Epilepsy is a chronic neurological disorder that affects about 50 million people worldwide. The most visible symptom is the appearance of seizures, an abnormal electrical signal that occurs inside the brain. The electroencephalogram (EEG) is a non-invasive brain recording technique that detects seizures accurately. In this post, we will show you the characteristics of the signals associated with seizures and the procedures used in the diagnosis of epilepsy.
What is an EEG and what does it measure?
Electroencephalogram (EEG) is a neurophysiological technique that records the electrical signals produced when groups of neurons communicate with each other by using multiple EEG electrodes placed over the scalp surface. Such electrical activity is amplified and displayed as wave signatures on a computer screen.
One of the main advantages of using EEG technologies is its excellent temporal resolution, which allows capturing neurocognitive processes that occur within tens to hundreds of milliseconds.
For a long time, neuroscientific research has consistently demonstrated how EEG signals relate to cognition, which has resulted in well-accepted theories and empirical data supporting such brain function and neural signal relationship (Berger, 1929; Gevins and Schaffer, 1980).
Some interesting facts that can be unraveled with EEG include the specific brain areas that are engaged during a particular mental activity, and the identification of several types of neurological disorders, like epilepsy syndrome (e.g. Adebimpe et al., 2016). Indeed, one of the most valuable capabilities of EEG is its precision in identifying brain waves abnormalities, providing a unique insight into a patient's level of impairment.
There are many different types of EEG systems and techniques. In a conventional EEG, an electrode montage is attached to an elastic cap according to the 10/20 system landmarks (Jasper, 1958). Today, we can find EEG systems that also provide a fixed electrode layout optimized for specific applications.
Regarding the type of sensors, there are gel, water-based, and dry EEG electrodes (small metal discs). Differences between them depend on the conductive substance required to reduce skin cell impedances and improve signal quality. While in the last century, gel-based EEG electrodes are widely used in both research and medical settings, today we can find in the market more sophisticated systems based on water and EEG sensors that are dry.
These innovative EEG systems have become an alternative to gel, providing more comfort to the user and a simplified setup to the researcher while preserving signal quality (Kam et al., 2019). See a performance comparison of gel, water (semi-dry), and dry EEG systems in the context of brain-computer interfaces here (Schwartz et al., 2020).
Left image: Dry EEG machine with fixed layout | Right image: Water-based EEG system with flexible layout
How can EEG aid in Epilepsy?
A primary role of EEG is to help clinicians and neurologists to establish an accurate diagnosis of epilepsy (Alebesque et al., 2017; NICE, 2012).
Since the activity in the brain linked to epileptic seizures entails unusual patterns of electrical discharges (e.g. epileptiform signals), an EEG test can assist in seizure detection and classification.
Therefore, high specificity is another useful feature of EEG (i.e. false-positive detection only in 0.5-3.5% of cases, WHO, 2012), which it may be clinically relevant in cases wherein the manifestation of the seizure is not clear (e.g. non-convulsive epileptic state) or a differential diagnosis is needed.
EEG can also aid in the medical advice and management of epilepsy. Often, the frequency and period between seizures (i.e. interictal) are unpredictable making it difficult to control the epileptic status of the patient. Here, the role of EEG is to predict or evaluate the risk of seizure relapses and recurrence. This is particularly useful in guiding the medical treatment and measuring its effectiveness.
Seizures occur due to brain wave abnormalities, and the EEG is a great tool capable of providing unique information about the landscape of a patient's epilepsy and predicting and monitoring an ongoing seizure activity.
Furthermore, EEG monitoring is essential to control ongoing seizure activity that may be subtle or absent especially during medication intake or after withdrawal (Berg and Shinnar, 1994; Smith, 2005).
One important consideration when using EEG is the need to control for sources of errors that could affect the correct management of patients with seizures. Generally, potential sources of error include poor technical quality of equipment and unskilled technicians. A minimal technical qualification that meets the standard of ABRET-certified laboratories is highly required (e.g. American Clinical Neurophysiology Society Guidelines).
For instance, a trustworthy EEG device must exhibit a high enough signal‐to‐noise‐ratio to distinguish an epileptic spike from background noise (Iwasaki et al.,2005). Once the quality of the EEG is confirmed, the EEG test results should always be interpreted by a specialized neurologist with a thorough knowledge of seizures and epileptic syndromes. A limited experience or untrained eye in reading EEG is one of the most common reasons for an incorrect diagnosis (Bendabis, 2010).
