Epileptic seizure prediction using relative spectral power features

Clinical Neurophysiology

Volumn 126, Issue 2, 2015, Pages 237-248

  Bandarabadi, Mojtaba ^a   Teixeira, César A ^a   Rasekhi, Jalil ^a   Dourado, António ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

Classification; Epileptic seizure prediction; Feature reduction; Relative spectral power

Indexed keywords

ADOLESCENT; ADULT; ALGORITHM; ARTICLE; CLINICAL ARTICLE; ELECTROENCEPHALOGRAPHY; FEMALE; FOCAL EPILEPSY; HUMAN; MACHINE LEARNING; MALE; MIDDLE AGED; POWER ANALYSIS; PREDICTION; PRIORITY JOURNAL; SEIZURE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VIDEORECORDING; YOUNG ADULT; ARTIFICIAL INTELLIGENCE; EPILEPSY; PATHOPHYSIOLOGY; PREDICTIVE VALUE; PROCEDURES;

ADOLESCENT; ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; ELECTROENCEPHALOGRAPHY; EPILEPSY; FEMALE; HUMANS; MALE; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; SUPPORT VECTOR MACHINES; YOUNG ADULT;

EID: 84921565638     PISSN: 13882457     EISSN: 18728952     Source Type: Journal    
DOI: 10.1016/j.clinph.2014.05.022     Document Type: Article

References (49)

