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A new era in electroencephalographic monitoring? Subscalp devices for ultra–long‐term recordings



Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital‐ and home‐based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud‐based software. This new generation of ultra–long‐term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra–long‐term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.
Epilepsia. 2020;61:1805–1817.
Received: 16 April 2020
Revised: 16 June 2020
Accepted: 5 July 2020
DOI: 10.1111/epi.16630
A new era in electroencephalographic monitoring? Subscalp
devices for ultra–long-term recordings
Mark P.Richardson1
John M.Heasman7
Ivan C.Zibrandtsen10
Troels W.Kjaer10,11
1Department of Basic & Clinical Neuroscience, King’s College London, London, UK
2UNEEG medical, Lynge, Denmark
3Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Bern University Hospital, University of Bern, Bern,
4Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
5Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
6Epi-Minder, Melbourne, Victoria, Australia
7Cochlear, Sydney, New South Wales, Australia
8NYU Langone Comprehensive Epilepsy Center, New York, New York, USA
9Department of Neurology, Tampere University and Tampere University Hospital, Tampere, Finland
10Center of Neurophysiology, Department of Neurology, Zealand University Hospital, Roskilde, Denmark
11Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original
work is properly cited.
© 2020 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy
Jonas Duun-Henriksen, UNEEG Medical
A/S, Nymoellevej 6, 3540 Lynge, Denmark.
Funding information
NH&MRC; Region Southern Denmark;
Engineering and Physical Sciences
Research Council; South London and
Maudsley NHS Foundation Trust;
Innovationsfonden; Health Science
Foundations of Region Zealand; Epilepsy
Inaccurate subjective seizure counting poses treatment and diagnostic challenges and
thus suboptimal quality in epilepsy management. The limitations of existing hospital-
and home-based monitoring solutions are motivating the development of minimally
invasive, subscalp, implantable electroencephalography (EEG) systems with accom-
panying cloud-based software. This new generation of ultra–long-term brain moni-
toring systems is setting expectations for a sea change in the field of clinical epilepsy.
From definitive diagnoses and reliable seizure logs to treatment optimization and
presurgical seizure foci localization, the clinical need for continuous monitoring of
brain electrophysiological activity in epilepsy patients is evident. Thispaper presents
the converging solutions developed independently by researchers and organizations
working at the forefront of next generation EEG monitoring. The immediate value
of these devices is discussed as well as the potential drivers and hurdles to adoption.
Additionally, this paper discusses what the expected value of ultra–long-term EEG
data might be in the future with respect to alarms for especially focal seizures, seizure
forecasting, and treatment personalization.
The average accuracy of seizure diaries is <50%, and this
complicates diagnosis and management of epilepsy.1,2
Recent progress in the development of wearable electroen-
cephalography (EEG)3–6 and non-EEG seizure detection de-
vices was reviewed in a number of papers,3,7–10 all revealing
the unmet need for devices that could chronically monitor
epileptic brain activity. Implantable subscalp EEG devices
meet this need by detecting electrographic seizures, which
has been shown to be a robust objective measure that corre-
lates to clinical symptoms.11,12 In cardiology, the invention of
Holter electrocardiography (ECG) and the implantable loop
recorder provided a solution for the problem of monitoring
rare cardiac events.13,14 We anticipate a similar advance in
neurology with respect to long-term monitoring of brain ac-
tivity in epilepsy.
Currently, scalp EEG has several critical limitations
for long-term monitoring. Electrodes must be held fixed
to the skin either with a cap or an adhesive, such as collo-
dion, and the skin-electrode interface must be maintained
regularly to provide good recording quality. Despite in-
tense research on dry-electrode technology, the quality
attained so far is not sufficient to warrant broad appli-
cability. Moreover, scalp electrodes are generally accept-
able for periods of up to 1-2weeks at most, which might
be insufficient if seizures are infrequent, and surveys
show that esthetic appearance is an important variable
to determine patients' choice of a method for ambulatory
Two commercially available intracranial devices enable
chronic EEG monitoring. The RNS System (NeuroPace)
continuously records counts of epileptic events per hour bin
and provides neurostimulation. However, only snippets of
raw data can be extracted, amounting to several minutes per
24 hours of monitoring. Percept PC (Medtronic) provides
neurostimulation treatment for symptoms associated with
movement disorders and obsessive-compulsive disorder as
well as epilepsy, where BrainSense technology can also pro-
vide a limited form of EEG monitoring (manually triggered
30-second EEG storage and bandpass average power every
10minutes). However, these are invasive intracranial systems
with a clear emphasis on therapeutic neurostimulation rather
than diagnostics.
With this gap in mind, a handful of researchers and or-
ganizations have individually pioneered the development
of subscalp EEG recording devices, reaching converging
technical solutions in recent years, and are currently work-
ing on translating the invention to the clinic and market. One
subscalp device has recently been launched in Europe (24/7
EEG SubQ, UNEEG Medical), and more are in development
at centers and companies around the world. In providing pre-
viously unobtainable data, these minimally invasive solutions
may lead to a paradigm shift in the management of epilepsy,
where clinical decisions will be based on objective brain ep-
ileptic activity, including seizure counts, sleep quality, and
This review describes the novel class of subscalp EEG
recording devices that can be implanted subcutaneously be-
tween the scalp and the cranium. A search on PubMed in
May 2020 for (((subcutaneous OR subgaleal OR subder-
mal OR subscalp OR epicranial OR epiosteal) AND EEG)
AND (epilepsy OR seizure)) resulted in 116 results, with
only a few of the systems mentioned in the current article
appearing. Given the sparse literature, we chose to perform
a knowledge-driven review of these EEG devices. The re-
view is based on information obtained from literature, con-
ferences, and personal correspondence as well as manually
reviewing references to articles mentioned in the literature
of the non-EEG and wearable EEG seizure detection de-
vices mentioned above.
We describe and provide an overview of current efforts for
subscalp EEG systems, commercially available or in devel-
opment, and discuss the utility of ultra–long-term monitoring
using subscalp devices in epilepsy and the advantages that
objective seizure counts can provide. We also speculate on
automatic seizure detection, chronotherapy, circadian rhythm, epilepsy monitoring and recording,
subcutaneous EEG
Key Points
• A new generation of subscalp, continuous brain
monitoring systems have the potential to advance
treatment and diagnosis in epilepsy
• First studies comparing subscalp recordings with
scalp EEG are favorable and show that seizures
can be documented electrographically
• Adoption of subscalp, ultra–long-term EEG moni-
toring may cause a shift from subjective seizure
reporting to objective seizure counting
The true value of ultra–long-term EEG has yet to
be proven; more data collected over long periods
of time are essential to show the benefit
TABLE 1 Overview and core characteristics of known subscalp EEG systems certified or currently in development
Device Channels/montage Recording modalities
EEG sampling
rate Battery
Continuous raw
data available Status
CE-approved devices
24/7 EEG SubQ 2 channels/unilateral EEG+3 axis accelerometry 207Hz External/24h
Yes Yes CE-marked by April
Devices in validation phase
Minder 2 channels/bilateral EEG a External/24h
Yes Yes Clinical trial is
EASEE 4 channel/unilateral
EEG a a Yes a Clinical trial is
Epios 7 channels/temporal
OR 14 channels/
bitemporal OR 28
channels/full montage
EEG+ECG + audio+3
axis accelerometer
250Hz External/24h
Yes Yes Clinical trial to start
in 2020
Neuroview Technology 1 or 2 channels/
EEG+3 axis accelerometry 256 Internal/1y No No; only relevant
Clinical trial to start
in 2020
UltimateEEG Up to 8 channels/
a a a a a Clinical trial to start
in 2020
Abbreviation: EEG, electroencephalography.
aTo be decided or not yet disclosed.
future possibilities of mechanistic insights into the epileptic
brain, seizure forecasting, and combination with non-EEG
modalities, and finally, we discuss current challenges and
limitations of the subscalp technology.
EEG is the most important paraclinical modality in diagnos-
ing epilepsy. In addition, it helps classify seizure types and
epilepsy syndromes.16 When routine outpatient EEG provides
insufficient information, long-term monitoring in a hospital-
based epilepsy monitoring unit (EMU) is the next classical
option. To mitigate the high costs of such inpatient investiga-
tions, many home-based solutions have been proposed, often
involving a few days of scalp EEG and a webcam placed in
the patient's house.17,18 Depending on the results of these in-
vestigations, surgical resection of the seizure focus may be
an option, and an additional intracranial EEG study is often
Motivation for subscalp EEG
The development of subscalp EEG devices is motivated by
an unmet clinical need that neither scalp nor intracranial EEG
addresses: ultra–long-term (ie, >1month) EEG data collec-
tion in a home environment that can reveal temporal fluctua-
tions in patterns of seizures. This may have many advantages
for personalized epilepsy management in the context of rare
seizures, cycles of epileptic brain activity in a majority of pa-
tients, and alternating seizure localization in some individu-
als with multifocal epilepsies (eg, bitemporal epilepsy).20–24
Before discussing its potential for clinical practice in detail in
Section 3, a technical review of current solutions for subscalp
EEG follows.
