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A ST, Asranna A, Kenchaiah R, Mundlamuri RC, Lg V, Sinha S. Benign epileptiform variants in EEG: A comprehensive study of 3000 patients. Seizure 2024; 120:157-164. [PMID: 39003934 DOI: 10.1016/j.seizure.2024.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/29/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND The analysis of EEG demands expertise and keen observation to distinguish epileptiform discharges from benign epileptiform variants (BEVs), a frequent source of erroneous interpretation. The prevalence of BEVs varies based on geographical, racial, and ethnic characteristics. However, most data on BEVs originates from Western populations, and additional studies on different cohorts would enrich the existing literature. METHODS We reviewed EEGs from our institutional database to study the prevalence of benign epileptiform variants and analyzed their frequency, topography, and other characteristics. Additionally, we investigated the co-existence of epileptiform discharges with BEVs. RESULTS We identified 296 patients with BEVs after reviewing 3000 EEGs (9.9%). The most common BEV was small sharp spikes (SSS), observed in 114 patients (3.8%). Wicket waves, 6 Hz spike and slow wave, 14 and 6 Hz positive bursts, and Rhythmic Temporal Theta of Drowsiness (RTTD) were identified in 67 (2.2%), 40 (1.3%), 39 (1.3%), and 35 (1.16%) patients, respectively and one patient with Subclinical Rhythmic EEG Discharges in Adults (SREDA). Additionally, we observed the co-existence of epileptiform discharges with BEVs, most commonly with SSS (27.8%). CONCLUSIONS The present study is a large study with 3000 EEGs to describe the BEV characteristics. BEVs were seen in 9.9% of patients, BSSS being the most common. There were minor differences in frequency, gender or age distribution compared to existing literature. We demonstrated the co-existence of epileptiform discharges. Morphological characteristics remain the cornerstone in recognising BEVs. EEG readers need to be aware of features of BEVs to avoid wrongly interpretation.
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Affiliation(s)
- Sangeeth T A
- Departments of Neurology, National Institute of Mental Health and Neurosciences, Hosur Road, Bangalore, Karnataka, India
| | - Ajay Asranna
- Departments of Neurology, National Institute of Mental Health and Neurosciences, Hosur Road, Bangalore, Karnataka, India
| | - Raghavandra Kenchaiah
- Departments of Neurology, National Institute of Mental Health and Neurosciences, Hosur Road, Bangalore, Karnataka, India
| | - Ravindranadh C Mundlamuri
- Departments of Neurology, National Institute of Mental Health and Neurosciences, Hosur Road, Bangalore, Karnataka, India
| | - Viswanathan Lg
- Departments of Neurology, National Institute of Mental Health and Neurosciences, Hosur Road, Bangalore, Karnataka, India
| | - Sanjib Sinha
- Departments of Neurology, National Institute of Mental Health and Neurosciences, Hosur Road, Bangalore, Karnataka, India
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Gélisse P, Benbadis SR, Crespel A, Tatum WO. Overcoming traps and pitfalls leading to misinterpretation of normal EEG variants and variation of the background activity. J Neurol 2024; 271:3869-3878. [PMID: 38761192 PMCID: PMC11233371 DOI: 10.1007/s00415-024-12440-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 05/20/2024]
Abstract
Normal EEG variants, especially the epileptiform variants, can be challenging to interpret because they often have sharp contours and may be confused with "epileptic" interictal activities. However, they can be recognized by the fact that "most spikes or sharp wave discharges of clinical import are followed by a slow wave or a series of slow deflections" (Maulsby, 1971). If there is no wave after the spike, electroencephalographers should be suspicious of artifacts and normal EEG variants. Most normal EEG variants display a single rhythm with the same frequency within the pattern and the morphology remains stable throughout the entire EEG recording with repetition of the same pattern. In case of doubt or difficulties with a standard EEG, it is recommended to undergo an EEG that includes sleep stages with or without sleep deprivation. Finally, epileptiform is an ambiguous term corresponding to an electroencephalographic trait. Epileptiform does not imply a pathological condition, including epilepsy. The clinical context remains the most paramount in the diagnosis of epilepsy. In this article, we propose a set of rules and guidelines to identify normal EEG variants in EEG tracings and normal variation of the background activity. It is not easy to accurately assign a specific/precise name to all EEG activity, but with an orderly approach to EEG that involves using a set of criteria, nonepileptic activity can be identified.
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Affiliation(s)
- Philippe Gélisse
- Epilepsy Unit, Hôpital Gui de Chauliac, 80 Avenue Fliche, 34295, Montpellier Cedex 05, France.
