| | Characteristics of generalised epileptiform activityAccepted 15 October 2008. Abstract ObjectiveTo study the age-related occurrence of specific features of generalised epileptiform activity (GEA), their correlation with EEG background activity (BA), and their internal correlation. Methods17,723 consecutive routine EEGs from 12,511 patients were annotated and categorised into a database. The first EEG containing GEA from all 325 patients with such activity were selected and categorised for GEA features. The BA was studied in multivariable fractional polynomial regression models including intervening variables. The GEA features were studied in similar models for age-dependency and internal correlation. ResultsHigh GEA-amplitude and low GEA-frequency correlated with BA slowing. The occurrence of ‘irregular spike/sharp slow wave’ pattern increased with age (p =0.003). Hyperventilation sensitivity was not age-related. There was no correlation between hyperventilation sensitivity and photoparoxysmal response. The age-related probability for specific GEA-types was established. ConclusionsHigh GEA-amplitude and low GEA-frequency correlate with BA slowing, indicating cerebral cortical dysfunction. Hyperventilation sensitivity and photoparoxysmal response independently increase the yield of EEG. There is no age-dependency for hyperventilation sensitivity showing that an upper age threshold for hyperventilation provocation is inappropriate. SignificanceThe results extend our understanding of GEA and help the electroencephalographer in weighing the various GEA components. 1. Introduction  The diagnosis of epilepsy is clinical, and good clinical information is essential for the assessment of the significance of epileptiform activity in EEG (Binnie and Stefan, 1999, Sam and So, 2001). The morphology of the epileptiform activity and the response to photic stimulation and activation procedures can, however, aid in determining the type of epilepsy and specific epilepsy syndromes, and thereby also be instrumental in the choice of antiepileptic medication and in evaluating the prognosis. Generalised epileptiform activity (GEA) can be seen in the EEG in idiopathic generalised epilepsies (IGEs), symptomatic/cryptogenic generalised epilepsies, and in localisation oriented epilepsies with fast generalisation of the epileptiform activity (Koutroumanidis and Smith, 2005). There are no infallible guidelines for EEG identification of these epilepsy types. Nevertheless some rules of thumb are widely accepted: polyspike wave discharges are typically found in epilepsies associated with myoclonus, and photoparoxysmal response (PPR) is most of all associated with juvenile myoclonic epilepsy (JME) (Markand, 2003, Smith, 2005). Generalised spike slow wave or polyspike slow wave discharges with abrupt bilateral synchronous onset and with a frequency of 3Hz or faster is a hallmark of IGE, while similar bursts, but with a frequency lower than 2.5Hz, are indicative for symptomatic generalised epilepsies. Focal or generalised slowing indicating cerebral non-epileptiform pathology is indicative of symptomatic epilepsy (Pillai and Sperling, 2006, Smit et al., 2006, Guerrini, 2006), although quantitative EEG studies have demonstrated some slowing of the background activity also in IGE (Pfurtscheller and da Silva, 1999, Clemens et al., 2000, Clemens, 2004). EEG-based studies of epileptiform activity are usually limited to clinically well defined epilepsy entities or syndromes. This study, in contrast, focuses on epileptiform activity itself and features linked to this activity from an electroencephalographer’s viewpoint. Including all EEGs with GEA implies a heterogeneous clinical group. Including numerous GEA features available from routine visual EEG examinations makes it possible to establish which of these features correlate with generalised brain pathology, operationally defined as slowing of the background activity. Such knowledge will contribute to a better understanding of the influence of such activity on general brain activity and also help the electroencephalographer to interpret the various components of the epileptiform activity. Examining GEA in a large and unselected population provides information that is inaccessible when studying selected clinical entities only. The aims of this study were: To define the GEA features that correlate with pathological background activity. To study the age-related occurrence of GEA features and GEA-related variables. To study the correlation between the various GEA features. 