There are three main applications in which EEG is highly valuable for epilepsy:
- Medical diagnosis and management: A primary role of EEG is to help clinicians and neurologists to establish an accurate diagnosis (Alebesque et al., 2017; NICE, 2012).
- Research: As a research tool, EEG technique is extremely useful in the study of how seizures begin, spread, and stop. Questions like why certain types of seizures emerge at specific ages or only in response to distinct events, the comorbidity with cognitive decline, and how anti-seizures medications affect brain cells to block seizures are nowadays topics of great scientific research (Helmstaedter and Witt, 2017; Holmes, 2013; Rogawski, Löscher, and Rho, 2016)
- Early detection: EEG devices that are capable of alerting the patient or caregiver of the occurrence of seizures several minutes in advance.
What is detected in an EEG test?
In patients suspected of suffering from epilepsy, performing an EEG test is advised after the first seizure that is not known to be caused by alcohol/drug withdrawal or other medical conditions (e.g. encephalopathy; Pohlmann-Eden et al., 2006). Once the diagnosis is set, an EEG test will provide accurate information about ongoing brain electrical activity while the test is carried out. It will inform the doctor about when a seizure begins, when and how the seizure spreads, and when the seizure stops.
EEG-detected seizures can be characterized according to delimited periods of time. For instance, the term ictal refers to the EEG activity recorded during a seizure event whereas interictal and postictal indicates the EEG activity captured between and after seizures respectively (Britton, Frey and Hopp, 2016). In the diagnosis of epilepsy and seizure onset localization, these recordings entail different wave morphologies resulting highly informative.
1. Ictal EEG patterns
For clinical reasons, seizures can be classified according to one of these two categories: focal-partial seizures and generalized seizures (Blume, 2010; Sharbrough, 1993). The main difference between these types of seizures relies on how they begin.
- Focal seizure: If the seizure activity arises from a very specific area of the brain, then it is identified as a focal seizure. Focal seizures generally originate in subcortical structures and are limited to one hemisphere. These types of seizures last about one or two minutes and are typically observed in Temporal Lobe Epilepsy.
- General: Conversely, when the epileptic seizure is shown as a widespread electrical discharge involving both brain hemispheres at the same time, then it may be indicative of a generalized seizure type.
Usually, when an epileptic seizure arises, abnormal EEG signatures will show up as rapid spiking waves (between 30-80ms) or sharp waves (70-200ms) that may or may not be followed by slow waves. At least five different patterns of epileptic waveforms can be distinguished during an ictal EEG (Fischer, 2014):
- Rhythmical frequencies that evolve in the delta (0–3Hz/s), theta (4–7Hz/s) or alpha (8–12Hz/s) bands with different degrees of sharpness.
- Rhythmic fast-spiking seizures (40-50Hz/s) are common in a seizure with focal onset (e.g. hippocampus origin).
- Spike-wave complexes are generalized discharges varying from 1.5 to 2.5Hz, with a sharp peak duration of 20 to 70 milliseconds; such types of epileptiform activity can be observed during the course of focal or generalized tonic-clonic seizures.
- Electrodecremental patterns reflect temporal lobe epilepsy seizures and are characterized by a general flattening of brain rhythms at the start of a seizure.
- Clinical seizure without changes in the EEG activity. The main assumption in this case is a seizure origin that is far from scalp electrodes.
2. Interictal and postictal EEG patterns
Interictal EEG provides indicators of abnormal activity between seizures intervals. During this type of EEG test, patients with seizure disorder epilepsy history often show a specific pattern of pathological activity referred to as interictal epileptiform discharges (IEDs), which is clearly distinguished from the activity observed during the seizure itself.
However, we know from research that such abnormal brain activity may not be so evident in all epileptic patients (Pillai and Sperling, 2006). This means that aside from the seizure interval itself, the remaining time the EEG activity may appear entirely normal.
A recovery period called postictal interval follows the seizure event (Abood and Bandyopadhyay, 2019). During this phase, the brain recovers from the trauma of the epileptic seizure. Usually, postictal EEG recordings are characterized by attenuation or slowing in slow-wave activity (delta-theta waves). The observed average time for EEG to return to baseline is approximately 2h with a maximum of 8h in adults (Arkilo et al., 2013) which will partly depend on the severity, brain location and type of the seizure episode. Typical symptoms include migraine, drowsiness, confusion, and other altered states of consciousness like memory loss.
What is the EEG procedure for epilepsy diagnosis?
Diagnosing epilepsy with EEG procedures helps complete the clinical picture of a patient's seizures. Commonly, the test will be performed by an EEG technician who will record and monitor brain activity while the patient is awake or asleep.