- Research Unit (URCMA: Unité de Recherchef sur les Comportements et Mouvements Anormaux), INSERM, U661, Montpellier, France.
| | - Selim R Benbadis
- Department of Neurology, University of South Florida, Tampa, FL, USA
| | - Arielle Crespel
- Epilepsy Unit, Hôpital Gui de Chauliac, 80 Avenue Fliche, 34295, Montpellier Cedex 05, France
- Research Unit (URCMA: Unité de Recherchef sur les Comportements et Mouvements Anormaux), INSERM, U661, Montpellier, France
| | - William O Tatum
- Department of Neurology, Mayo Clinic College of Medicine and Health Sciences, Jacksonville, FL, USA
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Gélisse P, Crespel A. Asymmetrical/unilateral ocular artifacts on EEG. Neurophysiol Clin 2024; 54:102942. [PMID: 38382140 DOI: 10.1016/j.neucli.2023.102942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 12/21/2023] [Accepted: 12/21/2023] [Indexed: 02/23/2024] Open
Affiliation(s)
- Philippe Gélisse
- Epilepsy Unit, Gui de Chauliac Hospital, Montpellier, France; Research Unit (URCMA: Unité de Recherche sur les Comportements et Mouvements Anormaux), INSERM, U661, Montpellier, France.
| | - Arielle Crespel
- Epilepsy Unit, Gui de Chauliac Hospital, Montpellier, France; Research Unit (URCMA: Unité de Recherche sur les Comportements et Mouvements Anormaux), INSERM, U661, Montpellier, France
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Macorig G, Crespel A, Nilo A, Tang NPL, Gigli GL, Gélisse P. Can epilepsy affect normal EEG variants? A comparative study between subjects with and without epilepsy. Neurophysiol Clin 2024; 54:102935. [PMID: 38394943 DOI: 10.1016/j.neucli.2023.102935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 02/25/2024] Open
Abstract
OBJECTIVES To compare the prevalence of benign EEG variants (BEVs) between epileptic and non-epileptic subjects. METHODS A prospective, observational EEG study of 1,163 consecutive patients, using the 10-20 international system with systematically two additional anterior/inferior temporal electrodes. The video-EEG monitoring duration was between 24 h and eight days. RESULTS We identified 917 (78.9%) epileptic patients (mean age: 33.42 ± 15.5 years; females: 53.4%) and 246 (21.2%) non-epileptic patients (mean age: 35.6 ± 18.75 years; females: 54.9%). Despite a shorter mean duration of the EEG recordings, the prevalence of BEVs was higher in non-epileptic vs. epileptic patients (73.2% vs. 57.8%, p = 0.000011). This statistical difference was confirmed for lambda waves (23.6% in the non-epilepsy group vs. 14.8% in the epilepsy group, p = 0.001), POSTs (50.8% vs. 32.5%, p < 0.000001), wicket spikes (20.3% vs. 13.6%, p = 0.009) in particular in NREM and REM sleep, and 14- and 6-Hz positive bursts (13% vs. 7.1% p = 0.003). Mu rhythm was observed at the same frequency in both groups (21.1% in the non-epilepsy group vs. 22.7% in the epilepsy group). There was no difference between the two groups for rarer rhythms, such as rhythmic mid-temporal theta burst of drowsiness, small sharp spikes, and midline theta rhythm. CONCLUSIONS There was no increase in any of the BEVs in the epilepsy group. On the contrary, BEVs were more frequent and diversified in the non-epilepsy group. Epilepsy may negatively affect the occurrence of the most common BEVs, with the exception of the mu rhythm, which is present in about one-fifth of the population with or without epilepsy.
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Affiliation(s)
- Greta Macorig
- Gui de Chauliac Hospital, Epilepsy Unit, Montpellier, France; San Giovanni di Dio Hospital, Neurology Unit, Gorizia, Italy
| | - Arielle Crespel
- Gui de Chauliac Hospital, Epilepsy Unit, Montpellier, France; Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Recherche sur les Comportements et Mouvements Anormaux, Montpellier, France
| | - Annacarmen Nilo
- Gui de Chauliac Hospital, Epilepsy Unit, Montpellier, France; S. Maria della Misericordia University Hospital, Clinical Neurology Unit, Udine, Italy
| | | | | | - Philippe Gélisse
- Gui de Chauliac Hospital, Epilepsy Unit, Montpellier, France; Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Recherche sur les Comportements et Mouvements Anormaux, Montpellier, France.
