|Year : 2018 | Volume
| Issue : 2 | Page : 64-69
Electroencephalography abnormalities in generalized epilepsy and their predictors: A multicenter experience
Lukman Femi Owolabi1, Shehu Sale2, Shakirah Desola Owolabi2, Aisha Nalado1, Muhammad Umar2, Aminu Abdullahi Taura2
1 Department of Medicine, Aminu Kano Teaching Hospital, Bayero University, Kano, Nigeria
2 Department of Psychiatry, Aminu Kano Teaching Hospital, Bayero University, Kano, Nigeria
|Date of Web Publication||13-Mar-2018|
Dr. Lukman Femi Owolabi
Department of Medicine, Aminu Kano Teaching Hospital, Bayero University, PMB 3452, Kano
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: In spite of the overwhelming significance of knowledge of basic elements of electroencephalography (EEG) in its application to the diagnostic workup and the management of patients with suspected or already established generalized epilepsy (GE), there is a dearth of data on the pattern and utility of clinical variables that can independently determine EEG abnormalities in GE. Objective: The study was designed to evaluate the frequency and pattern of EEG abnormality as well as assess the utility of clinical variables in predicting the likelihood of an abnormal EEG in GE. Methods: It was a cross-sectional study involving the analysis of EEGs of consecutive patients with clinical diagnosis of idiopathic GE from three centers over a 7-year period. Information on sociodemographic and seizure variables was obtained. The International Federation of Societies for Electroencephalography and Clinical Neurophysiology definition of interictal epileptiform discharges (interictal epileptiform activity [IEA]) was adopted in the study. Results: A total of 403 patients comprising 242 (60%) males and 161 (40%) females with clinical diagnosis of GE had EEG. Their age ranged between 2 weeks and 70 years, with a median age of 21 years and an interquartile age of 26 years. Two hundred and thirty-seven (58.8%) and 213 (52.9%) patients had abnormal EEG and IEA, respectively. Before adjustment for confounders, female gender (P = 0.0001), pediatric age group (P = 0.0388), duration of epilepsy of 1–4 years (P = 0.01387), uncontrolled seizure (P = 0.0060), and seizure frequency (P = 0.0001) were significantly associated with the presence of abnormal EEG. However, age, female gender, poor seizure control, and seizure frequencies were the independent predictors of EEG abnormality. Conclusion: The study showed that about 58% of patients with GE patients had abnormal EEG. Age, poor seizure control, and high frequency of seizure were independent predictors of the presence of EEG abnormality.
| Abstract in French|| |
Contexte: Malgré l'importance considérable de la onnaissance des éléments fondamentaux de l'électroencéphalographie (EEG) dans son application au bilan diagnostique et dans la prise en charge des patients atteints d'épilepsie généralisée (GE) suspectée ou déjà établie, on manque de données sur le profil. et l'utilité des variables cliniques qui peuvent déterminer indépendamment les anomalies EEG dans GE. Objectif: L'étude a été conçue pour évaluer la fréquence et le profil de l'anomalie EEG ainsi que pour évaluer l'utilité des variables cliniques dans la prédiction de la probabilité d'un EEG anormal dans GE. Méthodes: Il s'agissait d'une étude transversale impliquant l'analyse des EEG de patients consécutifs avec un diagnostic clinique de GE idiopathique à partir de trois centres sur une période de 7 ans. Des informations sur les variables sociodémographiques et de saisie ont été obtenues. La Fédération internationale des sociétés d'électroencéphalographie et de dé fi nition clinique de neurophysiologie des décharges épileptiformes intercritiques (activité épileptiforme inter-ictale [IEA]) a été adoptée dans l'étude. Résultats: Un total de 403 patients comprenant 242 (60%) mâles et 161 (40%) femelles avec le diagnostic clinique de GE avait EEG. Leur âge variait entre 2 semaines et 70 ans, avec un âge médian de 21 ans et un âge interquartile de 26 ans. Deux cent trente-sept (58,8%) et 213 (52,9%) patients avaient des EEG et IEA anormaux, respectivement. Avant ajustement pour les facteurs confondants, sexe féminin (P = 0,0001), groupe d'âge pédiatrique (P = 0,0388), durée de l'épilepsie de 1 à 4 ans (P = 0,01387), crise incontrôlée (P = 0,0060) et fréquence des crises (P = 0,0001) étaient significativement associés à la présence d'EEG anormal. Cependant, l'âge, le sexe féminin, le faible contrôle des crises épileptiques et la fréquence des crises étaient les prédicteurs indépendants de l'anomalie de l'EEG. Conclusion: L'étude a montré qu'environ 58% des patients avec GE avaient un EEG anormal. L'âge, le mauvais contrôle des crises et la fréquence élevée des crises étaient des prédicteurs indépendants de la présence d'une anomalie de l'EEG.