Key technical aspects
Technically, the electrodes are implanted subcutaneously
under the scalp but above the bone. Electrode location dif-
fers among devices and can be varied for some. The subscalp
placement removes the need for electrode care, avoids skin
abrasions, and secures a stable and low-impedance recording,
where several types of artifacts are attenuated.25–27 Modeling
studies show that subscalp electrodes provide more specific
and accurate measurements compared to scalp electrodes
but with lower spatial and temporal resolution than intracra-
nial electrodes.28 A comparison between subscalp and scalp
electrodes shows that the signal quality of the subscalp elec-
trodes were at least equally good during background activity
with closed and open eyes, and might be better during bodily
movements.29 Sleep recordings are also improved because
subscalp electrodes are less obtrusive than scalp electrodes
in the recumbent (sleeping) position. Many algorithms have
been proposed to remove noise from EEG30; however, espe-
cially for the modalities with only a few channels, this will be
challenging although not entirely impossible.31
Besides the implant, an external unit for power, data
storage, and transmission is needed. Five of six solutions
have opted for transmission of the data out of the implant
(see below). This requires an external battery that is simple
to recharge, easy to use, discreet, and unobtrusive. The sys-
tem should be able to function for prolonged monitoring for
>30 days and potentially for many months or years. Such
devices should also be connected to a secure, cloud-based
database supported by software applications to help organize
and analyze the recorded data. The same five solutions that
opted for external battery also provide continuous raw EEG
signals for later expert interpretation aided by detection al-
gorithms. Some solutions also include embedded software
for real-time EEG analysis, and some solutions are aiming
to enhance classification accuracy with multimodal detection
algorithms by including other physiological modalities such
as ECG, accelerometry, or voice recordings. Table1 gives an
overview of the different current subscalp EEG systems that
are described below.
Overview of current subscalp
EEG systems
Different subscalp devices are proposed, and they vary with
respect to the number of channels (from two to 32), degree
of invasiveness (one incision under local anesthesia or up to
four incisions under local or general anesthesia), and main
application (seizure counting, alarming, forecasting, locali-
zation, neurofeedback, or neurostimulation). This section
provides an overview of the characteristics of different sub-
scalp EEG devices that are presented below in alphabetical
order. Figure1 gives an overview of several of the systems
described below as well as main application areas.
24/7 EEG SubQ from UNEEG Medical (Lynge, Denmark)
features two bipolar channels introduced under local anes-
thesia. The SubQ was used to record EEG in healthy sub-
jects29 as well as to detect clinically relevant electrographic
seizures in epilepsy patients, showing high reliability and
tolerance.32,33 The device comes with dedicated software for
automatic seizure detection and EEG visualization. The de-
vice is CE-marked, and multiple clinical trials are ongoing.34
The Epicranial Application of Stimulation Electrodes
for Epilepsy from Precisis (Heidelberg, Germany) uses five
subscalp platelet electrodes (four smaller electrodes arranged
around a larger center one). This arrangement is inspired
by the surface Laplacian concept for improved stimulation
depth. It is meant to be implanted above a lesioned brain area
and/or epileptogenic focus and is capable of recording as well
as delivering neurostimulation at an individualized closed-
loop setting.35 A clinical trial is ongoing.
The Epios system from the Wyss Center for Bio and
Neuroengineering (Geneva, Switzerland) aims to offer flex-
ible configurations, from focal or bitemporal electrode lay-
outs to broad coverage transposing the locations of the full
10-20 scalp EEG montage to the subscalp compartment.
Implantation of the full montage is done under general an-
esthesia in <1 hour, through two to four small incisions
(<1 cm) using specialized epiosteal tunneling tools. With
lower coverage, implantation under mild sedation or nerve
blocking is being considered. EEG data are transferred wire-
lessly to a headpiece and on to a body-worn unit for power
and temporary storage.36 The body-worn unit also supports
multimodal coregistration (ECG, audio, accelerometry) that
is then transmitted to a secure cloud-based application de-
veloped to support long-term data visualization and analysis.
Preclinical trials are currently ongoing with the Epios im-
plant, and a clinical trial is expected to start in 2020.
Minder from Epi-Minder (Melbourne, Australia) is a
subscalp device that implants a multichannel electrode lead
across the skull using a tunneling procedure so that both
hemispheres are covered. Minder has the potential to provide
long-term and continuous measures of the EEG, which will
provide a platform to support improved diagnosis and man-
agement of epilepsy. A clinical trial is ongoing.
The Neuroview Technology Ally (Englewood, NJ) is
being developed as a fully implantable, subscalp EEG record-
ing system to quantify seizures and aid in the diagnosis of
infrequent paroxysmal episodes of altered consciousness or
convulsive activity.37 The fully implanted device can record
for 1 year of continuous use without the need to recharge.
Low-power, on-board algorithms identify epochs of subscalp
EEG activity suspicious for seizures and patient-identified
events. EEG epochs are transferred to a cloud platform via a
connected smartphone-based application for the neurologist
to review with the aid of cloud-based machine learning al-
gorithms to verify seizures and display and quantify seizure
activity between clinic visits. On-device detection algorithms
can subsequently be customized to improve the specificity of
seizure detection. Clinical trials are expected to commence
in 2020.
UltimateEEG from BrainCare Oy (Tampere, Finland)
uses platinum on silicon electrodes, with custom order sizes,
number of channels, and distance between electrodes. With
support for up to eight channels, the device offers mapping of
seizure propagation. The planar electrodes are directionally
focused toward the electrical sources to reduce electromyo-
graphic (EMG) noise. A clinical trial is expected to com-
mence in 2020.
Other subscalp EEG systems
Several studies have helped clarify other aspects of subscalp
EEG recordings, but none of these seems to have evolved
into a commercial concept. Jochum et al38 experimented with
an implanted EEG system on a sheep and found a correlation
coefficient of 0.86-0.92 with simultaneous scalp EEG at the
same location. Ahmed et al39 investigated high-density sub-
dermal EEG probes subjected to artificial aging, compared
volume conduction simulations based on four-layered head
models, and found that recordings from the subdermal elec-
trodes were less attenuated at higher frequencies than scalp
EEG recordings. Do Valle et al40 investigated an eight-chan-
nel implanted EEG-recorder with electrode arrays projecting
FIGURE 1 Overview of different implantable parts of subscalp
devices as well as application areas. From lower left and up: 24/7
EEG SubQ from UNEEG medical, Denmark; the Epios system
from the Wyss Center for Bio and Neuroengineering, Switzerland;
UltimateEEG from BrainCare Oy, Finland; Minder from Epi-Minder,
cranially in a fanlike pattern from behind the ear and used it
to test a seizure detection algorithm. Xu et al41 did a proof-
of-concept of subscalp EEG sensors that were comparatively
insensitive to motion-related artifacts that can be expected to
occur more often in daily life. In an intensive care unit set-
ting, low-maintenance subdermal wire electrodes have been
used, but although they are quick to set up, they can also be
dislodged easily, thus require additional fixation, and do not
appear to be practical for chronic monitoring in daily life.27
Other modalities
Multimodal monitoring, combining measurements of two or
more different modalities, can be used to improve classifi-
cation accuracy above what can be achieved by using one
modality.10,42 Heart rate variability features are correlated
with para- and sympathetic activity, and this can be used
to detect focal seizures or added to EEG-based detection to
improve accuracy.42 Electrodermal activity exhibits changes
during generalized tonic-clonic seizures (GTCS) and focal
seizures43 and is positively correlated with longer duration
of postictal EEG suppression.44 Audio recording could be
useful to detect the initial vocalization or noise sometimes
occurring during a seizure (ictal cries) or noise that can be
characteristic of the postictal period.
Home video combined with ambulatory EEG has demon-
strated clinical utility, aiding in interpretation in 14 of 17 (82%)
cases in one study.17 Subscalp EEG recordings could be com-
bined with video or other modalities in a similar way. Video
quality in a home setting can be at the same level as in-hos-
pital video recordings, and a majority of patients would pre-
fer home monitoring. Cognitive and behavioral testing during
seizures matter for seizure classification and could possibly be
implemented in the home setting if online seizure detection al-
gorithms are sufficiently accurate, with low latency of detection
after onset. Standardized ictal test batteries have been proposed
and are feasible for all but very short seizures.45
Tolerance and safety
A review of the literature on complication rates with similar
devices for deep brain neurostimulation and occipital nerve
stimulation revealed that expected complications include in-
fections (<2%), lead migration (~20%), fracture (~4%), and
skin erosion (~4%).46–49 Infections, a dreaded complication
with intracranial material, would here be limited to the sub-
scalp compartment, as the skull would act as an additional
protective barrier for the brain. Subscalp hematoma and scalp
fibrosis are expected to be very rare.
Prospective tolerance and safety data specific to sub-
scalp EEG come from a single trial.33 No serious adverse
device-related events occurred, and the patients generally
found the device easy to use, although this was only collected
anecdotally. Minor annoyances were reported, such as diffi-
culty with simultaneously wearing glasses, occasional nightly
disconnections, and the necessity of wearing clothes at night
to fix the external device. No participants felt constrained in
their ability to perform jobs or leisure activities, although six
of nine reported mild headache up to 1 week after surgery.
One participant reported uncommon mild headaches that
were tolerated without analgesics or other interventions.