1. Andrzejak R.G., Chicharro D., Elger C.E., Mormann F. Seizure prediction: any better than chance?. Clin Neurophysiol 2009, 120:1465-1478.
2. Ayala M., Cabrerizo M., Jayakar P., Adjouadi M. Subdural EEG classification into seizure and nonseizure files using neural networks in the gamma frequency band. J Clin Neurophysiol 2011, 28:20-29.
3. Cabrerizo M., Ayala M., Goryawala M., Jayakar P., Adjouadi M. A new parametric feature descriptor for the classification of epileptic and control EEG records in pediatric population. Int J Neural Syst 2012, 22:1250001.
4. Carney P.R., Myers S., Geyer J.D. Seizure prediction: methods. Epilepsy Behav 2011, 22(suppl. 1):S94-S101.
5. Cerf R., El Ouasdad E.H. Spectral analysis of stereo-electroencephalograms: preictal slowing in partial epilepsies. Biol Cybern 2000, 83:399.
6. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011, 2:1-27.
7. Chawla N.V., Japkowicz N., Kotcz A. Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explor Newsl 2004, 6:1-6.
8. Chisci L., Mavino A., Perferi G., Sciandrone M., Anile C., Colicchio G., et al. Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Trans Biomed Eng 2010, 57:1124-1132.
9. Dressler O., Schneider G., Stockmanns G., Kochs E.F. Awareness and the EEG power spectrum: analysis of frequencies. Br J Anaesth 2004, 93:806-809.
10. Ebersole J.S. In search of seizure prediction: a critique. Clin Neurophysiol 2005, 116:489-492.
11. Feldwisch-Drentrup H., Schelter B., Jachan M., Nawrath J., Timmer J., Schulze-Bonhage A. Joining the benefits: combining epileptic seizure prediction methods. Epilepsia 2010, 51:1598-1606.
12. Fukunaga K. Introduction to statistical pattern recognition 1990, Academic Press.
13. Gadhoumi K., Lina J.-M., Gotman J. Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG. Clin Neurophysiol 2012, 123:1906-1916.
14. Iasemidis L.D., Shiau D.S., Chaovalitwongse W., Sackellares J.C., Pardalos P.M., Principe J.C., et al. Adaptive epileptic seizure prediction system. IEEE Trans Biomed Eng 2003, 50:616-627.
15. Kharbouch A., Shoeb A., Guttag J., Cash S.S. An algorithm for seizure onset detection using intracranial EEG. Epilepsy Behav 2011, 22(suppl. 1):S29-S35.
16. Klatt J., Feldwisch-Drentrup H., Ihle M., Navarro V., Neufang M., Teixeira C., et al. The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia 2012, 53:1669-1676.
17. Le Van Quyen M., Martinerie J., Navarro V., Boon P., D'Havé M., Adam C., et al. Anticipation of epileptic seizures from standard EEG recordings. Lancet 2001, 357:183-188.
18. Lehnertz K., Litt B. The first international collaborative workshop on seizure prediction: summary and data description. Clin Neurophysiol 2005, 116:493-505.
19. Lehnertz K., Mormann F., Kreuz T., Andrzejak R.G., Rieke C., David P., et al. Seizure prediction by nonlinear EEG analysis. IEEE Eng Med Biol Mag 2003, 22:57-63.
20. Litt B., Echauz J. Prediction of epileptic seizures. Lancet Neurol 2002, 1:22-30.
21. Mirowski P., Madhavan D., LeCun Y., Kuzniecky R. Classification of patterns of EEG synchronization for seizure prediction. Clin Neurophysiol 2009, 120:1927-1940.
22. Mormann F., Kreuz T., Andrzejak R.G., David P., Lehnertz K., Elger C.E. Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Res 2003, 53:173-185.
23. Mormann F., Kreuz T., Rieke C., Andrzejak R.G., Kraskov A., David P., et al. On the predictability of epileptic seizures. Clin Neurophysiol 2005, 116:569-587.
24. Mormann F., Andrzejak R.G., Elger C.E., Lehnertz K. Seizure prediction: the long and winding road. Brain 2007, 130:314-333.
25. Navarro V., Martinerie J., Le Van Quyen M., Baulac M., Dubeau F., Gotman J. Seizure anticipation: do mathematical measures correlate with video-eeg evaluation?. Epilepsia 2005, 46:385-396.
26. Netoff T, Yun P, Parhi K. Seizure prediction using cost-sensitive support vector machine. Engineering in medicine and biology society, 2009 EMBC 2009. In: Annual international conference of the IEEE 2009. p. 3322-5.
27. Norton M.P., Karczub D.G. Fundamentals of noise and vibration analysis for engineers 2003, Cambridge University Press.
28. Parhi K.K., Ayinala M. Low-complexity Welch power spectral density computation. IEEE Trans Circuits Syst I, Reg Papers 2014, 61:172-182.
29. Park Y., Luo L., Parhi K.K., Netoff T. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 2011, 52:1761-1770.
30. Pearce A., Wulsin D., Blanco J.A., Krieger A., Litt B., Stacey W.C. Temporal changes of neocortical high-frequency oscillations in epilepsy. J Neurophysiol 2013, 110:1167-1179.
31. Peng H., Fulmi L., Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005, 27:1226-1238.
32. Rasekhi J., Karami Mollaei M.R., Bandarabadi M., Teixeira C., Dourado A. Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. J Neurosci Methods 2013, 217(1-2):9-16.
33. Sanei S., Chambers J.A. EEG signal processing 2008, Wiley.
34. Schelter B., Winterhalder M., Maiwald T., Brandt A., Schad A., Schulze-Bonhage A., et al. Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction. Chaos 2006, 16:013108.
35. Schelter B., Winterhalder M., Drentrup H.F.G., Wohlmuth J., Nawrath J., Brandt A., et al. Seizure prediction: the impact of long prediction horizons. Epilepsy Res 2007, 73:213-217.
36. Schölkopf B., Smola A.J. Learning with Kernels: support vector machines, regularization, optimization, and beyond 2002, Mit Press.
37. Schulze-Bonhage A., Sales F., Wagner K., Teotonio R., Carius A., Schelle A., et al. Views of patients with epilepsy on seizure prediction devices. Epilepsy Behav 2010, 18:388-396.
38. Shoeb A., Pang T., Guttag J., Schachter S. Vagus nerve stimulation triggered by machine learning based seizure vagus nerve stimulation triggered by machine learning? Based seizure. epilepsy 2011, CRC Press, p. 385-95.
39. Shufang L., Weidong Z., Qi Y., Yinxia L. Seizure prediction using spike rate of intracranial EEG. IEEE Trans Neural Syst Rehabil Eng 2013, 21:880-886.
40. Spencer S., Huh L. Outcomes of epilepsy surgery in adults and children. Lancet Neurol 2008, 7:525-537.
41. Stacey W., Le Van Quyen M., Mormann F., Schulze-Bonhage A. What is the present-day EEG evidence for a preictal state?. Epilepsy Res 2011, 97:243-251.
42. Teixeira C.A., Direito B., Feldwisch-Drentrup H., Valderrama M., Costa R.P., Alvarado-Rojas C., et al. EPILAB: a software package for studies on the prediction of epileptic seizures. J Neurosci Methods 2011, 200:257-271.
43. Teixeira C., Direito B., Bandarabadi M., Le Van Quyen M., Valderrama M., Schelter B., et al. Epileptic seizure predictors based on computational intelligence techniques A comparative study with 278 patients. Comput Methods Programs Biomed 2014, 114(3):324-336.
44. Téllez-Zenteno J.F., Hernández-Ronquillo L. A review of the epidemiology of temporal lobe epilepsy. Epilepsy Res Treat 2012, 2012:1-5.
45. Van Laar J.O.E.H., Peters C.H.L., Houterman S., Wijn P.F.F., Kwee A., Oei S.G. Normalized spectral power of fetal heart rate variability is associated with fetal scalp blood pH. Early Hum Dev 2011, 87:259-263.
46. Vapnik V. Statistical learning theory 1998, Wiley, New York.
47. Viglione S., Walsh G. Proceedings: epileptic seizure prediction. Electroencephalogr Clin Neurophysiol 1975, 39:435-436.
48. Williamson J.R., Bliss D.W., Browne D.W., Narayanan J.T. Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy Behav 2012, 25:230-238.
49. Winterhalder M., Maiwald T., Voss H.U., Aschenbrenner-Scheibe R., Timmer J., Schulze-Bonhage A. The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav 2003, 4:318-325.