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Greenblatt AS, Beniczky S, Nascimento FA. Pitfalls in scalp EEG: Current obstacles and future directions. Epilepsy Behav 2023; 149:109500. [PMID: 37931388 DOI: 10.1016/j.yebeh.2023.109500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023]
Abstract
Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.
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Affiliation(s)
- Adam S Greenblatt
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund and Aarhus University Hospital, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Fábio A Nascimento
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
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Amin U, Nascimento FA, Karakis I, Schomer D, Benbadis SR. Normal variants and artifacts: Importance in EEG interpretation. Epileptic Disord 2023; 25:591-648. [PMID: 36938895 DOI: 10.1002/epd2.20040] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 03/21/2023]
Abstract
Overinterpretation of EEG is an important contributor to the misdiagnosis of epilepsy. For the EEG to have a high diagnostic value and high specificity, it is critical to recognize waveforms that can be mistaken for abnormal patterns. This article describes artifacts, normal rhythms, and normal patterns that are prone to being misinterpreted as abnormal. Artifacts are potentials generated outside the brain. They are divided into physiologic and extraphysiologic. Physiologic artifacts arise from the body and include EMG, eyes, various movements, EKG, pulse, and sweat. Some physiologic artifacts can be useful for interpretation such as EMG and eye movements. Extraphysiologic artifacts arise from outside the body, and in turn can be divided into the environments (electrodes, equipment, and cellphones) and devices within the body (pacemakers and neurostimulators). Normal rhythms can be divided into awake patterns (alpha rhythm and its variants, mu rhythm, lambda waves, posterior slow waves of youth, HV-induced slowing, photic driving, and photomyogenic response) and sleep patterns (POSTS, vertex waves, spindles, K complexes, sleep-related hypersynchrony, and frontal arousal rhythm). Breach can affect both awake and sleep rhythms. Normal variants or variants of uncertain clinical significance include variants that may have been considered abnormal in the early days of EEG but are now considered normal. These include wicket spikes and wicket rhythms (the most common normal pattern overread as epileptiform), small sharp spikes (aka benign epileptiform transients of sleep), rhythmic midtemporal theta of drowsiness (aka psychomotor variant), Cigánek rhythm (aka midline theta), 6 Hz phantom spike-wave, 14 and 6 Hz positive spikes, subclinical rhythmic epileptiform discharges of adults (SREDA), slow-fused transients, occipital spikes of blindness, and temporal slowing of the elderly. Correctly identifying artifacts and normal patterns can help avoid overinterpretation and misdiagnosis. This is an educational review paper addressing a learning objective of the International League Against Epilepsy (ILAE) curriculum.
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Affiliation(s)
- Ushtar Amin
- University of South Florida, Department of Neurology, Tampa, Florida, USA
| | - Fábio A Nascimento
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ioannis Karakis
- Emory University School of Medicine - Neurology, Atlanta, Georgia, USA
| | - Donald Schomer
- Beth Israel Deaconess Medical Center, Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Selim R Benbadis
- University of South Florida, Department of Neurology, Tampa, Florida, USA
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EEG normal variants: A prospective study using the SCORE system. Clin Neurophysiol Pract 2022; 7:183-200. [PMID: 35865124 PMCID: PMC9294211 DOI: 10.1016/j.cnp.2022.06.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/21/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022] Open
Abstract
We analyzed the number of normal variants in a SCORE database of 3050 EEG recordings. The most common normal variant was sharp transients. We present typical examples and detailed characterization of the normal variants.
Objective To determine the prevalence and characteristics of normal variants in EEG recordings in a large cohort, and provide readers with typical examples of all normal variants for educational purposes. Methods Using the SCORE EEG system (Standardized Computer-Based Organized Reporting of EEG), we prospectively extracted EEG features in consecutive patients. In this dataset, we analyzed 3050 recordings from 2319 patients (mean age 38.5 years; range: 1–89 years). Results The distribution of the normal variants was as follows: sharp transients 19.21% (including wicket spikes), rhythmic temporal theta of drowsiness 6.03%, temporal slowing of the old 2.89%, slow fused transients 2.59%, 14-and 6-Hz bursts 1.83%, breach rhythm 1.25%, small sharp spikes 1.05%, 6-Hz spike and slow wave 0.69% and SREDA 0.03%. Conclusions The most prevalent normal variants are the sharp transients, which must not be over-read as epileptiform discharges. Significance EEG readers must be familiar with the normal variants to avoid misdiagnosis and misclassification of patients referred to clinical EEG recordings.
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