156
Views
0
CrossRef citations to date
0
Altmetric
Articles

Prognosis of Epileptic Seizure Event Onsets Using Random Survival Forests

, , , ORCID Icon & ORCID Icon

References

  • Afrin, K., Illangovan, G., Srivatsa, S. S., & Bukkapatnam, S. T. (2018). Balanced random survival forests for extremely unbalanced, right censored data. arXiv preprint arXiv:1803.09177.
  • Alickovic, E., Kevric, J., & Subasi, A. (2018). Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomedical Signal Processing and Control, 39, 94–102. https://doi.org/10.1016/j.bspc.2017.07.022
  • Bhattacharyya, A., & Pachori, R. B. (2017). A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform. IEEE Transactions on Bio-Medical Engineering, 64(9), 2003–2015. https://doi.org/10.1109/TBME.2017.2650259
  • Blanco, S., Garcia, H., Quiroga, R. Q., Romanelli, L., & Rosso, O. (1995). Stationarity of the EEG series. IEEE Engineering in Medicine and Biology Magazine, 14(4), 395–399. https://doi.org/10.1109/51.395321
  • Bonnett, L., Powell, G., Smith, C. T., & Marson, A. (2017). Risk of a seizure recurrence after a breakthrough seizure and the implications for driving: Further analysis of the standard versus new antiepileptic drugs (SANAD) randomised controlled trial. BMJ Open, 7(7), e015868. https://doi.org/10.1136/bmjopen-2017-015868
  • Borgan, Ø. (2005). Nelson–Aalen estimator. In Peter Armitage and Theodore Colton (Eds.), Encyclopedia biostatisics (Vol. 5). John Wiley & Sons.
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Bukkapatnam, S. T., Afrin, K., Dave, D., & Kumara, S. R. (2019). Machine learning and AI for long-term fault prognosis in complex manufacturing systems. CIRP Annals, 68(1), 459–462. https://doi.org/10.1016/j.cirp.2019.04.104
  • Chaovalitwongse, W. A., Suharitdamrong, W., Liu, C.-C., & Anderson, M. L. (2008). Brain network analysis of seizure evolution. Annales Zoologici Fennici, 45(5), 402–414. https://doi.org/10.5735/086.045.0504
  • Cheng, C., Sa-Ngasoongsong, A., Beyca, O., Le, T., Yang, H., Kong, Z. (J.)., & Bukkapatnam, S. T. S. (2015). Time series forecasting for nonlinear and non-stationary processes: A review and comparative study. Iie Transactions, 47(10), 1053–1071. https://doi.org/10.1080/0740817X.2014.999180
  • Cockerell, O. C., Hart, Y., Sander, J. W., Goodridge, D., Shorvon, S., & Johnson, A. (1994). Mortality from epilepsy: Results from a prospective population-based study. The Lancet, 344(8927), 918–921. https://doi.org/10.1016/S0140-6736(94)92270-5
  • Cockerell, O. C., Johnson, A. L., Sander, J. W., & Shorvon, S. D. (1997). Prognosis of epilepsy: A review and further analysis of the first nine years of the British National General Practice Study of Epilepsy, a prospective population-based study. Epilepsia, 38(1), 31–46. https://doi.org/10.1111/j.1528-1157.1997.tb01075.x
  • Cox, D. R. (1972). Regression models and life‐tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
  • D’Alessandro, M., Esteller, R., Vachtsevanos, G., Hinson, A., Echauz, J., & Litt, B. (2003). Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients. IEEE Transactions on Bio-Medical Engineering, 50(5), 603–615. https://doi.org/10.1109/tbme.2003.810706
  • Daoud, H., & Bayoumi, M. A. (2019). Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems, 13(5), 804–813. https://doi.org/10.1109/TBCAS.2019.2929053
  • Freeman, W. J., & Vitiello, G. (2006). Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics. Physics of Life Reviews, 3(2), 93–118. https://doi.org/10.1016/j.plrev.2006.02.001
  • Gerds, T. A., & Schumacher, M. (2006). Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical Journal. Biometrische Zeitschrift, 48(6), 1029–1040. https://doi.org/10.1002/bimj.200610301
  • Gerds, T. A., & Schumacher, M. (2007). Efron-type measures of prediction error for survival analysis. Biometrics, 63(4), 1283–1287. https://doi.org/10.1111/j.1541-0420.2007.00832.x
  • Harrell, F. E., Jr., Califf, R. M., Pryor, D. B., Lee, K. L., & Rosati, R. A. (1982). Evaluating the yield of medical tests. JAMA: The Journal of the American Medical Association, 247(18), 2543–2546. https://doi.org/10.1001/jama.1982.03320430047030
  • Hassan, A. R., & Subasi, A. (2016). Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Computer Methods and Programs in Biomedicine, 136, 65–77. https://doi.org/10.1016/j.cmpb.2016.08.013
