Literature DB >> 10082093

An automated system for epileptogenic focus localization in the electroencephalogram.

B Ramabhadran1, J D Frost, J R Glover, P Y Ktonas.   

Abstract

This paper describes an automated system for the detection and localization of foci of epileptiform activity in the EEG. The system detects sharp EEG transients (STs) in the process, but the emphasis is on epileptic focus localization. A combination of techniques involving signal processing, pattern recognition, and the expert rules of an experienced electroencephalographer, involving considerable spatiotemporal context information, is applied to multichannel EEG data. An overall emphasis on minimizing the number of false-positive sharp transient detections drives the system design. Tested on data from 13 subjects with epileptiform activity and 5 controls, all areas of focal epileptiform activity were detected by the system, although not all of the contributing foci were reported separately. Two false-positive foci were detected as well due to nonfocal spike activity and normal spike-like activity not present in the training set. The system detected 95.7% of the epileptiform events constituting the correctly detected foci, with a false detection rate of 11.1%.

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Mesh:

Year:  1999        PMID: 10082093     DOI: 10.1097/00004691-199901000-00006

Source DB:  PubMed          Journal:  J Clin Neurophysiol        ISSN: 0736-0258            Impact factor:   2.177


  4 in total

1.  EPILEPTIFORM SPIKE DETECTION VIA CONVOLUTIONAL NEURAL NETWORKS.

Authors:  Alexander Rosenberg Johansen; Jing Jin; Tomasz Maszczyk; Justin Dauwels; Sydney S Cash; M Brandon Westover
Journal:  Proc IEEE Int Conf Acoust Speech Signal Process       Date:  2016-05-19

2.  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

3.  Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping.

Authors:  J Jing; J Dauwels; T Rakthanmanon; E Keogh; S S Cash; M B Westover
Journal:  J Neurosci Methods       Date:  2016-03-02       Impact factor: 2.390

4.  Spike pattern recognition by supervised classification in low dimensional embedding space.

Authors:  Evangelia I Zacharaki; Iosif Mporas; Kyriakos Garganis; Vasileios Megalooikonomou
Journal:  Brain Inform       Date:  2016-03-16
  4 in total

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