Mots-clés: Anomalies, électroencéphalographie, épilepsie, généralisée
Keywords: Abnormalities, electroencephalography, epilepsy, generalized
|How to cite this article:|
Owolabi LF, Sale S, Owolabi SD, Nalado A, Umar M, Taura AA. Electroencephalography abnormalities in generalized epilepsy and their predictors: A multicenter experience. Ann Afr Med 2018;17:64-9
|How to cite this URL:|
Owolabi LF, Sale S, Owolabi SD, Nalado A, Umar M, Taura AA. Electroencephalography abnormalities in generalized epilepsy and their predictors: A multicenter experience. Ann Afr Med [serial online] 2018 [cited 2019 Oct 21];17:64-9. Available from: http://www.annalsafrmed.org/text.asp?2018/17/2/64/227173
| Introduction|| |
Electroencephalography (EEG) is an important tool in the evaluation of patients with epilepsy. It is a requisite for the diagnosis of certain electroclinical syndromes, and it also provides vital clues about background EEG and epileptiform discharges in epilepsy and other conditions., Besides, EEG can detect subclinical seizures as a cause of coma. Although diagnosis of epilepsy is mainly clinical, EEG helps in establishing the diagnosis of epilepsy, distinguish epileptic seizures from other nonepileptic events, localizes seizure origin, and helps with the classification of epilepsy and epilepsy syndromes. Apart from its use as a guide in the selection and discontinuation of antiepileptic medication (antiepileptic drug [AED]), EEG is also an invaluable tool in prognostication of epilepsy. Thus, even with the tremendous advances in neurodiagnostic procedures, EEG remains a relevant diagnostic tool in epilepsy.
Nonetheless, in individuals living with epilepsy, intermittent EEG changes including interictal epileptiform discharges can be infrequent and may not appear during the relatively brief period of routine EEG recording. Epileptiform activity in EEG record is specific, but not sensitive, for diagnosis of epilepsy as the cause of an episodic event or transient loss of consciousness in an individual with clinical diagnosis of epilepsy. The sensitivity of EEG in patients with epilepsy ranges between 25% and 56%. A study conducted in Nigeria showed that the occurrence of interictal EEG abnormality was about 57% in all categories of epilepsy.
In spite of the overwhelming significance of knowledge of basic elements of the practice of EEG in its application to the diagnostic workup and management of patients living with epilepsy, there is a dearth of data on the utility of clinical variable that can independently determine EEG abnormalities in generalized epilepsy (GE), hence the need for this study.
This study was designed to evaluate the frequency EEG abnormality and assess the utility of clinical variables in predicting the likelihood of an abnormal EEG in GE.
| Methods|| |
In this prospective study, 403 consecutive patients with clinical diagnosis of epilepsy had EEGs over a 7-year period at three diagnostic centers in Kano. These centers attract EEG requests from all the states of North Western Nigeria.
The patients included in the study were those with clinical diagnosis of GE, while patients with the other types of epilepsy and those whose seizure types were uncertain were excluded from participating in the study. Definition and classification of GE was in accordance with the classification of International League Against Epilepsy. Information obtained from the patients included age at onset of epilepsy, classification of the epileptic syndrome, medications, last episode of seizure, whether or not the patient was on AED, type of AED, and family history of epilepsy.
Routine, scalp, awake, and interictal EEG recording was performed on all the patients following a detailed explanation on the procedure. Cap electrodes were applied with collodion, according to the 10–20 system, with linked mandibular references. The data were obtained on a 16-channel Grass and Medtronic EEG machines. Common average referential, longitudinal, and transverse bipolar montages were used in all examinations. EEG recording was filtered with 1 Hz high-pass, 35 Hz low-pass, and 60 Hz notch filters at a paper speed of 30 mm/s. During the recordings, the patients were instructed to remain relaxed yet alert.
