Literature DB >> 88339

Automatic recognition of inter-ictal epileptic activity in prolonged EEG recordings.

J Gotman, J R Ives, P Gloor.   

Abstract

A method of automatic recognition and quantification of inter-ictal epileptic activity in the human EEG had previously been developed and tested using short recordings from awake subjects. This paper describes the adaptation of the method for use during overnight recordings in free-moving unattended patients, in combination with the already existing seizure monitoring system. EEG s were recorded from scalp and sphenoidal electrodes, using cable telemetry and a PDP-12 computer. The spike and sharp wave recognition method allowed the on-line analysis of 16 channels. A section of the 16-channel EEG including 1 sec before and 1 sec after each detected spike was saved on digital magnetic tape. Upon completion of the monitoring session, the tape was played back on the EEG machine, giving a discontinuous tracing of spike sections; this constituted a highly concentrated view of the inter-ictal epileptic activity, in traditional paper form. The spike sections were further analyzed by computer to determine and display on the computer terminal the spatial and temporal distributions of the epileptic activity, providing a complete synopsis of the recording. Several examples of the type of information available from this anslysis are discussed in detail. False detection rates are given for 34 six hour recordings, indicating a high vari ability in the performance, mainly because of artefacts. It is concluded that the final computer displays could only be trusted after visual inspection of the EEG sections provided on paper. The variety of morphologies of artefacts appeared to preclude a total automatic elimination.

Entities:  

Mesh:

Year:  1979        PMID: 88339     DOI: 10.1016/0013-4694(79)90004-x

Source DB:  PubMed          Journal:  Electroencephalogr Clin Neurophysiol        ISSN: 0013-4694


  10 in total

1.  Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation.

Authors:  Jin Jing; Haoqi Sun; Jennifer A Kim; Aline Herlopian; Ioannis Karakis; Marcus Ng; Jonathan J Halford; Douglas Maus; Fonda Chan; Marjan Dolatshahi; Carlos Muniz; Catherine Chu; Valeria Sacca; Jay Pathmanathan; Wendong Ge; Justin Dauwels; Alice Lam; Andrew J Cole; Sydney S Cash; M Brandon Westover
Journal:  JAMA Neurol       Date:  2020-01-01       Impact factor: 18.302

2.  Patient-specific early seizure detection from scalp electroencephalogram.

Authors:  Georgiy R Minasyan; John B Chatten; Martha J Chatten; Richard N Harner
Journal:  J Clin Neurophysiol       Date:  2010-06       Impact factor: 2.177

Review 3.  Clinical neurophysiology of epilepsy.

Authors:  Anil Mendiratta
Journal:  Curr Neurol Neurosci Rep       Date:  2003-07       Impact factor: 5.081

4.  Methods of assessment of antiepileptic drugs.

Authors:  N Milligan; A Richens
Journal:  Br J Clin Pharmacol       Date:  1981-05       Impact factor: 4.335

5.  Automated epilepsy detection techniques from electroencephalogram signals: a review study.

Authors:  Supriya Supriya; Siuly Siuly; Hua Wang; Yanchun Zhang
Journal:  Health Inf Sci Syst       Date:  2020-10-12

6.  A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Authors:  Marzia De Lucia; Juan Fritschy; Peter Dayan; David S Holder
Journal:  Med Biol Eng Comput       Date:  2007-12-11       Impact factor: 2.602

7.  Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts.

Authors:  Mustafa Aykut Kural; Jin Jing; Franz Fürbass; Hannes Perko; Erisela Qerama; Birger Johnsen; Steffen Fuchs; M Brandon Westover; Sándor Beniczky
Journal:  Epilepsia       Date:  2022-03-07       Impact factor: 6.740

8.  Deep learning approach to detect seizure using reconstructed phase space images.

Authors:  N Ilakiyaselvan; A Nayeemulla Khan; A Shahina
Journal:  J Biomed Res       Date:  2020-01-24

9.  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

Review 10.  Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review.

Authors:  Zhenning Mei; Xian Zhao; Hongyu Chen; Wei Chen
Journal:  Sensors (Basel)       Date:  2018-05-26       Impact factor: 3.576

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.