Prior to the EEG test, the patient is prepared for the recording, a necessary step that may last between 20 and 25 minutes. The EEG room should be quiet, well-conditioned, and often dimly lit.
During the EEG test, the technician proceeds with the recording while asking the patient to report any seizure symptoms that may experience. Importantly, the EEG procedure is painless, comfortable and generally, it does not entail any risk.
Usually, one of the following types of EEG test can be adopted, each entailing different procedures:
- Routine EEG: the EEG test is carried out in a specialized clinic or in the hospital and it should last less than 1.5 hours.
- Sleep EEG: may be considered when a routine EEG does not provide enough information. The procedures are the same as in normal awake EEG but the patient is sleeping during the test.
- Prolonged EEG: requires the patient to stay in an epilepsy monitoring unit for continuous EEG monitoring for 1 or 2 hours or inpatient over several days. A video camera may use to capture the onset and characteristics of seizures simultaneously with the EEG registering.
- Ambulatory EEG: when appropriate, the test may be done in the outpatient setting or in the patient's home over a number of hours or during a few days.
It could be the case that seizures itself may not show up when the EEG is being recorded. When necessary, epileptiform activity will be provoked with standard seizure activation procedures, which will increase the chance of capturing seizure-like activity or even seizures. Typical activation procedures include fast eyes opening and closing for several times, photic stimulation (e.g. staring at flashing lights), breathing deeply or rapidly (hyperventilation), and sleep deprivation (staying up the entire night before the EEG test).
Wearable devices for epilepsy seizure detection
The unpredictable nature of epileptic seizures causes distress and is highly disabling for both patients and caregivers. Today, the technology for seizure detection is already being commercialized, making it more accessible to the general public and 24/7 monitoring out of medical environments.
Seizure-detection devices are capable of warning the patient or caregiver of the early occurrence of seizures events. Importantly, this early detection is not equivalent to seizure prediction. It refers to the earliest time interval when abnormal EEG activity can be detected before the onset of clinical symptoms. As you can imagine, this technology has become tremendously essential for patients as early detection and actuation in case of epileptic attack can prevent head injury due to a fall or even death.
1. Key features of epilepsy seizure-detection devices:
According to current recommendations (Bruno et al., 2018, 2020; Simblett et al., 2019; Van de Vel et al., 2019), there are at least five key factors that commercial epilepsy biometric devices must meet to ensure users´needs:
This is probably the most important. A seizure-detection device can be considered reliable if first, it achieves a detection level greater than 90% and second, it proves a false alarm level of less than 2 per week. These two metrics are more than critical as low performance will only have detrimental effects on patients and caregivers’ lives.
Safety and privacy
EEG seizure-detection systems are completely secure devices. In addition to this, an important requirement is to ensure the confidentiality of the recorded brain data, so they must be subject to stringent privacy requirements and provided to users.
Self-management of seizures is another critical patient need that must supplement seizure detection technologies. This implies being able to support users´ regulation of rest and daily activities.
Design (e.g., discreteness and low intrusiveness) and comfort are key factors that may impact a device´s suitability ensuring an optimal level of acceptability and usability.
Proof of Evidence
Additionally, marketed seizure-detection devices should include scientific evidence of their reliability and proven efficacy to meet the needs of users.
2. Wearable seizure-detection devices currently available in the market
Ultimately, the choice of a particular seizure-detection device will depend on the type of seizure, personal circumstances, lifestyle, and the accepted risk level.
There are a number of wearables for early seizure detection available in the market (Bruno et al., 2020). Most of these devices are based on termed mobile health (mHealth) approaches, which are focused primarily on the recording of biometric signals associated with motor manifestations seizures (e.g. tonic‐clonic seizures). To date, evidence of accurate devices for the detection of seizures without major motor features has not yet been provided.
Specific instances of marketed devices for seizure detection, with either published evidence on device performance or medical device approval are listed in the table below.
EEG recordings have proven helpful to identify abnormal neural activity linked to different types of seizures with relatively high accuracy. Since EEG is a safe and painless technique, it is one of the first recommended tests for seizures detection in patients with an epileptic disorder diagnosis. A key contribution of EEG is to bring greater specificity in the diagnosis of types of epilepsy, thus aiding clinicians in effectively managing treatment of the disease.
To meet the overall needs of patients and their caregivers, wearable technology provides early seizure detection solutions in patient´s real‐life conditions. However, a major challenge of currently marketed devices will be to improve seizure detection accuracy and increase their versatility in self-management of the disease.