During eyes-open recordings, patients were told to fixate their gaze on a particular point on the wall and try to inhibit ocular movements. The behavioral state of the patient during the EEG recording was noted and spontaneous drowsiness and sleep encouraged. Standard activation procedure of hyperventilation was performed in each case unless hyperventilation was contraindicated or the patient did not cooperate. Routine EEG recording was performed for 30–40 min, and subsequently 5 min of hyperventilation was carried out. Photic stimulation was conducted in a few of the patients. EEG was done within 1 month of last seizure and mostly after a single event.
In most cases, the recordings from patients were independently interpreted by two of the investigators with adequate skills of EEG interpretation, and inter-rater agreement was determined. During interpretation, the following factors were documented; age at time of EEG, waking and sleeping background, the occurrence or not of baseline interictal epileptiform activity (IEA), location of focal IEA when identified, and the response of IEA to activation procedures. The International Federation of Societies for Electroencephalography and Clinical Neurophysiology definition of IEA was adopted in the study.
The EEG was examined for specific epileptiform abnormalities such as interictal spike or sharp wave. The abnormal electroencephalographic activity was classified as generalized or focal abnormalities. The presence and topography of bursts of slow waves and epileptiform paroxysms were also evaluated. IEA was classified as spike wave, sharp wave, and polyspike.
Analysis was done using SPSS (Version 16.0. Chicago, SPSS Inc.). Descriptive statistics including means and standard deviation for continuous variables and proportions for categorical variables were computed. Chi-square test was used for comparison of categorical variables and differences in means of parametric numerical variables were assessed using two-sample t-tests. Multiple logistic regression model was used and the covariates were adjusted for each independent (regression) variable to find independent predictors of EEG abnormality. P value was set at 0.05.
| Results|| |
A total of 403 patients comprising 242 (60%) males and 161 (40%) females with clinical diagnosis of GE had EEG. Their age ranged between 2 weeks and 70 years with a median age of 21 years and an interquartile age of 26 years. [Table 1] summarizes the age and gender distribution of the study participants. Majority (55%) of the patients have had epilepsy for <1 year. Ninety-three (23.1%) patients were on AED, the most common ones being carbamazepine (46%), sodium valproate (43%), phenytoin (28%), and levetiracetam (17%) singly and in combination.
|Table 1: Distribution of electroencephalography abnormalities across age groups*|
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Two hundred and thirty-seven (58.8%) patients had abnormal EEG, while EEG was normal in 166 (41.2%) patients. However, two hundred and thirteen (52.9%) patients had IEA.
Considering the predominant morphology of epileptiform discharges, 168 (70.9%) had sharp and slow waves, 36 (11.02%) had spike and slow, 12 (5.2%) had polyspikes. Disorganized background and/or asymmetric background were found in 19 (8%) patients. [Table 1] summarizes the overall distribution of EEG abnormalities in the study. However, some patients had more than one type of epileptiform discharges. Hyperventilation induced epileptiform activities in 17% of the patients, while only 6% of the patients had photo-induced epileptiform activity. [Figure 1] and [Figure 2] show morphology of epileptic discharges in some of the patients.
|Figure 1: Paroxysms of generalized sharp- and slow-wave complexes in a patient with generalized epilepsy|
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Before adjustment for confounders, female gender (P = 0.0001), pediatric age group (P = 0.0388), duration of epilepsy of 1–4 years (P = 0.01387), uncontrolled seizure (P = 0.0060), and seizure frequency (P = 0.0001) were significantly associated with the presence of abnormal EEG. However, age, female gender, poor seizure control, and frequency of seizure were independent predictors of EEG abnormality [Table 2].
