EEG normal variants: A prospective study using the SCORE system

Wüstenhagen, Stephan; Terney, Daniella; Gardella, Elena; Larsen, Pirgit Meritam; Rømer, Connie; Aurlien, Harald; Beniczky, Sándor

doi:10.1016/j.cnp.2022.06.001

Cited by 10 publications

(22 citation statements)

References 41 publications

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“…A high prevalence of 6.8–15% was recently reported, possibly due to longer EEG recordings 119,120 and broader definition. A more recent study found the prevalence of sharp transients, including wickets, to be 19.2%, 121 accounting for the most common normal variant. Wicket rhythms are more likely to be recorded on days 1 or 2 of video‐EEG monitoring, and thus may not be present on a routine EEG 120 .…”

Section: Normal Variants or Variants Of Uncertain Clinical Significancementioning

confidence: 97%

“…Whether they are labeled “wickets” or remain “nameless”, 2,115,122 it is critical that they not be labeled abnormal. They are predominantly seen in adults with a median age of 28 (range 1–89) 121 . They are monophasic arciform or sharply contoured waves that are surface negative in polarity.…”

Section: Normal Variants or Variants Of Uncertain Clinical Significancementioning

confidence: 99%

“…Their prevalence is reported to be between 1.85% and 24% 118,131,132 . A recently published study showed a prevalence of 1% with a median age of 38 (range 10–80) 121 . They are mainly seen in adults but have been reported in adolescents and children 132,133 .…”

Section: Normal Variants or Variants Of Uncertain Clinical Significancementioning

confidence: 99%

“…The prevalence of RMTD is 0.1–2.1% 117‐119,152‐154 and up to 10% in adults over 60 years 155 . A recent study showed a higher prevalence of 6% with a median age of 30 (range 3–80) 121 . It is mostly seen in young adults 134 but can be seen in children 156,157 .…”

Section: Normal Variants or Variants Of Uncertain Clinical Significancementioning

confidence: 99%

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Normal variants and artifacts: Importance in EEG interpretation

Amin

Nascimento

Karakis

et al. 2023

Epileptic Disorders

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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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Section: Normal Variants or Variants Of Uncertain Clinical Significancementioning

confidence: 97%

Section: Normal Variants or Variants Of Uncertain Clinical Significancementioning

confidence: 99%

Section: Normal Variants or Variants Of Uncertain Clinical Significancementioning

confidence: 99%

Section: Normal Variants or Variants Of Uncertain Clinical Significancementioning

confidence: 99%

See 2 more Smart Citations

Normal variants and artifacts: Importance in EEG interpretation

Amin

Nascimento

Karakis

et al. 2023

Epileptic Disorders

View full text Add to dashboard Cite

show abstract

“…Currently, the reliability of visual analysis of EEG data is moderate [ 11 , 12 , 13 ]. Over-interpretation of normal waveforms as abnormal [ 14 , 15 ], inappropriate pattern-recognition of normal variants [ 16 , 17 ], and the use of subjective interpretation and reporting [ 11 ] constitute the main pitfalls [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

MindReader: Unsupervised Classification of Electroencephalographic Data

Rivas-Carrillo

Akkuratov

Valdéz-Ruvalcaba

et al. 2023

Sensors

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Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader’s predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.

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The online educational tool “Roadmap to EEGs” significantly improved trainee performance in recognizing EEG patterns

Sheikh,

Katyal,

Hadjinicolaou

et al. 2024

Epileptic Disorders

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ObjectiveWe created a framework to assess the competency‐based EEG curriculum, outlined by the International League Against Epilepsy (ILAE) through a video‐based online educational resource (“Roadmap to EEGs”) and assessed its effectiveness and feasibility in improving trainees' knowledge.MethodsTen video‐based e‐learning modules addressed seven key topics in EEG and epileptology (normal EEG, normal variants, EEG artifacts, interictal epileptiform discharges (IED), focal seizures, idiopathic generalized epilepsy (IGE), and developmental and epileptic encephalopathies (DEE)). We posted the educational videos on YouTube for free access. Pre‐ and post‐tests, each comprising 20 multiple‐choice questions, were distributed to institution leadership and advertised on social media platforms to reach a global audience. The tests were administered online to assess the participants' knowledge. Pre‐ and post‐test questions showed different EEG samples to avoid memorization and immediate recall. After completing the post‐test, participants were asked to respond to 7 additional questions assessing their confidence levels and recommendations for improvement.ResultsA total of 52 complete and matched pre‐ and post‐test responses were collected. The probability of a correct response was 73% before teaching (95% CI: 70%–77%) and 81% after teaching (95% CI: 78%–84%). The odds of a correct response increased significantly by 59% (95% CI: 28%–98%, p < .001). For participants having >4 weeks of EEG training, the probability of a correct response was 76% (95% CI: .72–.79) and 81% after teaching (95% CI: .78–.84). The odds of answering correctly increased by 44% (95% CI: 15%–80%, p = .001). Participants felt completely confident in independently interpreting and identifying EEG findings after completing the teaching modules (17.1% before vs. 37.8% after, p‐value < .0001). 86.5% of participants expressed a high likelihood of recommending the module to other trainees.SignificanceThe video‐based online educational resource allows participants to acquire foundational knowledge in EEG/epilepsy, and participants to review previously learned EEG/epilepsy information.

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EEG normal variants: A prospective study using the SCORE system

Cited by 10 publications

References 41 publications

Normal variants and artifacts: Importance in EEG interpretation

Normal variants and artifacts: Importance in EEG interpretation

MindReader: Unsupervised Classification of Electroencephalographic Data

The online educational tool “Roadmap to EEGs” significantly improved trainee performance in recognizing EEG patterns

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