2. Methods 2.1. EEG recordings All routine EEGs recorded at Haukeland University Hospital from March 1st 2000 to December 31st 2005 were visually evaluated and described. This included 17,723 EEGs from 12,511 patients. Long-term registrations, and EEGs during general anesthesia, Wada tests, and tilt tests were not included in this study (Wada and Rasmussen, 1960, Low, 1983). Due to the lack of consensus regarding the assessment of rhythmic and periodic EEG patterns encountered in critically ill patients, 40 EEGs from such patients were excluded (Hirsch et al., 2005). Critically ill patients were defined as patients with an EEG referral, hospital admittance, or hospital discharge diagnosis for this stay including coma or acute anoxic brain damage. Suppression-burst complexes, triphasic waves and paroxysmal flattening can appear as epileptiform phenomena, but all 13 EEGs with such activity were excluded due to the controversy about the etiology of this activity (Raegrant et al., 1991, Husain et al., 1999). The first EEG containing GEA from each patient was selected for this study. A total of 366 EEGs marked in routine coding as having GEA were re-evaluated, and 325 EEGs, 181 from females and 144 from males, fulfilled all inclusion criteria (Table 1). These 325 EEGs were scored for the following GEA features: Waveform, bilateral synchronicity, regularity, frequency, amplitude of the sharp component, photoparoxysmal response (PPR), and hyperventilation sensitivity (HVS). The first EEG from all drug-free outpatients with no EEG pathology from the study period were included in the control group (N=3268). Haukeland University Hospital recruits patients from a population of about 500,000, and is the only provider of EEG within this area. The EEGs were described by one of 6 electroencephalographers. Three of these were certified, experienced electroencephalographers, and three were trainees under supervision. The age distribution at test time for the patients with GEA is shown in Fig. 1. The mean age was 18.2 years, median 14.8 years. The mean age for the control group was 24.2 years, median 19.6 years. Epilepsy was the referral diagnosis in 318 of the GEA patients (98%). For 300 patients (92%), a clinical diagnosis of epilepsy was made. One hundred and sixty-eight (56%) had an ICD10 diagnosis of generalised epilepsy, 105 (35%) had both diagnoses of generalised and localisation oriented epilepsy, 71 (24%) had only localisation oriented epilepsy diagnosis, whereas 61 (20%) had an unspecified epilepsy diagnosis. For 2 patients (1%), no clinical diagnosis was available. The referral diagnosis was epilepsy in 1885 controls (57%). Totally, 197 GEA patients (61%) received one or more drugs (Table 2), 117 (36%) were drug-free, and this information was missing in 11 (3%). Fifty EEGs with GEA (15%) were recorded after sleep-deprivation with the patient both awake and asleep. No patients received drugs to induce sedation. Standard duration for the EEG registration was 20, and 60min after sleep-deprivation. Hyperventilation provocation was performed in 199 (61%), and photic stimulation in 259 (80%) of the GEA patients. All EEGs were recorded on the digital EEG system Nervus®. The electrodes were placed according to the international 10–20 system (Fp1, Fp2, Fpz, F3, F4, F7, F8, Fz, A1, A2, T3, T4, T5, T6, C1, C2, Cz, P3, P4, Pz, O1, and O2). | | |  | Drug type | N | % | |
|---|
| Antiepileptics | 138 | 42 | | | Respiratory system | 20 | 6 | | | Psycholeptics | 17 | 5 | | | Psychoanaleptics | 15 | 5 | | | Cardiovascular system | 7 | 2 | | | Analgesics | 6 | 2 | | | Antidiabetics | 4 | 1 | | | Antithrombotics | 4 | 1 | | | Antiinflammatory | 3 | 1 | | | Sex hormones or modulators of the genital system | 3 | 1 | | | Systemic hormonal preparations, excl. sex hormones and insulin | 3 | 1 | | | Antiinfectives | 2 | 1 | | | Other | 25 | 8 | | | | |