In a study with a subdermal wire electrode partially im-
planted for 60days in the intensive care unit, no safety con-
cerns were noted.50
It is estimated that 50% of seizures are unreported, par-
ticularly nocturnal seizures or focal seizures with impaired
awareness.2 The direct consequence is the inability to ascer-
tain therapeutic response; how often is epilepsy undertreated
when seizures are underreported, and are true changes in
seizure frequency overlooked? Patients may also misclassify
nonepileptic events as seizures in their diaries, potentially
causing overtreatment. Furthermore, the issue of comorbid
epileptic seizures and nonepileptic seizures is not uncom-
mon.51 In this section, we outline the most important aspects
and discuss the practical utility of subscalp EEG.
The value of personal long data
Today, there is an ongoing debate on the importance of detect-
ing purely EEG seizures that patients are unaware of and do
not feel negatively affected by.52 This discussion is important
for subscalp EEG, as its ambulatory nature makes simultaneous
video unviable and thus difficult to classify seizures as clinical
or not. Neuroimaging studies in patients with temporal lobe ep-
ilepsy have identified widespread anatomical abnormalities,53
and longitudinal studies in patients with chronic epilepsy show
declines in memory and intelligence quotient,54 so it is possible
that repeated seizures have negative consequences, or perhaps
these results come from preferential sampling of the most se-
verely affected patients with chronic refractory epilepsy. In ei-
ther case, ultra–long-term monitoring technology will be useful
in clarifying this important question.
The challenge of personal long data
With limited resources available for data review, a prereq-
uisite for ultra–long-term EEG systems is algorithms for
analysis of the vast amount of recorded data. A trained EEG
technician can analyze 24 hours of two-channel EEG in
2-3hours. Use of trending tools alone can reduce the time
for analysis by a factor of 8-10. Whether the algorithms need
to be online or offline, simple or complex, and patient-spe-
cific or generic will very much depend on the application.
The actual time series will always be valuable to validate
the findings of the algorithms such that algorithms might be
considered a data reduction method, while the final valida-
tion is still made by expert EEG reviewers supported by the
algorithms. The right level of sensitivity must minimize the
number of false negatives, because going through a tractable
number of false-positive clips is highly feasible in daily clini-
cal routine. In addition to underlying algorithms, data visu-
alization is also an important issue; if a patient has worn the
system for 6 months, a way to obtain an overview of the sei-
zure frequency, seizure duration, periodicity, and time of day
would be crucial. With machine learning and big data analy-
sis, the prospects for automated detections are considerable.
Objective seizure counting
Treatment decisions are informed by seizure counts, and
there have long been calls for more reliable measures than
what seizure diaries provide.1,2,55,56 Although wearables for
the detection of tonic-clonic seizures in particular are gaining
approval, the ability to automatically detect focal seizures,
particularly with impaired awareness or without major motor
features, remains unmet.57 Objective seizure counts may in-
form clinical decisions to avoid that an effective treatment
is abandoned because no discernible effect in self-reported
measures was apparent58 or that an inefficacious treatment
is maintained or initiated on the basis of nonepileptic events,
because nonseizure events (eg, antiepileptic drug [AED] side
effects, nonepileptic seizures) are incorrectly classified as
seizures by the patient.
Ultra–long-term EEG data have identified periodic pat-
terns in seizure and spike occurrences operating on different
timescales.22 Importantly, cycles appear to be stable within
individuals and thus potentially constitute interesting targets
for therapeutic intervention.22,59
Initial epilepsy diagnosis
The differential diagnosis of epilepsy is broad; syncope and
nonepileptic seizures are commonly misdiagnosed as epi-
lepsy, and less commonly, hypoglycemia, paroxysmal disor-
ders of movement, sleep disorders, transient ischemic attack,
migraines with aura, and transitory global amnesia.51 On top
of that, there is also the risk of not being diagnosed with epi-
lepsy when recurring seizures are present but not identified.
Before establishing a definitive diagnosis of epilepsy, char-
acterization of the events is a key step. Inpatient video-EEG
monitoring is regularly successful but may not capture events
if they are too infrequent. Figure2 visualizes the cumulative
probability functions for seizure detection as a function of
monitoring duration assuming a constant seizure frequency
where each day can be conceived as a Bernoulli trial for a
seizure occurring. Many patients will have seizure frequen-
cies < 1/wk and are thus unlikely to have a seizure during a
standard EMU visit.
Seizure localization
When seizures are refractory to medical treatment, surgery is
often indicated and has been increasingly used with improv-
ing results in the past decades.60 Presurgical workup regularly
requires the implantation of intracranial electrodes to refine
the localization of the seizure onset zone, sometimes in brain
areas inaccessible to scalp or subscalp EEG. However, sub-
scalp EEG that includes bilateral electrode coverage would
FIGURE 2 Cumulative probability functions for seizure detection as a function of monitoring duration
enable lateralization of seizures. A study of outpatient after
inpatient intracranial EEG monitoring in 82 patients with me-
sial temporal lobe epilepsy of unknown laterality reclassified
16 (20%) as having unilateral or bilateral onset, which can
help evaluate candidates for epilepsy surgery.23,24
Subscalp EEG that offers broad head electrode coverage
could localize to a given cerebral lobe, although studies will
be needed to confirm this. Basing surgical decisions on doz-
ens to hundreds of electrographic seizures instead of a hand-
ful typically collected in hospital is a promising possibility
for the future. Subscalp EEG will improve the continuum
between optimization of medical treatment and presurgical
planning, and represents a bridge partially mitigating both the
critical lack of information in outpatient epileptology and the
somewhat artificial conditions imposed in the EMU.11
Seizure alarming
A majority of patients and caregivers want some form of sei-
zure monitoring, either at night only or 24/7, to feel more safe
and less stigmatized.61 This is where subscalp EEG is most
likely to improve the everyday life of a person with epilepsy.
A large study on quality of life (QoL) in epilepsy describes
problems in terms of lower self-esteem, higher levels of anx-
iety and depression, social isolation, stigmatization, risk of
sudden unexpected death in epilepsy (SUDEP), and higher
rate of unemployment.62 Injuries (burns, head and dental in-
juries) increase with higher seizure frequency. Feelings of
stigmatization were common, and 48% worried about epi-
lepsy some or much of the time. Hopefully, a reliable online
alarm can alleviate such issues.
SUDEP is a major cause of anxiety and is one of the
primary motivations some people with epilepsy have for
wanting a seizure alarm. Death following a seizure may be
preceded by a critical interval, where an intervention could
potentially save lives.63 Because GTCS are the main risk
factor for SUDEP,63 the motivation for seizure alarming for
SUDEP prevention may be weaker for focal non–tonic-clonic
seizures. For a subscalp device to be relevant for SUDEP pre-
vention, it needs to provide at least equivalent performance to
wearables, or it could be part of a multimodal system that is
more robust. It could also be used to assess whether the risk
of SUDEP changes over time for a certain patient by esti-
mating changes in the postictal EEG, although this has to be
shown in a clinical trial.64–66
Seizure forecasting
When patients are asked directly about the impact of seizure
unpredictability, 66%-68% consider it an important or very
important aspect.67 Developing systems that can predict the
occurrence of a future seizure event with sufficient time to
act would be a game changer. Instead of a binary output,
the prediction could be expressed as elevated seizure risk,
referred to as seizure forecasting. One successful demonstra-
tion of seizure forecasting used intracranial EEG recordings
to provide visual feedback to patients minutes in advance
of seizures.68 Much effort has been put into developing so-
lutions for seizure prediction combining intracranial EEG
dataset and online competitions reaching classification ac-
curacies of 81%.69
However, as intracranial recordings are unlikely to be-
come widespread due to their invasiveness, it will be rele-
vant to test whether good forecasting performance can be
achieved on subscalp recordings. Although no prospective
study with good forecasting results on extracranial EEG has
yet been carried out, the authors are aware of several ongoing
studies that hopefully will shed new light on predictability
when ultra–long-term recordings are available. Such systems
should always be trained and tested on at least several months
of labeled data to cover natural physiological variation70 and
circadian and multidien cycles in epilepsy.22
Using subscalp EEG in the future
Ultra–long-term monitoring can be used both before and
after establishing a diagnosis of epilepsy. Long monitoring
durations are necessary to detect rare paroxysmal events, as
shown in Figure2. Routinely used solutions of drug taper-
ing, sleep deprivation, and other provocations may in some
cases induce events that differ from spontaneous seizures and
cloud the interpretation. Therefore, an outpatient-based so-
lution may under these circumstances outperform the EMU.
Currently, clinicians will estimate the underlying seizure fre-
quency before referring a patient to the EMU, but if ultra–
long-term EEG monitoring is an option, a probability plot as
in Figure2 could be informative when deciding the optimal
diagnostic strategy. Furthermore, having a subscalp EEG
implant does not prohibit an EMU stay for full video-EEG
characterization; on the contrary, given that multidien cycles
of seizures are highly prevalent among epilepsy patients,22
the hospital stay could be timed to take place during a period
of high likelihood of seizures.11
We envisage a toolkit, whereby subscalp devices for ultra–
long-term EEG monitoring can help detect focal or general-
ized seizures, and non-EEG modalities (EMG, ECG, others)
could be “added on” to the setup depending on the specific
circumstances. Furthermore, as a relationship between sleep
quality/duration and seizure risk has been suggested, the abil-
ity to record objective sleep quality and seizures is critical to
understanding whether strategies to improve sleep can help
seizure control. One study has even shown that two-channel
subscalp EEG is sufficient to do robust sleep staging.71,72
As no published studies have dealt with the usability of sub-
scalp devices, we must look into the readiness for wearables,
scalp EEG, and intracranial EEG.
