Dergiler / Journal of Health Sciences and Medicine / 2026 / Volume: 9 Issue: 6

Quantitative EEG correlates of visual background slowing in routine EEG

Nur Türkmen(Ankara Bilkent City Hospital, Division of Clinical NEurophysiology)0000-0001-5863-7010
Duygu Engez Hancı(Ankara Bilkent City Hospital, Department of Neurology, Ankara, Türkiye)0000-0001-6672-5774
Güray Koç(UNIVERSITY OF HEALTH SCIENCES, GÜLHANE SCHOOL OF MEDICINE, GÜLHANE MEDICINE PR. (ANKARA))0000-0002-2477-5244
Sayfa
—
Yayın Tarihi
2026-10-25
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Quantitative EEG should complement expert visual interpretation rather than serve as an independent diagnostic tool.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Aims: Background slowing on routine electroencephalography (EEG) is a clinically relevant nonepileptiform finding.
📊
Temel Bulgular & Veri:Methods: This retrospective study included 56 adults who underwent routine EEG between 2023 and 2026.
💡
Sonuç & Katkı:Quantitative EEG should complement expert visual interpretation rather than serve as an independent diagnostic tool.
🔬Tür:Research Article
📍Kapsam:2023 Verileri
⚙️Değişkenler:Quantitative electroencephalography · background slowing · spectral analysis

Abstract

Aims: Background slowing on routine electroencephalography (EEG) is a clinically relevant nonepileptiform finding. This study aimed to determine how visually identified pathological background slowing is represented by interpretable quantitative EEG (qEEG) parameters. Methods: This retrospective study included 56 adults who underwent routine EEG between 2023 and 2026. Recordings were classified by two epileptologists blinded to the qEEG results as normal background activity (n=30) or pathological background slowing (n=26). Quantitative analysis included relative delta, theta, alpha, and beta power from 19 scalp channels, together with the delta/alpha ratio (DAR), the (delta+theta)/(alpha+beta) ratio (DTABR), dominant frequency, and generalized slowing percentage. Results: Compared with the normal group, the pathological slowing group showed higher relative delta and theta power and lower relative alpha and beta power (all p<0.001). DAR (4.38 vs. 0.53), DTABR (5.47 vs. 0.61), and generalized slowing percentage (98.56% vs. 7.63%) were significantly increased, whereas dominant frequency was reduced (2.56 vs. 9.12 Hz; p<0.001). DTABR showed the strongest correlation with visual slowing grade (Spearman rho=0.772; p<0.001). Exploratory receiver operating characteristic analysis showed the highest within-sample discrimination of visual EEG classification for DTABR [area under the curve (AUC)=1.000], followed by DAR (AUC=0.978) and dominant frequency (AUC=0.871). These findings reflect concordance with visual assessment rather than independent diagnostic validation. Conclusion: Visually identified pathological background slowing is consistently represented by interpretable qEEG parameters. DAR, DTABR, dominant frequency, and generalized slowing percentage are measurable correlates of visual background slowing. Quantitative EEG should complement expert visual interpretation rather than serve as an independent diagnostic tool.

🔖

Bu Makaleye Atıf Yap

Türkmen, N., Hancı, D. E., Koç, G. (2026). Quantitative EEG correlates of visual background slowing in routine EEG. Journal of Health Sciences and Medicine, Volume: 9 Issue: 6. https://ajindex.com/articles/1668058

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (18)

  1. Vellieux G, Amiel H, Roos C, et al. Spectral analysis of EEG in etiological assessment of patients with transient neurological deficits. Neurophysiol Clin. 2021;51(3):225-232. doi:10.1016/j.neucli.2021.04.001
  2. Comanducci A, Boly M, Claassen J, et al. Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group. Clin Neurophysiol. 2020;131(11):2736-2765. doi:10.1016/j.clinph.2020.07.015
  3. Ballanti S, Campagnini S, Liuzzi P, et al. EEG-based methods for recovery prognosis of patients with disorders of consciousness: a systematic review. Clin Neurophysiol. 2022;144:98-114. doi:10.1016/j.clinph.2022.09.017
  4. Müller M, Rossetti AO, Zimmermann R, et al. Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies. Crit Care. 2020;24:680. doi:10.1186/s13054-020-03407-2
  5. Curley WH, Comanducci A, Fecchio M. Conventional and investigational approaches leveraging clinical EEG for prognosis in acute disorders of consciousness. Semin Neurol. 2022;42(3):309-324. doi:10.1055/s-0042-1755220
  6. Kondziella D, Bender A, Diserens K, et al. European Academy of Neurology guideline on the diagnosis of coma and other disorders of consciousness. Eur J Neurol. 2020;27(5):741-756. doi:10.1111/ene.14151
  7. Sansevere AJ, Hahn CD, Abend NS. Conventional and quantitative EEG in status epilepticus. Seizure. 2019;68:38-45. doi:10.1016/j.seizure.2018.09.011
  8. Mahajan A, Jaramillo-Jimenez A, D’Anselmo A, et al. Quantitative electroencephalography in the diagnosis of dementia with Lewy bodies. Clin Neurophysiol Pract. 2025;10:222-233. doi:10.1016/j.cnp.2025.06.008
  9. Finnigan S, Wong A, Read S. Defining abnormal slow EEG activity in acute ischaemic stroke: delta/alpha ratio as an optimal QEEG index. Clin Neurophysiol. 2016;127(2):1452-1459. doi:10.1016/j.clinph.2015.07.014
  10. Finnigan S, van Putten MJAM. EEG in ischaemic stroke: quantitative EEG can uniquely inform (sub-)acute prognoses and clinical management. Clin Neurophysiol. 2013;124(1):10-19. doi:10.1016/j.clinph.2012.07.003
  11. Yu Z, Wen D, Zheng J, et al. Predictive accuracy of alpha-delta ratio on quantitative electroencephalography for delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage: meta-analysis. World Neurosurg. 2019;126:e510-e516. doi:10.1016/j.wneu.2019.02.082
  12. Wiegand TLT, Rémi J, Dimitriadis K. Electroencephalography in delirium assessment: a scoping review. BMC Neurol. 2022;22:86. doi:10.1186/s12883-022-02557-w
  13. Munjal NK, Bergman I, Scheuer ML, Genovese CR, Simon DW, Patterson CM. Quantitative EEG predicting acute neurological deterioration in the pediatric ICU: a case series. J Child Neurol. 2022;37(1):73-79. doi:10.1177/08830738211053908
  14. Kimchi EY, Neelagiri A, Whitt W, et al. Clinical EEG slowing correlates with delirium severity and predicts poor clinical outcomes. Neurology. 2019;93(13):e1260-e1271. doi:10.1212/WNL.0000000000008164
  15. Welch PD. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust. 1967;15(2):70-73. doi:10.1109/TAU.1967.1161901
  16. Michels DM, van Marum S, Arends S, Tavy DLJ, Wirtz PW, de Bruijn BSFTM. Visual electroencephalography assessment in the diagnosis and prognosis of cognitive disorders. J Clin Neurophysiol. 2025;42(3):243-250. doi:10.1097/WNP.0000000000001107
  17. Jonkman EJ. A simple EEG scoring method for senile dementia of the Alzheimer type. Electroencephalogr Clin Neurophysiol. 1989;72:44.
  18. Hirsch LJ, Fong MWK, Leitinger M, et al. American Clinical Neurophysiology Society’s standardized critical care EEG terminology: 2021 version. J Clin Neurophysiol. 2021;38(1):1-29. doi:10.1097/WNP.0000000000000806