2.3. EEG interpretation The interpretation was divided into 3 main sections: Alpha rhythm (AR), general background activity (GBA), and EEG events. AR was defined as the dominant posterior rhythm with frequency 8–13Hz that was blocked or attenuated by eye opening (Chatrian et al., 1983). Alpha variant rhythm was defined as with AR characteristics but with frequency outside the 8–13Hz alpha band (Chatrian et al., 1983). AR and alpha variant rhythm were analysed together and is referred to as AR. AR was evaluated for frequency, amplitude, reactivity, and asymmetry. Frequency and amplitude were measured for the occipital leads in a monopolar average montage in an EEG segment where AR was distinctly appearing. They were evaluated as a range with a minimum and maximum value. The maximum value was used for AR-frequency. AR-amplitude was defined as the mean of the upper and lower value. Background activity was defined as any EEG activity representing the setting in which a given normal or abnormal pattern appeared and from which such a pattern was distinguished (Chatrian et al., 1983). AR was described separately. The remaining continuous general activities were defined as the GBA. Focal, asymmetric, and intermittent activities were not defined as part of the background activity, but described as EEG events. The GBA was evaluated in a monopolar average montage without signs of drowsiness. To assure alertness the patients were instructed to open their eyes during parts of the test. If drowsy, the patients were alerted with an audible signal (knocking), or asked to perform calculations and repeat series of digits. GBA was described as one or more ranges of frequencies and amplitudes. The minimum value was used for GBA-frequency as this value was assumed to be most relevant for EEG pathology (Gloor et al., 1977). GBA-amplitude was defined as the mean of the upper and lower value. EEG events were defined as any part of the EEG that the electroencephalographer described separately from the frequency and amplitude measurement of the AR and GBA. An example of each type of EEG event was manually marked in the EEG editor with start and stop time. These events were automatically picked up by the description module, and the electroencephalographer then classified them according to the American Standards for Testing and Materials (ASTM, 1994). The ASTM categories were divided hierarchically into 4 main branches: ‘epileptiform pathology’, ‘non-epileptiform pathology’, ‘normal findings/variants’, and ‘extra-cerebral activity’ (Westmoreland and Klass, 1990). Pathological EEGs were defined as having at least one EEG event from one of the first two branches. 2.4. GEA The localisation of the EEG events was given by clicking the traces where the activity occurred. The electrodes were divided into 5 regions; frontal, temporal, central, parietal, and occipital. Each event was automatically classified as general or focal. If three or more regions were affected at both sides and with no, mild or moderate asymmetry, the event was classified as general. All other events were classified as focal. All EEGs with one or more general events categorised as ‘epileptiform pathology’ were classified as having GEA, also if there were additional focal epileptiform activity (FEA) in the same EEG. 2.4.1. GEA-types, GEA features, and GEA-related variables GEA was categorised into 5 types; ‘Spike/sharp slow wave’, ‘polyspike slow wave’, ‘polyspike’, ‘irregular spike/sharp wave’, and ‘hypsarrhythmia’ (Table 1). ‘Spike/sharp slow wave’ was defined as a pattern of a spike followed by a slow wave or a sharp wave followed by a slow wave. ‘Polyspike slow wave’ was defined as a sequence of multiple spikes and a slow wave. ‘Polyspike’ was defined as a multiple spike complex; several spikes occurring in a sequence without slow waves. ‘Hypsarrhythmia’ was defined as a pattern consisting of high voltage arrhythmic slow waves interspersed with spikes, without consistent synchrony between the two sides of the head or different areas on the same side (Noachtar et al., 1999). Irregular spike/sharp wave’ was defined as paroxysms of spike or sharp waves with or without slow waves and with an irregular frequency