Neurologist readiness
In a survey of 21 neurologists,73 16 agreed that current am-
bulatory recordings are diagnostically useful over traditional
inpatient recordings, and 18 agreed that there is a further
need for wearable EEG devices. Although the questionnaire
addresses standard ambulatory EEG, it does give a good indi-
cation that there is an unmet need that exceeds the 30-minute
routine EEG and 1- to 3-week EMU stay.
Surveys of medical doctors’ views on the usefulness of
seizure detection devices found that most considered alarms
with major motor seizures and seizures associated with fall
important, and 53% gave a 4 or 5 on a 0-5 scale of necessity
for alarms for impaired awareness during focal seizure and
Patient readiness
Patients have heterogeneous expectations for the seizure
tracking device’s performance but describe desirable features
in medication reminders, water-proof design, real-time data
analysis, improved diagnostics, and seizure management.74
Surveys suggest that patients would accept devices for sei-
zure registrations provided that they have only a small nega-
tive effect on daily life,75 but patients are concerned about
appearance and visibility of sensors, so concealed sensors
could help increase user acceptance.15 Subscalp sensors are
concealed but may use an external device for power and/or
data storage that can be hidden under the user's clothing. The
majority (82%) of surveyed patients expected a seizure detec-
tion sensitivity of 90% or better.61
When asking the patients whether they would agree to
wear a device on a daily basis, the participants saw the pos-
sible benefits for improved treatment effect and valued this
benefit more than the possible inconvenience of wearing a
sensor.15 Most (90%) would prefer the size of a wristwatch
or smaller. Obviously, acceptance will vary on an individual
basis and depend on the tradeoff between perceived benefit
and the sum of inconveniences and potential side effects. It
must be kept in mind that the questionnaires mentioned deal
in hypotheticals regarding implantable devices, and what
patients might imagine when posed such questions may not
accurately reflect their reactions toward the real devices de-
tailed in this review. The implantable cardiac loop recorder
is well accepted, and so it seems likely that with sufficient
benefit for the recipient, implantable subscalp EEG devices
will also be well accepted.
Some surveys suggest that up to 45.8% of patients think
documentation of seizures is either an “important” or an “es-
sential” feature in a long-term seizure detection system.75
Seizure alarming can help to reduce anxiety and assist auton-
omy; 60.6% of caregivers found that the seizure alarm gave
them more freedom, and 30.3% believed that it gave the pa-
tients more autonomy.76
In this section, we outline proposed objectives in future tri-
als, considerations about low spatial resolution, logistical
considerations relating to implantation, data management,
and safety.
Future trials
Future trials involving ultra–long-term monitoring in epi-
lepsy using subscalp EEG will be required to explore the
value proposition of the technology. Although the first safety
and feasibility studies have been completed, evidence of
clinical usefulness of ultra–long-term EEG recordings is not
available at the current stage of development, although mul-
tiple studies are in preparation or have commenced.
Development of seizure detection algorithms should fol-
low the standards for reporting diagnostic accuracy proposed
in Beniczky and Ryvlin77 or Cohen.78 We should be moving
from small sample sizes and repeated training on retrospec-
tive data to prospective trials with larger samples and pre-
defined thresholds for the algorithm’s detection. It should
be clear whether the goal of the trial is seizure alarming or
counting. Detection of interictal abnormalities should also
adhere to published standards.4
Trials on seizure detection devices focus on reliable and
accurate seizure counting, which rapidly raises the question
of the clinical relevance of the many electrographic seizures
typically recorded with ultra–long-term EEG. This is an op-
portunity to improve the quality of how seizures are defined,
although the question is not trivial.52 More advanced trials
aimed at optimizing medical management, increasing the
ability to identify a change in seizure frequency, or inform-
ing epilepsy surgery will be necessary after this first step is
achieved. Using the classical patient-reported outcomes for
such trials (including seizure self-report) would defy the
purpose. Other impacts could be quantified in terms of QoL
scores, changes in level of disability, number of accidents,
and mortality, or simply whether the neurologist found a
treatment improvement or was aided in reaching his thera-
peutic decisions.
Without a full understanding of the real seizure burden,
outcomes of medication trials are often set for failure, pro-
longing patients suffering. Simulations based on self-re-
ported seizure events in the SeizureTracker database have
investigated factors that can reduce costs of randomized
clinical trials on AED efficacy without lowering statistical
power, but did not attempt to incorporate seizure event uncer-
tainty directly into the model.79 Simulations could be useful
in clarifying the impact of objective versus subjective seizure
counts in epilepsy for randomized controlled trials on AEDs
in advance of real data having been accumulated, which will
take many years.
Considerations regarding reduced
spatial resolution
One disadvantage of most suggested subscalp devices
except one compared to standard scalp EEG is reduced
spatial resolution. For focal abnormalities, this can re-
sult in lower sensitivity compared to standard scalp EEG,
but strategically selecting the location of the subscalp
electrodes (eg, guided by abnormality seen on standard
EEG or magnetic resonance imaging) might be useful
to inform placement in individual cases. Spikes in the
interictal EEG might inform implantation strategy, but
unilateral implantation will likely miss seizures confined
to the contralateral hemisphere, precluding a discovery
of bilateral seizure onset in such cases. In the absence
of interictal spikes and lateralizing semiology from the
patient history, but where a strong suspicion of epilepsy
is present, a bilateral implantation strategy could be
considered. One development has shown that it is pos-
sible to place electrodes according to the 10-20 system
in the subscalp space with minimally invasive surgery,
although it is done under general anesthesia. Importantly,
a major limitation of subscalp EEG as compared to in-
tracranial EEG is that it cannot monitor deep structures
of the brain and has in that sense the same “field of view”
as scalp EEG.
Subscalp EEG recording is an emergent technology. Studies
comparing subscalp recordings with scalp EEG are favorable
and show that seizures can be documented electrographically.
Different devices are being developed to offer a range of
subscalp electrode coverage, some with minimally invasive
implantation of just a few electrodes under local anesthesia,
others increasing coverage to the full head with general an-
esthesia. Some devices are fit for seizure counting, whereas
others aim at localization.
The true value of ultra–long-term EEG has yet to be estab-
lished. It could give novel insights into brain function and is
likely to open new avenues for biomarker discovery, person-
alized treatment, and population analytics, especially when
combined with other complementary information such as
movement and heart rate. Only when data have been collected
over long periods of time will the true value of algorithm de-
velopment for seizure prediction in patients be apparent.
Adoption of subscalp EEG for ultra–long-term mon-
itoring in epilepsy will cause a shift away from subjective
seizure reporting in favor of objective seizure counting, a
long-awaited change. This could have a broad impact on the
daily management of epilepsy and place patients at the center
of management of the disorder.
For clinical science, the technology will also facilitate
the collection of otherwise very rare ultra–long-term EEG
recordings that could not only provide novel insights into ep-
ilepsy and other brain diseases but also provide high tempo-
ral resolution of physiological short- and long-term rhythms
over time.
M.P.R. is funded in part by MRC Centre for
Neurodevelopmental Disorders (MR/N026063/1), the
National Institute for Health Research Biomedical Research
Centre at South London and Maudsley National Health
Service Foundation Trust and the Epilepsy Foundation. M.C.
is funded by the Epilepsy Foundation and National Health
and Medical Research Council. T.W.K. is funded in part
by the health science foundations of Region of Zealand and
Region of Southern Denmark, as well as Innovation Fund
J.D.-H. is a full-time employee at UNEEG medical, a
company developing and producing a subscalp EEG de-
vice. M.B. is a part-time employee at Wyss Center for Bio
and Neuroengineering, a not-for-profit foundation, and
coinventor on an international patent application under
the Patent Cooperation Treaty number 62665486 entitled
“Neural Interface System.” M.P.R. holds research fund-
ing from Medical Research Council, National Institute
for Health Research, Wellcome, Epilepsy Research UK,
Epilepsy Foundation of America, European Commission,
and Canadian Institutes of Health Research. He is UK
Chief Investigator of a trial sponsored by Xenon Pharma
and has research collaborations with UNEEG, UCB,
ANT Neuro, J&J, and Seer Medical. He has been an ad-
visory board member for UNEEG medical. He holds pat-
ent WO2013182848A1. M.C. is CMO at Epi-Minder, as
well as CMO at SEER Medical. G.K. is Chief Technology
Officer at the Wyss Center for Bio and Neuroengineering,
an independent, nonprofit research and development organ-
ization developing a subscalp EEG device. J.M.H. consults
for Epi-Minder, a company developing a subscalp EEG
device. D.F. is a cofounder of Neuroview Technology and
holds equity interests in the company. He also receives sal-
ary support for consulting and clinical trial–related activi-
ties performed on behalf of the Epilepsy Study Consortium,
a nonprofit organization. D.F. receives no personal income
for these activities. New York University receives a fixed
amount from the Epilepsy Study Consortium toward D.F.’s
salary. Within the past year, the Epilepsy Study Consortium
received payments for research services performed by
D.F. from Adamas, Axcella, Biogen, Crossject, CuroNZ,
Engage Pharmaceuticals, Eisai, GW Pharmaceuticals,
Pfizer, SK Life Science, Takeda, Xenon, and Zynerba. He
has also served as a paid consultant for Eisai. He has re-
ceived an honorarium from Neuropace. He has received
travel support from Medtronic, Eisai, and the Epilepsy
Foundation. He receivesresearch support from the Centers
for Disease Control and Prevention, The National Institute
of Neurological Disorders and Stroke, Epilepsy Foundation,
Empatica, Epitel, UCB, and Neuropace. He serves on the
scientific advisory board for Receptor Life Sciences and
holds equity interests in the company. J.P. has participated
in clinical trials for Eisai, UCB, and Bial; received research
grants from Eisai, Medtronic, UCB, and LivaNova; received
speaker honoraria from LivaNova, Eisai, Medtronic, Orion
Pharma, and UCB; received support for travel to congresses
from LivaNova, Eisai, Medtronic, and UCB; and partici-
pated in advisory boards for LivaNova, Eisai, Medtronic,
UCB, and Pfizer. He is a cofounder of Neuroeventlabs and
holds equity interest in the company. He is also medical
advisor to Braincare. I.C.Z. consults for UNEEG medical.
