Literature DB >> 12464324

Spike detection: a review and comparison of algorithms.

Scott B Wilson1, Ronald Emerson.   

Abstract

For algorithm developers, this review details recent approaches to the problem, compares the accuracy of various algorithms, identifies common testing issues and proposes some solutions. For the algorithm user, e.g. electroencephalograph (EEG) technician or neurologist, this review provides an estimate of algorithm accuracy and comparison to that of human experts. Manuscripts dated from 1975 are reviewed. Progress since Frost's 1985 review of the state of the art is discussed. Twenty-five manuscripts are reviewed. Many novel methods have been proposed including neural networks and high-resolution frequency methods. Algorithm accuracy is less than that of experts, but the accuracy of experts is probably less than what is commonly believed. Larger record sets will be required for expert-level detection algorithms.

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Year:  2002        PMID: 12464324     DOI: 10.1016/s1388-2457(02)00297-3

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


  39 in total

1.  Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning.

Authors:  Maurice Abou Jaoude; Jin Jing; Haoqi Sun; Claire S Jacobs; Kyle R Pellerin; M Brandon Westover; Sydney S Cash; Alice D Lam
Journal:  Clin Neurophysiol       Date:  2019-11-11       Impact factor: 3.708

Review 2.  High-frequency oscillations and other electrophysiological biomarkers of epilepsy: clinical studies.

Authors:  Greg Worrell; Jean Gotman
Journal:  Biomark Med       Date:  2011-10       Impact factor: 2.851

3.  User-guided interictal spike detection.

Authors:  Mahmoud El-Gohary; James McNames; Siegward Elsas
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008

4.  Interictal epileptiform activity outside the seizure onset zone impacts cognition.

Authors:  Hoameng Ung; Christian Cazares; Ameya Nanivadekar; Lohith Kini; Joost Wagenaar; Danielle Becker; Abba Krieger; Timothy Lucas; Brian Litt; Kathryn A Davis
Journal:  Brain       Date:  2017-08-01       Impact factor: 13.501

5.  Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data.

Authors:  Otis Smart; Lauren Burrell
Journal:  Eng Appl Artif Intell       Date:  2015-03       Impact factor: 6.212

6.  SADE3: an effective system for automated detection of epileptiform events in long-term EEG based on context information.

Authors:  Fernanda I M Argoud; Fernando M De Azevedo; José Marino Neto; Eugênio Grillo
Journal:  Med Biol Eng Comput       Date:  2006-05-04       Impact factor: 2.602

7.  High inter-reviewer variability of spike detection on intracranial EEG addressed by an automated multi-channel algorithm.

Authors:  Daniel T Barkmeier; Aashit K Shah; Danny Flanagan; Marie D Atkinson; Rajeev Agarwal; Darren R Fuerst; Kourosh Jafari-Khouzani; Jeffrey A Loeb
Journal:  Clin Neurophysiol       Date:  2011-10-26       Impact factor: 3.708

8.  Neuronal network dysfunction precedes storage and neurodegeneration in a lysosomal storage disorder.

Authors:  Rebecca C Ahrens-Nicklas; Luis Tecedor; Arron F Hall; Elena Lysenko; Akiva S Cohen; Beverly L Davidson; Eric D Marsh
Journal:  JCI Insight       Date:  2019-11-01

9.  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

10.  EEG CLassification Via Convolutional Neural Network-Based Interictal Epileptiform Event Detection.

Authors:  John Thomas; Luca Comoretto; Jing Jin; Justin Dauwels; Sydney S Cash; M Brandon Westover
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2018-07
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