Support vector machines for seizure detection in an animal model of chronic epilepsy

Journal of Neural Engineering

Volumn 7, Issue 3, 2010, Pages

  Nandan, Manu ^a   Talathi, Sachin S ^a,b   Myers, Stephen ^a   Ditto, William L ^c   Khargonekar, Pramod P ^b   Carney, Paul R ^a,b  

^a University of Florida   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

^c AZ 85281   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL MODEL; CONTROL PARAMETERS; CROSS VALIDATION; DATA SETS; DETECTION LATENCY; MEAN ENERGY; ONE CLASS-SVM; OPTIMAL CONTROLS; OPTIMALITY; PERFORMANCE STATISTICS; RELIABLE PERFORMANCE; SAMPLING RATES; SEIZURE DETECTION; SUPPORT VECTOR DATA DESCRIPTION; WAVELET ENERGY;

ANIMALS; CONTROL SYSTEM ANALYSIS; ELECTROENCEPHALOGRAPHY; GEARS;

SUPPORT VECTOR MACHINES;

ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; CONTROLLED STUDY; DATA BASE; ELECTROENCEPHALOGRAM; ENERGY; EPILEPSY; EPILEPTIC STATE; INTERMETHOD COMPARISON; NONHUMAN; ONE CLASS SUPPORT VECTOR MACHINE; PERFORMANCE; PRIORITY JOURNAL; RAT; RELIABILITY; SEIZURE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR DATA DESCRIPTION; SUPPORT VECTOR MACHINE; VALIDATION STUDY; WEIGHTED SUPPORT VECTOR MACHINE; ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; CHRONIC DISEASE; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; ELECTROENCEPHALOGRAPHY; EVALUATION; HUMAN; METHODOLOGY; PATHOPHYSIOLOGY; REPRODUCIBILITY; SPRAGUE DAWLEY RAT;

ANIMALIA; RATTUS;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BRAIN; CHRONIC DISEASE; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; PATTERN RECOGNITION, AUTOMATED; RATS; RATS, SPRAGUE-DAWLEY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 77952326567     PISSN: 17412560     EISSN: 17412552     Source Type: Journal    
DOI: 10.1088/1741-2560/7/3/036001     Document Type: Article

References (28)

1. Ahmad A R, Khalia M, Viard-Gaudin C and Poisson E 2004 Online handwriting recognition using support vector machine TENCON 2004 (IEEE Region 10 Conference vol A) vol 1 pp 311-4
2. Bennett K and Campbel C 2000 Support vector machines: hype or hallelujah? SIGKDD Explor. Newsl. 2 1-13
3. Burges C J 1998 A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discovery 2 121-67
4. Cao L 2003 Support vector machines experts for time series forecasting Neurocomputing 51 321-39
5. Chang C C and Lin C J 2001 LIBSVM: a library for support vector machines (software available at www.csie.ntu.edu.tw/∼cjlin/libsvm)
6. Chew H G, Crisp D J, Bogner R E and Lim C C 2000 Target detection in radar imagery using support vector machines with training size biasing Proc. of the 6th Int. Conf. on Control, Automation, Robotics and Vision (ICARCV 2000)
7. Cortes C and Vapnik V 1995 Support-vector networks Machine Learning pp 273-97
8. Devijver P A and Kittler J 1982 Pattern Recognition: A Statistical Approach (London: Prentice-Hall)
9. Esteller R, Echauz J, Tcheng T, Litt B and Pless B 2001 Line length: an efficient feature for seizure onset detection Proc. of the 23rd Ann. Int. Conf. of the IEEE on Engineering in Medicine and Biology Society vol 2 pp 1707-10
10. Gardner A B, Krieger A M, Vachtsevanos G and Litt B 2006 One-class novelty detection for seizure analysis from intracranial eeg J. Mach. Learn. Res. 7 1025-44
11. Gonzalez-Vellon B, Sanei S and Chambers J A 2003 Support vector machines for seizure detection Proc. of the 3rd IEEE Int. Symp. on Signal Processing and Information Technology pp 126-129
12. Katz M J 1988 Fractals and the analysis of waveforms Comp. Biol. Med. 18 145-56
13. Lothman E W, Bertram E H, Bekenstein J W and Perlin J B 1989 Self-sustaining limbic status epilepticus induced by 'continuous' hippocampal stimulation: electrographic and behavioral characteristics Epilepsy Res. 3 107-19
14. Monto S, Vanhatalo S, Holmes M D and Palva J M 2007 Epileptogenic neocortical networks are revealed by abnormal temporal dynamics in seizure-free subdural eeg Cerebral Cortex 17 1386-93
15. Mormann F, Andrzejak R G, Elger C E and Lehnertz K 2007 Seizure prediction: the long and winding road Brain 130 314-33
16. Müller K R, Mika S, Rätsch G, Tsuda K and Schölkopf B 2001 An introduction to kernel-based learning algorithms IEEE Trans. Neural Netw. 12 181-201
17. Osorio I, Frei M G and Wilkinson S B 1998 Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset Epilepsia 39 615-27
18. Saab M E and Gotman J 2005 A system to detect the onset of epileptic seizures in scalp eeg J. Clinic Neurophysiol. 116 427-42
19. Sanchez J C, Mareci T, Norman W M, Principe J C, Ditto and Carney P R 2006 Evolving into epilepsy: multiscale electrophysiological analysis and imaging in an animal model Exp. Neurol. 198 31-47 (Pubitemid 43243742)
20. Schölkopf B, Platt J C, Taylor J S, Smola A J and Williamson R C 2001 Estimating the support of a high-dimensional distribution Neural Comput. 13 1443-71
21. Talathi S S, Hwang D U, Spano M L, Simonott J, Furman M D, Myers S M, Winters J T, Ditto W L and Carney P R 2008 Non-parametric early seizure detection in an animal model of temporal lobe epilepsy J. Neural Eng. 5 85-98
22. Talathi S S, Hwang D U, Ditto W L, Mareci T, Sepulveda H, Spano M L and Carney P R 2009 Circadian control of neural excitability in an animal model of temporal lobe epilepsy Neurosci. Lett. 15 145-9
23. Tax D M J and Duin R P W 2004 Support vector data description Mach. Learn. 54 45-66
24. Meier R, Dittrich H, Schulze-Bonhage A and Aertsen A 2008 Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns J. Clinic. Neurophysiol. 25 119-31
25. Temko A, Thomas E, Boylan G, Marnane W and Lightbody G 2009 An SVM-based system and its performance for detection of seizures in neonates Conf. Proc. IEEE Eng. Med. Biol. Soc. 1 2643-6
26. Friedrichs F and Igel C 2005 Evolutionary tuning of multiple SVM parameters Neurocomputing 64 107-17
27. Huang C L and Wang C J 2006 A GA-based feature selection and parameters optimization for support vector machines Expert Syst. Appl. 31 231-40
28. Chan A, Sun F, Boto E and Wingeier B 2008 Automated seizure onset detection for accurate onset time determination in intracranial EEG Clinic. Neurophysiol. 119 2687-96