T.W.K. consults for UNEEG medical. We confirm that we
have read the Journal’s position on issues involved in ethi-
cal publication and affirm that this report is consistent with
those guidelines.
Jonas Duun-Henriksen
Maxime Baud
Mark P. Richardson
Mark Cook
Daniel Friedman
Ivan C. Zibrandtsen
Troels W. Kjaer
1. Blachut B, Hoppe C, Surges R, Elger C, Helmstaedter C. Subjective
seizure counts by epilepsy clinical drug trial participants are not re-
liable. Epilepsy Behav. 2017;67:122–7.
2. Elger CE, Hoppe C. Diagnostic challenges in epilepsy: sei-
zure under-reporting and seizure detection. Lancet Neurol.
3. Ramgopal S, Thome-Souza S, Jackson M, et al. Seizure detection,
seizure prediction, and closed-loop warning systems in epilepsy.
Epilepsy Behav. 2014;37:291–307.
4. Casson AJ, Luna E, Rodriguez-Villegas E. Performance metrics
for the accurate characterisation of interictal spike detection algo-
rithms. J Neurosci Methods. 2009;177(2):479–87.
5. Bateson AD, Baseler HA, Paulson KS, Ahmed F, Asghar AUR.
Categorisation of mobile EEG: a researcher’s perspective. BioMed
Res Int. 2017;2017:1–15.
6. Casson AJ. Wearable EEG and beyond. Biomed Eng Lett.
7. Van de Vel A, Cuppens K, Bonroy B, et al. Non-EEG seizure de-
tection systems and potential SUDEP prevention: state of the art:
review and update. Seizure. 2016;41:141–53.
8. Bidwell J, Khuwatsamrit T, Askew B, Ehrenberg JA, Helmers S.
Seizure reporting technologies for epilepsy treatment: a review of
clinical information needs and supporting technologies. Seizure.
9. Jory C, Shankar R, Coker D, McLean B, Hanna J, Newman C.
Safe and sound? A systematic literature review of seizure detection
methods for personal use. Seizure. 2016;36:4–15.
10. Leijten FSS, Dutch TeleEpilepsy Consortium. Multimodal seizure
detection: a review. Epilepsia. 2018;59(Suppl 1):42–7.
11. Baud MO, Rao VR. Gauging seizure risk. Neurology.
12. Khan S, Nobili L, Khatami R, et al. Circadian rhythm and epilepsy.
Lancet Neurol. 2018;17(12):1098–108.
13. Krahn AD, Klein GJ, Yee R, Skanes AC. The use of monitoring
strategies in patients with unexplained syncope—role of the exter-
nal and implantable loop recorder. Clin Auton Res. 2004;14(Suppl
14. Krahn AD, Klein GJ, Skanes AC, Yee R. Insertable loop re-
corder use for detection of intermittent arrhythmias. Pacing Clin
Electrophysiol. 2004;27(5):657–64.
15. Bruno E, Simblett S, Lang A, et al. Wearable technology in epi-
lepsy: the views of patients, caregivers, and healthcare profession-
als. Epilepsy Behav. 2018;85:141–9.
16. Tatum WO, Rubboli G, Kaplan PW, et al. Clinical utility of EEG in
diagnosing and monitoring epilepsy in adults. Clin Neurophysiol.
17. Goodwin E, Kandler RH, Alix JJP. The value of home video
with ambulatory EEG: a prospective service review. Seizure.
18. Kandler R, Ponnusamy A, Wragg C. Video ambulatory EEG:
a good alternative to inpatient video telemetry? Seizure.
19. Wong CH, Birkett J, Byth K, et al. Risk factors for complications
during intracranial electrode recording in presurgical evaluation of
drug resistant partial epilepsy. Acta Neurochir. 2009;151(1):37–50.
20. Chan AY, Knowlton RC, Chang EF, Rao VR. Seizure localization
by chronic ambulatory electrocorticography. Clin Neurophysiol
Pract. 2018;3:174–6.
21. Karoly PJ, Freestone DR, Boston R, et al. Interictal spikes and
epileptic seizures: their relationship and underlying rhythmicity.
Brain. 2016;139(4):1066–78.
22. Baud MO, Kleen JK, Mirro EA, et al. Multi-day rhythms modulate
seizure risk in epilepsy. Nat Commun. 2018;9(1):1–10.
23. King-Stephens D, Mirro E, Weber PB, et al. Lateralization of me-
sial temporal lobe epilepsy with chronic ambulatory electrocorti-
cography. Epilepsia. 2015;56(6):959–67.
24. Hirsch LJ, Mirro EA, Salanova V, et al. Mesial temporal resec-
tion following long-term ambulatory intracranial EEG monitoring
with a direct brain-responsive neurostimulation system. Epilepsia.
25. Wendel K, Väisänen J, Seemann G, Hyttinen J, Malmivuo J. The
influence of age and skull conductivity on surface and subdermal
bipolar EEG leads. Comput Intell Neurosci. 2010;2010:397272.
26. Duun-Henriksen J, Kjaer T, Sørensen J, Juhl C. Ultra-long term
subcutaneous recording system for EEG surveillance. 15th Eur
Congr Clin Neurophysiol. 2015;127(3):e104–5.
27. Young GB, Ives JR, Chapman MG, Mirsattari SM. A comparison
of subdermal wire electrodes with collodion-applied disk elec-
trodes in long-term EEG recordings in ICU. Clin Neurophysiol.
28. Mendes MR, Subramaniyam NP, Wendel-Mitoraj K. Evaluating
the electrode measurement sensitivity of subdermal electroen-
cephalography electrodes. In: 2015 7th International IEEE/EMBS
Conference on Neural Engineering (NER). Montpellier, France:
IEEE; 2015:1092–5.
29. Duun-Henriksen J, Kjaer TW, Looney D, et al. EEG signal quality
of a subcutaneous recording system compared to standard surface
electrodes. J Sens. 2015;2015:1–9.
30. Islam MK, Rastegarnia A, Yang Z. Methods for artifact detec-
tion and removal from scalp EEG: a review. Neurophysiol Clin.
31. Liu Q, Liu A, Zhang X, Chen X, Qian R, Chen X. Removal of
EMG artifacts from multichannel EEG signals using combined
singular spectrum analysis and canonical correlation analysis. J
Healthc Eng. 2019;2019:1–13.
32. Weisdorf S, Gangstad SW, Duun-Henriksen J, Mosholt KSS, Kjær
TW. High similarity between EEG from subcutaneous and prox-
imate scalp electrodes in patients with temporal lobe epilepsy. J
Neurophysiol. 2018;120(3):1451–60.
33. Weisdorf S, Duun-Henriksen J, Kjeldsen MJ, Poulsen FR,
Gangstad SW, Kjaer TW. Ultra-long-term subcutaneous home
monitoring of epilepsy—490 days of EEG from nine patients.
Epilepsia. 2019;60(3):2204–14.
34. 24/7 EEG™ SubQ. Available at:
psy/produ cts/subq. Accessed June 16, 2020.
35. EASEE® electrode. Available at: https://preci ology/
subcu taneo us-elect rode. Accessed June 16, 2020.
36. Epios. Available at: https://www.wyssc ces/epios
-brain -monit oring. Accessed June 16, 2020.
37. Neuroview Technology. Available at: https://neuro viewt
Accessed June 16, 2020.
38. Jochum T, Engdahl S, Kolls BJ, Wolf P. Implanted electrodes for
multi-month EEG. In: 2014 36th Annu Int Conf IEEE Eng Med
Biol Soc EMBC. Chicago, IL: IEEE; 2014:6543–8.
39. Ahmed Z, Reddy J, Deshpande K, et al. Flexible ultra-resolution
subdermal EEG probes. In: 2018 IEEE Biomedical Circuits and
Systems Conference (BioCAS). Cleveland, OH, USA: IEEE; 2018:
40. Do Valle BG, Cash SS, Sodini CG. Low-power, 8-channel EEG
recorder and seizure detector ASIC for a subdermal implantable
system. IEEE Trans Biomed Circuits Syst. 2016;10(6):1058–67.
41. Xu J, Wang B, McLaughlin B, Schachther S, Yang Z. An integrated
sub-scalp EEG sensor for diagnosis in epilepsy. In: IEEE Biomed
Circuits Syst Conf Eng Healthy Minds Able Bodies BioCAS
2015—Proc. Atlanta, GA: IEEE; 2015:1–4.