of about 3–5Hz (Markand, 2003). If the GEA changed from one category to another during a paroxysm, the GEA-type at the start of the paroxysm was chosen for this study. The GEA and patient were further described by 11 features (Table 1): age (years), gender (female/male), contact-type (outpatient/inpatient), medicated (no/yes), FEA (no/yes), post-ictal (no/yes), ictal (no/yes), current brain-related disease (no/yes), HVS (no/yes), PPR (no/yes), regular bilateral synchronous (RBS) GEA (no/yes), GEA-frequency (Hz), and GEA-amplitude (μV). GEA-frequency was measured only if the GEA appeared rhythmically. The GEA-amplitude was defined as the amplitude of the sharp part of the paroxysm measured in the monopolar montage. GEA-amplitude and frequency were measured during the first part of a paroxysm. The EEG was by us assessed as HVS and PPR if the occurrence of GEA was more than doubled during hyperventilation and flicker provocation, respectively (Waltz et al., 1992). The EEGs were assessed as ‘post-ictal’ if a generalised convulsive seizure had occurred less than 24h before the recording and as ‘ictal’ if a generalised convulsive seizure was observed during the EEG registration. Current brain-related disease was defined as a chronic brain-related ICD10-diagnose set within one month after the EEG or a non-chronic ICD10-diagnose set between one month before and one month after the EEG. 2.4.2. Statistics Continuous and binomial dependent variables were analysed using multiple linear and logistic fractional polynomial regression models, respectively (Royston and Sauerbrei, 2005). Age-related changes in the occurrence of GEA-types were analysed using multinomial logistic regression analysis. Pairwise correlations between AR and GBA frequencies and amplitudes, as well as between HVS and PPR, were quantified using Spearman’s correlation coefficient. The BA variables (AR-amplitude, AR-frequency, GBA-amplitude, and GBA-frequency) were subsequently included as dependent variables and analysed for three groups: All EEGs, EEGs with GEA, and EEGs with RBS GEA. By including the GEA features as independent variables, we tested their individual correlation with the BA variables. GEA features were subsequently included as dependent variables and analysed for two groups: EEGs with GEA and EEGs with RBS GEA. These tests were first performed with only age as independent variable. Then the other GEA features were included as independent variables. As HVS and PPR were tested in only 59% and 77% of GEA patients they were not included as independent variables. GEA-frequency was included as an independent variable only for the RBS GEA group. ‘Medicated’ and ‘post-ictal’ were always first included as independent variables. However, these variables were omitted if not significant to avoid excluding EEGs with missing ‘medicated’ and ‘post-ictal’ values. Index variables were included to test for differences between GEA subtypes. All GEA subtypes were compared to the most common subtype, ‘spike/sharp slow wave’. In some situations ‘irregular sharp waves’, ‘polyspikes’, ‘hypsarrhythmia’, ‘ictal’ and ‘contact-type’ were excluded as covariates due to collinearity. Only significant covariates (p<0.05) were included. The square root transformation of the AR-amplitude and the natural log (ln) transformation of the GBA-amplitude and GBA-frequency were used to achieve near normality for the error terms. Data were back transformed for graphical display. No transformation was necessary for AR-frequency. Results were considered significant with p-values <0.05. Stata 9.2 was used for the analyses. 3. Results 3.1. GEA and background activity All EEG background variables were significantly affected in EEGs with GEA compared to control EEGs, as illustrated in Fig. 2 and Table 4 (see also Online Appendix A). High GEA-amplitude and low GEA-frequency correlated with BA slowing (Table 4 and Online Appendix A). | | | | Study groups | Independent variable | Effect variable for EEG background activity | Significance probability (p-value) | |
|---|