Cristina Gil-López, Ph.D. - Researcher at the Psychology and Neurocognition institute (LPNC), University of Grenoble (France); Associated Research Scientist at Polytechnic University of Valencia (Spain)
Cristina Gil-López is an experienced neuroscientist with a large professional career (> 7 years) investigating human cognition. In 2016, she obtained her doctorate in Cognitive Neuroscience from the University of Valencia. Her research career within the academy includes her thesis research on the neural bases of Math Cognition in Bilinguals carried out at the Basque Center for Cognition, Brain and Language (Spain), her published studies on visual word recognition developed in the University of Valencia (Spain) and her current research on visual perception and emotion at Grenoble University (France). Within the industry, she has led three outstanding research projects at Johnson & Johnson and collaborated in three other projects working in partnership with Takasago, Bunge & Loders Croklaan, and Bitbrain companies. Cristina is passionate about the human brain, its neurodevelopment and functioning, as well as dedicated to promoting high-quality research on brain cognitive enhancement through neurotechnology.
Linkedin, Scholar, homepage, https://cristinagillopez.com/
- Adebimpe, A., Aarabi, A., Bourel-Ponchel, E., Mahmoudzadeh, M., & Wallois, F. (2016). EEG Resting State Functional Connectivity Analysis in Children with Benign Epilepsy with Centrotemporal Spikes. Frontiers in neuroscience, 10, 143. https://doi.org/10.3389/fnins.2016.00143
- Arkilo D, Wang S, Thiele EA. Time interval required for return to baseline of the background rhythm on electroencephalogram after recorded electrographic seizures. Epilepsy Res. 2013 Sep;106(1-2):288-91. doi: 10.1016/j.eplepsyres.2013.04.007. Epub 2013 May 14. PMID: 23684125.
- A. Viloria Alebesque, A. López Bravo, E. Bellosta Diago, S. Santos Lasaosa, J.A. Mauri Llerda. Utilidad del electroencefalograma en el manejo de la epilepsia en el Servicio de Urgencias. Neurología, Volume 35, Issue 4, May 2020, Pages 238-244.https://doi.org/10.1016/j.nrl.2017.08.008
- Selim R Benbadis (2010) The tragedy of over-read EEGs and wrong diagnoses of epilepsy. Expert Review of Neurotherapeutics, 10:3, 343-346.https://doi.org/10.1586/ern.09.157
- Berg A.T., Shinnar S. Relapse following discontinuation of antiepileptic drugs: a meta-analysis. Neurology. 1994;44:601-608. 10.1212/wnl.44.4.601
- Berger, H. Über das Elektrenkephalogramm des Menschen. Archiv f. Psychiatrie 87, 527–570 (1929). https://doi.org/10.1007/BF01797193
- Blume W. T. (2010). Focal and generalized: both here and there. Epilepsy currents, 10(5), 115–117. https://doi.org/10.1111/j.1535-7511.2010.01375.x
- Claassen J, Mayer SA, Kowalski RG, Emerson RG, Hirsch LJ. Detection of electrographic seizures with continuous EEG monitoring in critically ill patients. Neurology 2004; 62: 1743– 1748 . 10.1212/01.wnl.0000125184.88797.62
- Berg AT, Shinnar S. Relapse following discontinuation of antiepileptic drugs: a meta-analysis. Neurology.1994;44:601–8. DOI: 10.1212/wnl.44.4.601
- Bruno, E., Viana, P. F., Sperling, M. R., & Richardson, M. P. (Accepted/In press). Seizure detection at home: Do devices on the market match the needs of people living with epilepsy and their caregivers? Epilepsia. https://doi.org/10.1111/epi.16521
- Bruno E, Simblett S, Lang A, Biondi A, Odoi C, Schulze‐Bonhage A, et al. Wearable technology in epilepsy: the views of patients, caregivers, and healthcare professionals. Epilepsy Behav. 2018;85:141–9. https://doi.org/10.1111/epi.16521
- Falco-Walter, J.J., Scheffer, I.E., and Fisher, R.S. (2018) The new definition and classification of seizures and epilepsy. Epilepsy Res. 139: 73-79. DOI: 10.1016/j.eplepsyres.2017.11.015
- Fisher, R. S., Scharfman, H. E., & deCurtis, M. (2014). How can we identify ictal and interictal abnormal activity?. Advances in experimental medicine and biology, 813, 3–23. https://doi.org/10.1007/978-94-017-8914-1_1
- Gevins AS, Schaffer RE. A critical review of electroencephalographic (EEG) correlates of higher cortical functions. Crit Rev Bioeng. 1980;4(2):113-64. PMID: 7004774.