42. van Westrhenen A, De Cooman T, Lazeron RHC, Van Huffel S,
Thijs RD. Ictal autonomic changes as a tool for seizure detection: a
systematic review. Clin Auton Res. 2019;29(2):161–81.
43. Poh M-Z, Swenson NC, Picard RW. A wearable sensor for unobtru-
sive, long-term assessment of electrodermal activity. IEEE Trans
Biomed Eng. 2010;57(5):1243–52.
44. Meyer S, Strittmatter M. Autonomic changes with seizures correlate
with postictal EEG suppression. Neurology. 2013;80(16):1538–9.
45. Beniczky S, Neufeld M, Diehl B, et al. Testing patients during seizures:
a European consensus procedure developed by a joint taskforce of the
ILAE–Commission on European Affairs and the European Epilepsy
Monitoring Unit Association. Epilepsia. 2016;57(9):1363–8.
46. Maslen H, Cheeran B, Pugh J, et al. Unexpected complications of
novel deep brain stimulation treatments: ethical issues and clini-
cal recommendations: novel deep brain stimulation treatments.
Neuromodulat Technol Neural Interface. 2018;21(2):135–43.
47. Umemura A, Oyama G, Shimo Y, et al. Current topics in deep brain stim-
ulation for Parkinson disease. Neurol Med Chir. 2016;56(10):613–25.
48. Robbins MS, Lipton RB. Transcutaneous and percutaneous neu-
rostimulation for headache disorders. Headache J Head Face Pain.
49. Miller S, Sinclair AJ, Davies B, Matharu M. Neurostimulation in the
treatment of primary headaches. Pract Neurol. 2016;16(5):362–75.
50. Martz GU, Hucek C, Quigg M. Sixty day continuous use of subder-
mal wire electrodes for EEG monitoring during treatment of status
epilepticus. Neurocrit Care. 2009;11(2):223–7.
51. Benbadis S. The differential diagnosis of epilepsy: a critical re-
view. Epilepsy Behav. 2009;15(1):15–21.
52. Karoly P, Goldenholz DM, Cook M. Are the days of counting sei-
zures numbered? Curr Opin Neurol. 2018;31(2):162–8.
53. Bell B, Lin JJ, Seidenberg M, Hermann B. The neurobiology of
cognitive disorders in temporal lobe epilepsy. Nat Rev Neurol.
54. Sutula TP, Hagen J, Pitkänen A. Do epileptic seizures damage the
brain? Curr Opin Neurol. 2003;16(2):189–95.
55. Fisher RS, Blum DE, DiVentura B, et al. Seizure diaries for clinical
research and practice: limitations and future prospects. Epilepsy
Behav. 2012;24(3):304–10.
56. Hoppe C, Poepel A, Elger CE. Epilepsy: accuracy of patient sei-
zure counts. Arch Neurol. 2007;64(11):1595–9.
57. Bruno E, Viana PF, Sperling MR, Richardson MP. Seizure detec-
tion at home: do devices on the market match the needs of people
living with epilepsy and their caregivers? Epilepsia. 2020. http://
58. Weisdorf S, Zibrandtsen IC, Kjaer TW. Subcutaneous EEG moni-
toring reveals AED response and breakthrough seizures. Case Rep
Neurol Med. 2020;2020:1–6.
59. Karoly PJ, Goldenholz DM, Freestone DR, et al. Circadian and cir-
caseptan rhythms in human epilepsy: a retrospective cohort study.
Lancet Neurol. 2018;17(11):977–85.
60. Baud MO, Perneger T, Rácz A, et al. European trends in epilepsy
surgery. Neurology. 2018;91(2):e96–106.
61. Van de Vel A, Smets K, Wouters K, Ceulemans B. Automated non-
EEG based seizure detection: do users have a say? Epilepsy Behav.
62. Taylor RS, Sander JW, Taylor RJ, Baker GA. Predictors of
health-related quality of life and costs in adults with epilepsy: a
systematic review. Epilepsia. 2011;52(12):2168–80.
63. Ryvlin P, Nashef L, Lhatoo SD, et al. Incidence and mech-
anisms of cardiorespiratory arrests in epilepsy monitoring
units (MORTEMUS): a retrospective study. Lancet Neurol.
64. Pottkämper JCM, Hofmeijer J, van Waarde JA, van Putten
MJAM. The postictal state—what do we know? Epilepsia.
65. Carlson C. Generalized postictal EEG background suppression: a
marker of SUDEP risk: postictal EEG suppression and SUDEP.
Epilepsy Curr. 2011;11(3):86–7.
66. Arbune AA, Conradsen I, Cardenas DP, et al. Ictal quantitative sur-
face electromyography correlates with postictal EEG suppression.
Neurology. 2020;94(24):e2567–76.
67. Schulze-Bonhage A, Sales F, Wagner K, et al. Views of patients
with epilepsy on seizure prediction devices. Epilepsy Behav.
68. Cook MJ, O’Brien TJ, Berkovic SF, et al. Prediction of seizure
likelihood with a long-term, implanted seizure advisory system in
patients with drug-resistant epilepsy: a first-in-man study. Lancet
Neurol. 2013;12(6):563–71.
69. Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri
HP. Seizure prediction—ready for a new era. Nat Rev Neurol.
70. Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure pre-
diction: the long and winding road. Brain J Neurol. 2007;130(Pt
71. Rossi KC, Joe J, Makhija M, Goldenholz DM. Insufficient sleep,
electroencephalogram activation, and seizure risk: re-evaluating
the evidence. Ann Neurol. 2020;87(6):798–806.
72. Gangstad SW, Mikkelsen KB, Kidmose P, et al. Automatic sleep
stage classification based on subcutaneous EEG in patients with
epilepsy. Biomed Eng Online. 2019;18(1):106.
73. Casson AJ, Yates DC, Smith SJM, Duncan JS, Rodriguez-Villegas
E. Wearable electroencephalography. IEEE Eng Med Biol Mag.
74. Johansson D, Malmgren K, Alt MM. Wearable sensors for clin-
ical applications in epilepsy, Parkinson’s disease, and stroke: a
mixed-methods systematic review. J Neurol. 2018;265(8):1740–52.
75. Hoppe C, Feldmann M, Blachut B, Surges R, Elger CE,
Helmstaedter C. Novel techniques for automated seizure regis-
tration: patients’ wants and needs. Epilepsy Behav. 2015;52(Pt
76. Arends J, Thijs RD, Gutter T, et al. Multimodal nocturnal seizure
detection in a residential care setting: a long-term prospective trial.
Neurology. 2018;91(21):e2010–9.
77. Beniczky S, Ryvlin P. Standards for testing and clinical validation
of seizure detection devices. Epilepsia. 2018;59:9–13.
78. Cohen JF, Korevaar DA, Altman DG, et al. STARD 2015 guide-
lines for reporting diagnostic accuracy studies: explanation and
elaboration. BMJ Open. 2016;6(11):e012799.
79. Goldenholz DM, Tharayil J, Moss R, Myers E, Theodore WH.
Monte Carlo simulations of randomized clinical trials in epilepsy.
Ann Clin Transl Neurol. 2017;4(8):544–52.
How to cite this article: Duun-Henriksen J, Baud M,
Richardson MP, et al. A new era in
electroencephalographic monitoring? Subscalp devices
for ultra–long-term recordings. Epilepsia.
... We suggest a possible hybrid solution that consists in defining a final preictal interval to use in training and testing as the average of the clustering preictal intervals and the grid-search preictal intervals for the remaining seizures. Less invasive procedures, such as subscalp EEG, have been recently developed for ultra-long-term brain monitoring [70][71][72] . The method involves implanting subscalp (or subcutaneous) electrodes, for example, unilaterally behind the ear 70 . ...
... Less invasive procedures, such as subscalp EEG, have been recently developed for ultra-long-term brain monitoring [70][71][72] . The method involves implanting subscalp (or subcutaneous) electrodes, for example, unilaterally behind the ear 70 . Subscalp EEG and scalp EEG similarly capture background activity with closed and open eyes, showing a similar signal-to-noise ratio. ...
... Subscalp EEG and scalp EEG similarly capture background activity with closed and open eyes, showing a similar signal-to-noise ratio. Additionally, despite subscalp EEG may still be affected by artefacts such as muscle activity, these recordings present improved signal quality compared to scalp EEG, particularly during body movements that produce interferences due to the movement of wires [70][71][72] . Concomitantly, scalp EEG devices able to collect data from a few electrodes (placed, e.g. in the temporal lobe) are emerging as alternatives to conventional scalp EEG by providing patients with more comfort and usability 73 . ...
Full-text available
Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and nonlinear features were extracted from scalp electroencephalography (EEG) signals collected from 41 patients with drug-resistant epilepsy undergoing presurgical monitoring. Nonlinear dimensionality reduction was performed for each group of features and each of the 226 seizures. We applied different clustering methods in searching for preictal clusters located until 2 h before the seizure onset. We identified preictal patterns in 90% of patients and 51% of the visually inspected seizures. The preictal clusters manifested a seizure-specific profile with varying duration (22.9 ± 21.0 min) and starting time before seizure onset (47.6 ± 27.3 min). Searching for preictal patterns on the EEG trace using unsupervised methods showed that it is possible to identify seizure-specific preictal signatures for some patients and some seizures within the same patient.