| GEA patients (N=325) and controls (N=2368) | GEA present | ↑ AR amp | < 0.001 | | | ↓ AR freq | < 0.001 | | | ↑ GBA amp | < 0.001 | | | ↓ GBA freq | 0.038 | | | Male gender | ↓ AR freq | 0.002 | | | ↓ GBA freq | 0.002 | | | | | | GEA patients (N=325) | ‘Polyspike’ | ↑ AR amp | 0.037 | | | ‘Hypsarrhythmia’ | ↑ GBA amp | 0.001 | | | ↓ GBA freq | < 0.001 | | | ‘Ictal’ | ↓ AR amp | 0.040 | | | ↑ AR freq | 0.042 | | | ↓ GBA freq | 0.004 | | | ‘Post-ictal’ | ↓ GBA freq | 0.004 | | | ↑ GEA amp | ↑ AR amp | 0.003 | | | ↑ GBA amp | 0.006 | | | Male gender | ↓ GBA freq | 0.009 | | | CBRDEE | ↑ GBA amp | 0.018 | | | ↓ GBA freq | 0.004 | | | | | | Patients with RBS GEA (N=57) | ↓ GEA freq | ↓ GBA freq | 0.005 | | | ‘Post-ictal’ | ↓ AR freq | 0.004 | | | Male gender | ↓ AR freq | 0.033 | | | | |
AR-amplitude, AR-frequency, GBA-amplitude, and GBA-frequency were all pairwise correlated (p<0.001) (Fig. 3). Lower AR-frequency correlated with higher AR-amplitude, higher GBA-amplitude, and lower GBA-frequency. AR could be identified in 236 of the 325 patients with GEA (73%), in 47 of the 57 patients with RBS GEA (82%), and in 2726 of the 3268 controls (83%). GBA could be identified in 321 of the patients with GEA (99%), in all patients with RBS GEA, and in all controls. The patients in which GBA could not be identified had continuous GEA. 4. Discussion All EEG BA parameters were clearly affected in EEGs with GEA as compared to controls: AR and GBA frequencies were lower and amplitudes were higher. Using an EEG-centric approach we have identified GEA-related variables correlating with a change in the EEG BA. These correlations were independent of intervening factors such as age, medication, and ‘current brain-related disease except epilepsy’, as these factors were all included as explanatory variables in the multivariate statistical models used. This is in line with a previous quantitative EEG study demonstrating EEG BA slowing in patients with IGE (Clemens et al., 2000). GEA could induce the BA slowing, be a result of the BA slowing, or there could be a common factor causing both the GBA and the BA slowing. A causal connection is not established, but the first and the last possibility seem most plausible. GEA-amplitude and lower GEA-frequency correlated with slowing of the EEG BA. This is in accordance with the view that low-frequency GEA indicates symptomatic epilepsy (Markand, 2003, Smith, 2005, Pillai and Sperling, 2006). The amplitude of scalp recorded epileptiform spikes and ictal rhythms depends on multiple factors such as the size and localisation of the cortical area involved, orientation of the electrical field, synchrony, and the amplitude of the cortical signals (Ebersole and Hawes-Ebersole, 2007). The decrease of GEA-amplitude with age could in part be due to less synchrony related to a higher proportion of ‘irregular spike/sharp wave’. However, higher GEA-amplitudes remained correlated with GBA slowing also after adjusting for all these factors. Our findings suggest that higher GEA-amplitude leads to a change in brain function independent of other GEA characteristics. HVS correlated with RBS GEA and with ‘spike/sharp slow wave’ activity. As many as 59% of the RBS GEA subgroup had HVS, in contrast to only 14% of GEA patients without RBS GEA. Hyperventilation provocation is regarded as more effective in generalised than in focal epilepsies (Dalby, 1969, Blume et al., 1973, Holmes et al., 2004). Our findings show that HVS is a common phenomenon, especially in EEGs with typical IGE features. HVS appearing as 3Hz bilateral synchronous GEA is almost obligate in absence epilepsy in children (Binnie and Stefan, 1999). Due to the close relation between HVS and this age-related epilepsy, an inverse age-correlation would be expected for HVS. However, we found no HVS age-dependency; neither in the total group of patients with GEA, nor in the RBS GEA subgroup. Therefore no upper age-limit for hyperventilation provocation should be applied. Yenjun et al. (2003) found no HVS difference between adolescent and adult onset IGE, in agreement with our findings. PPR was, in agreement with previous studies, age-dependent with a maximum probability at 16 years of age, and with female predominance (Quirk et al., 1995, Stephani et al., 2004, Fisher et al., 2005, Pillai and Sperling, 2006). PPR was more common in patients with no drug treatment