|Table 2: Relationship between common variables and abnormal electroencephalography (unadjusted) and independent predictors of abnormal electroencephalography (adjusted)|
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| Discussion|| |
Our study found a fairly low frequency of abnormal EEG (58.8%) and IEA (52.9%) in the study population. Previous reports showed that GE will have a first abnormal and characteristic EEG in 54%–81% of the patients , even though it could be as low as 19%., It is, however, worthy of note that the result in the current study was derived from the first EEG ever conducted on the patients. There are chances that the procedure could have yielded a higher figure if repeated over and over in the study population. Marsan and Zivin, in a study that involved 308 people living with epilepsy in which serial EEGs were conducted, found abnormal EEG in 56% of the patients on first EEG examination. However, in subsequent recordings, 26% had abnormal records and only 18% of the patients had consistently abnormal recordings. Similarly, Salinsky et al. reviewed 1201 multiple EEG records obtained from 429 adult patients and showed that 50% of the patients had IEA on the first record, 84% by the third EEG, and 92% by the fourth EEG. Nonetheless, sleep during EEG recording, sleep deprivation before EEG recording, hyperventilation, and photic stimulation  are other techniques that have been shown to increase the yield of recording the IEA in patients with epilepsy. The combination of wake and sleep EEG records improves the yield to about 80% in patients with clinically diagnosed epilepsy. Only hyperventilation and photic stimulations were used in our study; hyperventilation induced epileptiform activities in 17% of the patients. This finding, which was in conformity to report from studies elsewhere, was most commonly found in patients with absence seizure in the current study. However, in agreement with the report of Danesi and Oni, a small proportion (6%) had photo-induced IEA.
The IEAs found in the current study were consistent with the established EEG signatures of GE which are often bisynchronous and symmetric, and generalized spike-wave complex, focal, irregular discharges are occasionally seen. Like already established, the other EEG features observed included polyspikes, polyspike-wave discharges, and occipital intermittent rhythmic delta activity. The mechanism underlying generation of epileptic discharges on scalp EEG is not fully understood. Nevertheless, the bilaterally synchronous and symmetric spike and wave discharges on the scalp would suggest a deep-seated generator of these discharges in GE. In support of a deep seated generator hypothesis was an early concept of centrencephalic epilepsy proposed by Morison and Dempsey  which was corroborated by the finding of bisynchronous spike and wave epileptic activity obtained on stimulation of intralaminar nucleus of the thalamus of a cat at three cycles/s. Nonetheless, there are other studies that have also demonstrated the role of cerebral cortex and brainstem in the generation of epileptic activity in GE.
We attempted to identify clinical variables that could be of predictive value for abnormal EEG findings on first EEG. Age was found to be an independent predictor of the presence of EEG abnormality in the current study. This finding is consistent with an age-dependent effect of a linear trend for the detection of higher rates of epileptiform patterns with increasing age found in studies elsewhere.,
The relationship between frequency of seizure, poor seizure control, and EEG abnormality found in our study could somewhat give credence to the saying that seizure begets seizure. There have been suggestions that seizures in some way modify brain function and that each seizure increases the risk for further EEG abnormality and seizures. However, this phenomenon is not fully understood, and the reports in support of it have been flawed because of inappropriate study design. Hauser and Lee evaluated the risk for seizure recurrence following a first unprovoked seizure in a cohort of patients identified at their first unprovoked seizure. They found out that individuals with low risk for a seizure recurrence demonstrate a significant increase in risk for seizure recurrence with increasing number of seizures. Sills, in an experimental study, showed that brief epileptic seizures can markedly reduce gamma-aminobutyric acid (GABA)-mediated monosynaptic inhibitory postsynaptic currents (IPSCs) and GABA currents and alter GABAA-receptor subunit protein levels. He ascribed the reductions in IPSCs and GABA currents to altered receptor subunit composition, with reduced gamma-2 levels causing reduced GABAA-receptor sensitivity to GABA. Seizure-induced reductions in GABA-mediated inhibition could exacerbate epilepsy.
Idiopathic generalized epilepsies (IGEs) are often easy to diagnose. However, as with any other medical conditions, they are sometimes difficult and challenging. Although not a substitute for a clinical examination, EEG is an integral part of the diagnostic process in epilepsies and should not be underrated in establishing accurate diagnosis of IGE.
Our study is not without the usual methodological drawbacks of an interictal EEG study; nonetheless, given the sample size and the careful attention paid to selection criteria, we are strongly of the opinion that the study is sufficiently powered to give the results with clinical and statistical validity.
| Conclusion|| |
This study showed that about 58% of patients with IGE patients had abnormal EEG. Among those with epileptiform activities, generalized sharp and wave complexes were the most common findings. Age, poor seizure control, and high frequency of seizure were independent predictors of the presence of EEG abnormality.
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Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2]