... /fvets. . (35). For instance, Bacher et al. (36) recorded awake and ictal EEG epochs obtained from 21 human epilepsy patients with subscalp electrodes and validated them against simultaneous iEEG recordings. ...
... Several devices for recording subscalp EEG have been developed in recent years (Table 1) (35). One of these, the 24/7 EEG TM SubQ system from UNEEG Medical (Lillerød, Denmark) has been approved for use in humans in the EU and is undergoing evaluation and investigational trial in the US ( Figure 1C). ...
... A multichannel electrode lead implanted across the skull with a tunneling technique is how the subscalp device known as Minder from Epi-Minder (Melbourne, Australia) obtains subscalp EEG from both hemispheres (35,45). The fully implanted EEG acquisition device digitizes subscalp EEG captured from the implanted electrode and -similar to the UNEEG and Epibios devices -transmits these signals to a wearable data storage device that is inductively coupled to the fully implanted device and provides power to the implant and Bluetooth connection to a smartphone. ...
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Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.
... There currently is not a well-accepted standard for when a reported event is considered "correctly" reported when there is an electrographic or behavioral correlate. A clear definition used by the broader epilepsy community will be necessary as there is an increasing clinical use of other ambulatory monitoring devices such as chronic EEG, nocturnal monitoring devices, and wearable systems which would benefit from a standardized measure (Duun-Henriksen et al., 2020;Stirling et al., 2021;Armand Larsen et al., 2022;Brinkmann et al., 2021;Elger and Hoppe, 2018). ...
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Objectives: Recording electrographic and behavioral information during epileptic and other paroxysmal events is important during video EEG monitoring. This study was undertaken to measure the event capture rate of an ambulatory service operating across Australia using a shoulder-worn EEG device and telescopic pole-mounted camera. Methods: Neurologist reports were accessed retrospectively. Studies with confirmed events were identified and assessed for event capture by recording modality, whether events were reported or discovered, and wakefulness. Results: 6,265 studies were identified, of which 2,788 (44.50%) had events. A total of 15,691 events were captured, of which 77.89% were reported. The EEG-ECG amplifier was active for 99.83% of events. The patient was in view of the camera for 94.90% of events. 84.89% of studies had all events on camera, and 2.65% had zero events on camera (mean=93.66%, median=100.00%). 84.42% of events from wakefulness were reported, compared to 54.27% from sleep. Conclusion: Event capture was similar to previously reported rates from ambulatory studies, with higher capture rates on video. Most patients have all events captured on camera. Significance: Ambulatory monitoring is capable of high rates of event capture, and the use of wide-angle cameras allows for all events to be captured in the majority of studies.
... Measuring changes in seizure severity following stimulation parameter adjustments could close the loop for improving iterative therapies and enable clinicians to approach optimal device settings more rapidly. The emerging development of minimally invasive devices with more recording channels and a suite of on-board algorithms could leverage seizure severity recordings to self-optimize parameters without manual clinical input[41][42][43] . Administration of ASMs are also iterative, as dosage and new medications are altered based on patient reports of seizure frequency44 . ...
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Objective: More than one-third of the people with focal epilepsy do not achieve seizure freedom with medication, neuromodulation, or neurosurgery therapies. Palliative care with the goal of reducing epilepsy burden is an alternative for these patients. Minimizing severe seizures is essential for reducing morbidity. Existing seizure severity scales are qualitative and rely on patient reports, limiting our ability to rigorously track and intervene to curb severe seizures. The goal of this study is to develop and validate a quantitative metric for seizure severity. Methods: We retrospectively analyzed preictal and ictal intracranial-EEG (iEEG) recordings from 54 people with drug-resistant epilepsy undergoing pre-surgical evaluation. We developed a new metric that objectively combines seizure duration, spread, and semiology to quantify seizure severity. We calculated preictal iEEG network features and fit a linear mixed-effects model to quantify patient-specific associations between preictal networks and seizure severity. Results: We evaluated 256 seizures from 54 patients using the quantitative seizure severity score. Seizure severity was consistent with clinical seizure type. Medication taper strategy was associated with seizure severity (p = 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical seizure severity was associated with better post-surgical seizure outcome (U = 465, p = 0.042). A linear mixed-effects model with preictal network features as regressors and seizure severity as response revealed a group-level positive trend. In 12 out of 14 patients with multiple types of seizures, more severe seizures were preceded by more abnormal preictal networks. Significance: We present a quantitative metric for seizure severity that correlates with clinical and electrographic features. We found that the seizure severity score was associated with abnormal preictal networks. We propose this measure to holistically capture patient condition and guide incremental changes in therapy to improve patient outcome over time.
The diagnosis of seizures and epilepsy is primarily based on the history, but history-taking is fraught with difficulties and has serious limitations, which is one reason for the common misdiagnosis of seizures. EEG is a very useful tool, but routine EEG has poor sensitivity, and prolonged EEG-video monitoring, the gold-standard for diagnosis, is only useful for patients with frequent events. Smartphones are ubiquitous, and their videos are increasingly used as an extension of the history and a diagnostic tool. Stand-alone videos should be considered a diagnostic tool and treated as such, including with a Current Procedural Terminology (CPT) code, the American uniform nomenclature for medical procedures, which is used for billing and reimbursement.
This work proposes a variational mode decomposition (VMD) and binary grey wolf optimization (BGWO) based seizure classification framework. VMD decomposes the EEG signal into band-limited intrinsic mode function (BL-IMFs) non-recursively. The frequency domain, time domain, and information theory-based features are extracted from the BL-IMFs. Further, an optimal feature subset is selected using BGWO. Finally, the selected features were utilized for classification using six different supervised machine learning algorithms. The proposed framework has been validated experimentally by 58 test cases from the CHB-MIT scalp EEG and the Bonn University database. The proposed framework performance is quantified by average sensitivity, specificity, and accuracy. The selected features, along with Bayesian regularized shallow neural networks (BR-SNNs), resulted in maximum accuracy of 99.53 and 99.64 for 1 and 2 s epochs, respectively, for database 1. The proposed framework has achieved 99.79 and 99.84 accuracy for 1 and 2 s epochs, respectively, for database 2.
Forecasting seizure risk aims to detect pro‐ictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long‐term EEG recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure – during a vigilance‐controlled period – to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase‐locking value), estimated from daily vigilance‐controlled resting‐state recordings, could allow to distinguish interictal (no seizure) from preictal (seizure within the next 24hrs) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide best prediction performances (AUC≥0.7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier and skill Brier scores of 0.11 and 0.74, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices. Sharing link:
People with epilepsy can experience tremendous stress from the uncertainty of when a seizure will occur. Three factors deemed important because of their potential influence on seizure risk are exercise, medication adherence, and the menstrual cycle. A narrative review was conducted through PubMed searching for relevant articles on how seizure risk is modified by 1) exercise, 2) medication adherence, and 3) the menstrual cycle. There was no consensus about the impact of exercise on seizure risk. Studies about medication nonadherence suggested an increase in seizure risk, but there was not a sufficient amount of data for a definitive conclusion. Most studies about the menstrual cycle reported an increase in seizures connected to a specific aspect of the menstrual cycle. No definitive studies were available to quantify this impact precisely. All three triggers reviewed had gaps in the research available, making it not yet possible to definitively quantify a relationship to seizure risk. More quantitative prospective studies are needed to ascertain the extent to which these triggers modify seizure risk.
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Purpose of Review Despite advancements in medical and surgical therapies, many people with epilepsy continue to have seizures or have other comorbidities impacting their wellbeing. Self-management interventions have been shown to benefit people with other chronic diseases, and this review will examine the increasing evidence for a role of self-care as part of comprehensive, patient-centered epilepsy management. This will be a practical overview of self-care interventions for clinical practice. Recent Findings There are a variety of self-management support programs which provide structured educational or psychosocial therapy interventions for people with epilepsy. The Centers for Disease Control and Prevention (CDC) supports the Managing Epilepsy Well (MEW) Network, which has developed evidence-based programs with different focuses and goals. Progressive muscle relaxation training, mindfulness-based therapy, and structured physical activity have demonstrated decreased seizure frequency in randomized control trials. Other self-care interventions, such as healthcare software, devices, seizure dogs, and complementary medicine, are commonly used with limited evidence. Low carbohydrate diets remain an effective treatment option for some with epilepsy. Summary There are many interventions which can be used to promote health and to develop disease management skills for people with epilepsy, and it is important for providers to be aware of how they can support essential self-care for their patients.