and in the GEA-types ‘polyspike slow wave’ and ‘polyspikes’. This supports the clinical experience of a good drug treatment effect on PPR, and fits with the high proportion of PPR in epilepsies associated with myoclonus, especially in JME (Shiraishi et al., 2001, Guerrini and Genton, 2004, Covanis et al., 2004, Benbadis, 2005, Covanis, 2005, Mendez and Brenner, 2006, Trenite, 2006). The probabilities for HVS and PPR did not correlate, and these provocation procedures should therefore be complementary to increase the yield of the EEG registration. Photic stimulation was of less value in older adults than in children and adolescents. Adult onset IGE is more common than previously acknowledged, and with EEG features resembling those of adolescent IGE (Yenjun et al., 2003, Marini et al., 2003, Nicolson et al., 2004a). The probability for RBS GEA, the EEG hallmark of IGE, was age-dependent in our study, with a maximum at 12 years. The GEA-amplitude decreased with increasing age, and the GEA-type ‘irregular spike/sharp wave’ increased compared to the ‘spike/sharp slow wave’ which was more prominent at young age. These changes in GEA can account for the observed age-dependent decrease in RBS GEA. In contrast, the GEA-frequency was not age-dependent. This may indicate that the underlying epileptic “generator” is the same at different ages, but with a different expression in the scalp EEG due to changes in the neuronal network. All patients with GEA in their EEGs were included in this study. The term “generalised” has been a somewhat imprecise expression (Wolf, 2005). However, the ILAE Classification Core Group recently decided to retain the terms “focal” and “generalised”, but with the understanding that the former does not necessarily imply that the epileptogenic region is limited to a small circumscribed area, nor does the latter imply that the entire brain is involved in initialisation of the epileptogenic process (Engel, 2006). GEA often has a frontal amplitude dominance, and does not necessarily show in the most posterior electrodes. This is reflected in our definition of GEA. Patients with GEA could have IGE, symptomatic/cryptogenic generalised epilepsies, localisation oriented epilepsies with fast generalisation of the epileptiform activity, as well as the combination of localisation oriented and generalised epilepsy (Koutroumanidis et al., 1999, Nicolson et al., 2004b, Hirsch, 2005, Jeha et al., 2006). The results from this EEG study should therefore not be directly applied on generalised epilepsy. Major new findings in this study are the correlation between high GEA-amplitude and slowing of the background activity, the accurate age-related probability for specific GEA-types, the lack of correlation between HVS and PPR, and no HVS age-dependency. HVS and PPR are complimentary in increasing the EEG yield, and no upper age-limit for HVS should be applied. This knowledge extends our understanding of GEA and helps the electroencephalographer in weighing the various GEA components. Acknowledgements We are indebted to Dr. Bjørn Karlsen and Dr. Håvard Skeidsvoll, Section of Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, Bergen, Norway, and Tom Eichele, Department of Biological and Medical Psychology, University of Bergen, Norway for discussion and comments. Appendix A. Supplementary data Appendix A. AR and GBA functions Appendix B. GEA functions Appendix C. HVS and PPR probability functions Appendix D. 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a Section of Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, Jonas Liesvei 65, N-5021 Bergen, Norway b Department of Neurology, Haukeland University Hospital, Bergen, Norway c Centre for Clinical Research, Haukeland University Hospital, Bergen, Norway d Department of Public Health and Primary Health Care, University of Bergen, Bergen, Norway e Department of Clinical Medicine, University of Bergen, Bergen, Norway  Corresponding author. Tel.: +47 55975000; fax: +47 55975164.
PII: S1388-2457(08)01021-3 doi:10.1016/j.clinph.2008.10.149 © 2008 International Federation of Clinical Neurophysiology. Published by Elsevier Inc. All rights reserved. | |
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