency bands and using Bonferrioni correction for the
multiple comparisons.
Finally, we will compute the correlation coefficient between the zEEG and clinical EEG
spectra under all 5 conditions
Spec Aim 2: Our neurologists will identify, and clip prerecorded simultaneous zEEG and cEEG
samples during normal resting state, during movements, and during electrographic events based
on video replay and EEG. We will select 60 s intervals during each type of events, as many as
we can obtain during a 1 hr EEG session/subject. We apply the same filter setting and sample
rate (500 Hz) for both systems. Next we compute sample correlation between the zEEG and cEEG
within 1.25 s sliding windows. We compare power spectral density change within the same
system between resting state EEG, movement artifacts and electrographic events (inter-ictal
spikes, seizures, spikes, spike and wave, other epileptic discharges). We quantify the
spectral difference between these events by computing a discriminability index using ROC
analysis. ROC analysis tests how an 'ideal observer' would be able to discriminate between
those samples based on the spectral differences relative to the grand truth (based on the
classification by our expert neurologists) without knowing what period those samples were
extracted from.
For statistical an analysis, a four-way ANOVA will be used, where the 4 factors are: age
group (pediatric, adult), system (zEEG, cEEG), tasks (1-5), and recording sites (10-20
system). The dependent variable is the spectral signal to noise ratio (SNR) computed as the
spectral mean (divided by the spectral standard deviation.
Spec Aim 3: The maximum achievable correct score per subject is 7 for the first test and 7
for the second. Next, we combine the scores from the sessions within the same system as
follows. If the scores from the first set are denoted as clinical EEG [a1 a2 a3 a4 a5 a6] and
zEEG [b1 b2 b3 b4 b5 b6] and in the second set as clinical EEG [a7 a8 a9 a10 a11 a12] and
zEEG [b7 b8 b9 b10 b11 b12], then we compute the difference scores as DclinicalEEG-zEEG (a1-
b1, a2-b2, … ,a12-b12). Our NULL is that the evaluations by the two experts will be
concordant between the two systems and the D scores will not be different from zero. The
alternative is that the expert evaluations of the two systems are discordant; hence the
distribution of D scores is significantly different from zero. We will calculate a one-sample
student T-test to compare the deviation of distribution from zero.
We will ask 16 experts to provide their responses on testing the samples. We will make the
test to be performed on-line. The EEG samples will be de-identified. Gain, filtering and
sampling rate will be identical for all samples and between both systems. No more than 1 min
is needed per item. The total test with the 5 questions, about 10 samples (clinical EEG set
and zEEG set) will take about 10 minutes to take. According to our power test, a sample
size=16, STD=3 (in the range of 1 to 7) sample average D=2 the power of statistical test is
84.7%, sufficient for statistical conclusion to be drawn. Hence, the sample size = 16
suffices the desired statistical power of >80% (http://rpsychologist.com/d3/NHST/). By
convention, 80% is an acceptable level of power (Ellis, 2010).