- Gregory RP, Oates T, Merry RT. Electroencephalogram epileptiform abnormalities in candidates for aircrew training. Electroencephalography Clin Neurophysiol. 1993 Jan;86(1):75-7. doi: 10.1016/0013-4694(93)90069-8. PMID: 7678394.
- Helmstaedter C, Witt JA. Epilepsy and cognition - A bidirectional relationship? Seizure. 2017 Jul;49:83-89. doi: 10.1016/j.seizure.2017.02.017. Epub 2017 Mar 1. PMID: 28284559
- Holmes G. L. (2013). EEG abnormalities as a biomarker for cognitive comorbidities in pharmacoresistant epilepsy. Epilepsia, 54 Suppl 2(0 2), 60–62. https://doi.org/10.1111/epi.12186
- Klein, H. (2019, August 26). Temporal Lobe Epilepsy (TLE). Epilepsy Foundation. https://www.epilepsy.com/learn/types-epilepsy-syndromes/temporal-lobe-epilepsy-aka-tle
- Iwasaki M,Pestana E,Burgess RC,Luders HO,Shamoto H,Nakasato N( 2005): Detection of epileptiform activity by human interpreters: Blinded comparison between electroencephalography and magnetoencephalography. Epilepsia 46: 59–68. https://doi.org/10.1111/j.0013-9580.2005.21104.x
- Jouny, C. C., Franaszczuk, P. J., & Bergey, G. K. (2011). Improving early seizure detection. Epilepsy & behavior : E&B, 22 Suppl 1(Suppl 1), S44–S48. https://doi.org/10.1016/j.yebeh.2011.08.029
- Manjari Tripathi and Man Mohan Mehendiratta (2014). Role of EEG in Epilepsy, Epilepsy Topics, Mark D. Holmes, IntechOpen, DOI: 10.5772/57430. Available from: https://www.intechopen.com/books/epilepsy-topics/role-of-eeg-in-epilepsy
- Pillai J, Sperling MR. Interictal EEG and the diagnosis of epilepsy. Epilepsia. 2006 ;47 Suppl 1:14-22. DOI: https://doi.org/10.1111/j.1528-1167.2006.00654.x
- Pohlmann-Eden, B., Beghi, E., Camfield, C., & Camfield, P. (2006). The first seizure and its management in adults and children. BMJ (Clinical research ed.), 332(7537), 339–342. https://doi.org/10.1136/bmj.332.7537.339
- Rogawski, M. A., Löscher, W., & Rho, J. M. (2016). Mechanisms of Action of Antiseizure Drugs and the Ketogenic Diet. Cold Spring Harbor perspectives in medicine, 6(5), a022780. https://doi.org/10.1101/cshperspect.a022780
- Shafi, M. M., Westover, M. B., Cole, A. J., Kilbride, R. D., Hoch, D. B., & Cash, S. S. (2012). Absence of early epileptiform abnormalities predicts lack of seizures on continuous EEG. Neurology, 79(17), 1796–1801. https://doi.org/10.1212/WNL.0b013e3182703fbc
- Sharbrough FW. Scalp recorded ictal patterns in focal epilepsy. J Clin Neurophysiol 1993; 10:262-267. DOI: 10.1097/00004691-199307000-00002
- Sim I. Mobile Devices and Health. N Engl J Med. 2019 Sep 5;381(10):956-968. doi: 10.1056/NEJMra1806949. PMID: 31483966.
- Simblett SK, Bruno E, Siddi S, Matcham F, Giuliano L, López JH, et al. Patient perspectives on the acceptability of mHealth technology for remote measurement and management of epilepsy: a qualitative analysis. Epilepsy Behav. 2019;97:123–9. DOI: 10.1016/j.yebeh.2019.05.035
- Smith, S J M. “EEG in the diagnosis, classification, and management of patients with epilepsy.” Journal of neurology, neurosurgery, and psychiatry vol. 76 Suppl 2,Suppl 2 (2005): ii2-7. doi:10.1136/jnnp.2005.069245
- Sundaram M, Sadler RM, Young GB, Pillay N. EEG in Epilepsy: Current Perspectives. Can. J. Neurol. Sci. 1999; 26: 255-262
- National Institute for Clinical Evidence. NICE guidelines: the diagnosis and management of the epilepsies in adults and children in primary and secondary care. London: NICE, 2004
- Van de Vel A, Smets K, Wouters K, Ceulemans B. Automated non‐EEG based seizure detection: do users have a say? Epilepsy Behav. 2016;62:121–8. DOI: 10.1016/j.yebeh.2016.06.029
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