Literature DB >> 20005158

Seizure lateralization in scalp EEG using Hjorth parameters.

T Cecchin1, R Ranta, L Koessler, O Caspary, H Vespignani, L Maillard.   

Abstract

OBJECTIVE: This paper describes and assesses a new semi-automatic method for temporal lobe seizures lateralization using raw scalp EEG signals.
METHODS: We used the first two Hjorth parameters to estimate quadratic mean and dominant frequency of signals. Their mean values were computed on each side of the brain and segmented taking into account the seizure onset time identified by the electroencephalographist, to keep only the initial part of the seizure, before a possible spreading to the contralateral side. The means of segmented variables were used to characterize the seizure by a point in a (frequency, amplitude) plane. Six criteria were proposed for the partitioning of this plane for lateralization.
RESULTS: The procedure was applied to 45 patients (85 seizures). The two best criteria yielded, for the first one, a correct lateralization for 96% of seizures and, for the other, a lateralization rate of 87% without incorrect lateralization.
CONCLUSIONS: The method produced satisfactory results, easy to interpret. The setting of procedure parameters was simple and the approach was robust to artifacts. It could constitute a help for neurophysiologists during visual inspection. SIGNIFICANCE: The difference of quadratic mean and dominant frequency on each side of the brain allows lateralizing the seizure onset.

Entities:  

Mesh:

Year:  2009        PMID: 20005158     DOI: 10.1016/j.clinph.2009.10.033

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  6 in total

1.  Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals.

Authors:  Yinda Zhang; Shuhan Yang; Yang Liu; Yexian Zhang; Bingfeng Han; Fengfeng Zhou
Journal:  Sensors (Basel)       Date:  2018-04-28       Impact factor: 3.576

2.  Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

Authors:  Paul Fergus; David Hignett; Abir Hussain; Dhiya Al-Jumeily; Khaled Abdel-Aziz
Journal:  Biomed Res Int       Date:  2015-01-29       Impact factor: 3.411

3.  A Novel EEG Based Spectral Analysis of Persistent Brain Function Alteration in Athletes with Concussion History.

Authors:  Tamanna T K Munia; Ali Haider; Charles Schneider; Mark Romanick; Reza Fazel-Rezai
Journal:  Sci Rep       Date:  2017-12-08       Impact factor: 4.379

4.  Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment.

Authors:  Juan C Mier; Yejin Kim; Xiaoqian Jiang; Guo-Qiang Zhang; Samden Lhatoo
Journal:  BMC Med Inform Decis Mak       Date:  2020-12-24       Impact factor: 2.796

Review 5.  The Temporal Lobe as a Symptomatogenic Zone in Medial Parietal Lobe Epilepsy.

Authors:  Nadim Jaafar; Amar Bhatt; Alexandra Eid; Mohamad Z Koubeissi
Journal:  Front Neurol       Date:  2022-03-15       Impact factor: 4.003

6.  Prediction of gait intention from pre-movement EEG signals: a feasibility study.

Authors:  S M Shafiul Hasan; Masudur R Siddiquee; Roozbeh Atri; Rodrigo Ramon; J Sebastian Marquez; Ou Bai
Journal:  J Neuroeng Rehabil       Date:  2020-04-16       Impact factor: 4.262

  6 in total

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