Introduction The lack of ideal measurement of treatment efficacy is a well acknowledged problem in the epilepsy community, both in clinical care and clinical trials. Whilst still the current gold-standard, self-reported seizure frequency significantly underestimates the true number of seizures and does not account for any other at least equally important outcome parameters, such as neurodevelopment and cognition. With the rise of disease modifying treatments, the need for more reliable endpoints in practice and clinical trials becomes more pressing. In this paper we assembled an expert panel to discuss the nature of these needs, current limitations, and obstacles based on a survey amongst these experts who were queried about the most important issues regarding the use of electroencephalography (EEG) parameters as endpoints in clinical drug and device development. Methods A structured survey was sent to a group of experts in the design and conduct of epilepsy trials in adults and children. This was followed by a virtual in-person meeting discussing the results of the trial and identifying a list of most important issues. Results Six clinical trialists and 5 individuals from pharmaceutical companies returned the survey containing 14 questions, and 8 clinical trialists and 10 pharma-representatives attended the meeting. Three main issues were identified (1) lack of accuracy of seizure diaries due to nocturnal seizures, subtle motor seizures, impairment of consciousness and lack of awareness of the seizure by the patient (2) inter-rater variability of EEG assessment (3) lack of standardization regarding definition(s) of seizures (clinical and electrographic), EEG recording methods and EEG data management. Recommended solutions included (1) validation of EEG parameters as biomarkers and use of wearables (2) development of a manual that describes EEG rating criteria, protocol for validation by > 1 central reader and use of a resolution of disagreements reporting template (3) standardization of EEG recording, data management and reporting. Discussion & conclusion Current developments in research and technology seem promising to advance the use of EEG parameters as potential endpoints and offer partial solutions to the current needs. However, continuous, focused and collaborative efforts of all stakeholders (academia, industry and regulatory agencies) are needed to formulate guidelines, validate emerging technologies and approve them for use in trials. It is the intent of this opinion “position paper” to stimulate those efforts.
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Objective To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment. Methods Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES. Results We found significant correlations between quantitative surface EMG parameters and the duration of PGES ( p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%. Conclusions Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk. Classification of evidence This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.
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This narrative review provides a broad and comprehensive overview of the most important discoveries on the postictal state over the past decades as well as recent developments. After a description and definition of the postictal state, we discuss postictal sypmtoms, their clinical manifestations, and related findings. Moreover, pathophysiological advances are reviewed, followed by current treatment options.
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In patients with epilepsy, the potential to prevent seizure‐related injuries and to improve the unreliability of seizure self‐report have fostered the development and marketing of numerous seizure detection devices for home use. Understanding the requirements of users (patients and caregivers) is essential to improve adherence and mitigate barriers to the long‐term use of such devices. Here we reviewed the evidence on the needs and preferences of users and provided an overview of currently marketed devices for seizure detection (medically approved or with published evidence for their performance). We then compared devices with known needs. Seizure‐detection devices are expected to improve safety and clinical and self‐management, and to provide reassurance to users. Key factors affecting a device’s usability relate to its design (attractive appearance, low visibility, low intrusiveness), comfort of use, confidentiality of recorded data, and timely support from both technical and clinical ends. High detection sensitivity and low false alarm rates are paramount. Currently marketed devices are focused primarily on the recording of non–electroencephalography (EEG) signals associated with tonic‐clonic seizures, whereas the detection of focal seizures without major motor features remains a clear evidence gap. Moreover, there is paucity of evidence coming from real‐life settings. A joint effort of clinical and nonclinical experts, patients, and caregivers is required to ensure an optimal level of acceptability and usability, which are key aspects for a successful continuous monitoring aimed at seizure detection at home.
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Objective: To describe seizure outcomes in patients with medically refractory epilepsy who had evidence of bilateral mesial temporal lobe (MTL) seizure onsets and underwent MTL resection based on chronic ambulatory intracranial EEG (ICEEG) data from a direct brain-responsive neurostimulator (RNS) system. Methods: We retrospectively identified all patients at 17 epilepsy centers with MTL epilepsy who were treated with the RNS System using bilateral MTL leads, and in whom an MTL resection was subsequently performed. Presumed lateralization based on routine presurgical approaches was compared to lateralization determined by RNS System chronic ambulatory ICEEG recordings. The primary outcome was frequency of disabling seizures at last 3-month follow-up after MTL resection compared to seizure frequency 3 months before MTL resection. Results: We identified 157 patients treated with the RNS System with bilateral MTL leads due to presumed bitemporal epilepsy. Twenty-five patients (16%) subsequently had an MTL resection informed by chronic ambulatory ICEEG (mean = 42 months ICEEG); follow-up was available for 24 patients. After MTL resection, the median reduction in disabling seizures at last follow-up was 100% (mean: 94%; range: 50%-100%). Nine patients (38%) had exclusively unilateral electrographic seizures recorded by chronic ambulatory ICEEG and all were seizure-free at last follow-up after MTL resection; eight of nine continued RNS System treatment. Fifteen patients (62%) had bilateral MTL electrographic seizures, had an MTL resection on the more active side, continued RNS System treatment, and achieved a median clinical seizure reduction of 100% (mean: 90%; range: 50%-100%) at last follow-up, with eight of fifteen seizure-free. For those with more than 1 year of follow-up (N = 21), 15 patients (71%) were seizure-free during the most recent year, including all eight patients with unilateral onsets and 7 of 13 patients (54%) with bilateral onsets. Significance: Chronic ambulatory ICEEG data provide information about lateralization of MTL seizures and can identify additional patients who may benefit from MTL resection.
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Unrecognized seizures are a common problem in temporal lobe epilepsy potentially leading to undertreatment. Objective seizure counting using EEG home monitoring for prolonged periods with a minimally invasive device has not been feasible until now. We present a case in which a novel, subcutaneous EEG device was utilized to provide an objective seizure count. This information revealed unrecognized breakthrough seizures and informed treatment response, prompting treatment adjustment. The case illustrates how objective seizure counting in epilepsy using new devices can completely change diagnosis and management.
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Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.
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Background: The interplay between sleep structure and seizure probability has previously been studied using electroencephalography (EEG). Combining sleep assessment and detection of epileptic activity in ultralong-term EEG could potentially optimize seizure treatment and sleep quality of patients with epilepsy. However, the current gold standard polysomnography (PSG) limits sleep recording to a few nights. A novel subcutaneous device was developed to record ultralong-term EEG, and has been shown to measure events of clinical relevance for patients with epilepsy. We investigated whether subcutaneous EEG recordings can also be used to automatically assess the sleep architecture of epilepsy patients. Method: Four adult inpatients with probable or definite temporal lobe epilepsy were monitored simultaneously with long-term video scalp EEG (LTV EEG) and subcutaneous EEG. In total, 11 nights with concurrent recordings were obtained. The sleep EEG in the two modalities was scored independently by a trained expert according to the American Academy of Sleep Medicine (AASM) rules. By using the sleep stage labels from the LTV EEG as ground truth, an automatic sleep stage classifier based on 30 descriptive features computed from the subcutaneous EEG was trained and tested. Results: An average Cohen's kappa of [Formula: see text] was achieved using patient specific leave-one-night-out cross validation. When merging all sleep stages into a single class and thereby evaluating an awake-sleep classifier, we achieved a sensitivity of 94.8% and a specificity of 96.6%. Compared to manually labeled video-EEG, the model underestimated total sleep time and sleep efficiency by 8.6 and 1.8 min, respectively, and overestimated wakefulness after sleep onset by 13.6 min. Conclusion: This proof-of-concept study shows that it is possible to automatically sleep score patients with epilepsy based on two-channel subcutaneous EEG. The results are comparable with the methods currently used in clinical practice. In contrast to comparable studies with wearable EEG devices, several nights were recorded per patient, allowing for the training of patient specific algorithms that can account for the individual brain dynamics of each patient. Clinical trial registered at on 19 October 2016 (ID:NCT02946151).
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Objective: To explore the feasibility of home monitoring of epilepsy patients with a novel subcutaneous electroencephalography (EEG) device, including clinical implications, safety, and compliance via the first real-life test. Methods: We implanted a beta-version of the 24/7 EEG SubQ (UNEEG Medical A/S, Denmark) subcutaneously in nine participants with temporal lobe epilepsy. Data on seizures, adverse events, compliance in using the device, and use of antiepileptic drugs (AEDs) were collected. EEG was recorded for up to 3 months, and all EEG data were reviewed visually to identify electrographic seizures. These were descriptively compared to seizure counts and AED changes reported in diaries from the same period. Results: Four hundred ninety days of EEG and 338 electrographic seizures were collected. Eight participants completed at least 9 weeks of home monitoring, while one cancelled participation after 4 weeks due to postimplantation soreness. In total, 13 cases of device-related adverse events were registered, none of them serious. Recordings obtained from the device covered 73% of the time, on average (range 45%-91%). Descriptively, electrographic seizure counts were substantially different from diary seizure counts. We uncovered several cases of underreporting and revealed important information on AED response. Electrographic seizure counts revealed circadian distributions of seizures not visible from seizure diaries. Significance: The study shows that home monitoring for up to 3 months with a subcutaneous EEG device is feasible and well tolerated. No serious adverse device-related events were reported. An objective seizure count can be derived, which often differs substantially from self-reported seizure counts. Larger clinical trials quantifying the benefits of objective seizure counting should be a priority for future research as well as development of algorithms for automated review of data.
Insufficient sleep has been considered a seizure trigger in people with epilepsy for thousands of years. Here, we reviewed the evidence upon which this axiomatic association might be predicated. After evaluating evidence suggesting insufficient sleep as an EEG activator, we systematically reviewed the literature for longitudinal studies measuring the effect of insufficient sleep on seizure risk. Ultimately, published works designed to prove a causal relationship were of low level of evidence and disagreed with one another. Despite the idea's deep-seated nature, when considering causal evidence only, no conclusion can be made regarding the relationship between insufficient sleep and seizure risk. This article is protected by copyright. All rights reserved.
The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research effort has been put into the creation of “wearable EEG” which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this field, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal “tattoo” type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present.