  • Hassan, A. R., Subasi, A., & Zhang, Y. (2020). Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowledge-Based Systems, 191, 105333. https://doi.org/10.1016/j.knosys.2019.105333
  • Heagerty, P. J., & Zheng, Y. (2005). Survival model predictive accuracy and ROC curves. Biometrics, 61(1), 92–105. https://doi.org/10.1111/j.0006-341X.2005.030814.x
  • Howbert, J. J., Patterson, E. E., Stead, S. M., Brinkmann, B., Vasoli, V., Crepeau, D., Vite, C. H., Sturges, B., Ruedebusch, V., Mavoori, J., Leyde, K., Sheffield, W. D., Litt, B., & Worrell, G. A. (2014). Forecasting seizures in dogs with naturally occurring epilepsy. PLoS One, 9(1), e81920. https://doi.org/10.1371/journal.pone.0081920
  • Huang, N. E., & Wu, Z. (2008). A review on Hilbert‐Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics, 46(2), RG2006. https://doi.org/10.1029/2007RG000228
  • Hussein, R., Lee, S., Ward, R., & McKeown, M. J. (2021). Semi-dilated convolutional neural networks for epileptic seizure prediction. Neural Networks, 139, 212–222. https://doi.org/10.1016/j.neunet.2021.03.008
  • Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. Annals of Applied Statistics, 2(3), 841–860.
  • Ishwaran, H., Kogalur, U. B., Chen, X., & Minn, A. J. (2011). Random survival forests for high‐dimensional data. Statistical Analysis and Data Mining: The ASA Data Science Journal, 4(1), 115–132. https://doi.org/10.1002/sam.10103
  • Ishwaran, H., Kogalur, U. B., Gorodeski, E. Z., Minn, A. J., & Lauer, M. S. (2010). High-dimensional variable selection for survival data. Journal of the American Statistical Association, 105(489), 205–217. https://doi.org/10.1198/jasa.2009.tm08622
  • Kamath, P. S. (2001). A model to predict survival in patients with end‐stage liver disease. Hepatology, 33(2), 464–470. https://doi.org/10.1053/jhep.2001.22172
  • Kim, L. G., Johnson, T. L., Marson, A. G., Chadwick, D. W., & Group, M. M. S. (2006). Prediction of risk of seizure recurrence after a single seizure and early epilepsy: Further results from the MESS trial. The Lancet Neurology, 5(4), 317–322. https://doi.org/10.1016/S1474-4422(06)70383-0
  • Kuhlmann, L., Lehnertz, K., Richardson, M. P., Schelter, B., & Zaveri, H. P. (2018). Seizure prediction—Ready for a new era. Nature Reviews. Neurology, 14(10), 618–630. https://doi.org/10.1038/s41582-018-0055-2
  • Kuruba, R., Wu, X., & Reddy, D. S. (2018). Benzodiazepine-refractory status epilepticus, neuroinflammation, and interneuron neurodegeneration after acute organophosphate intoxication. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease, 1864(9 Pt B)(9), 2845–2858.
  • Le, T. Q., & Bukkapatnam, S. T. (2016). Nonlinear dynamics forecasting of obstructive sleep apnea onsets. PLoS One, 11(11), e0164406. https://doi.org/10.1371/journal.pone.0164406
  • Le, T. Q., Bukkapatnam, S. T., Benjamin, B. A., Wilkins, B. A., & Komanduri, R. (2013). Topology and random-walk network representation of cardiac dynamics for localization of myocardial infarction. IEEE Transactions on Bio-Medical Engineering, 60(8), 2325–2331. https://doi.org/10.1109/TBME.2013.2255596
  • Le, T. Q., Cheng, C., Sangasoongsong, A., Wongdhamma, W., & Bukkapatnam, S. T. (2013). Wireless wearable multisensory suite and real-time prediction of obstructive sleep apnea episodes. IEEE Journal of Translational Engineering in Health and Medicine, 1, 2700109.
  • Lehnertz, K., & Elger, C. E. (1998). Can epileptic seizures be predicted? Evidence from nonlinear time series analysis of brain electrical activity. Physical Review Letters, 80(22), 5019–5022. https://doi.org/10.1103/PhysRevLett.80.5019
  • Li, Y., Wang, X.-D., Luo, M.-L., Li, K., Yang, X.-F., & Guo, Q. (2018). Epileptic seizure classification of EEGs using time-frequency analysis based multiscale radial basis functions. IEEE Journal of Biomedical and Health Informatics, 22(2), 386–397. https://doi.org/10.1109/JBHI.2017.2654479
  • Litt, B., & Echauz, J. (2002). Prediction of epileptic seizures. The Lancet. Neurology, 1(1), 22–30. https://doi.org/10.1016/S1474-4422(02)00003-0
  • Mogensen, U. B., Ishwaran, H., & Gerds, T. A. (2012). Evaluating random forests for survival analysis using prediction error curves. Journal of Statistical Software, 50(11), 1–23. https://doi.org/10.18637/jss.v050.i11
  • Mormann, F., Andrzejak, R. G., Elger, C. E., & Lehnertz, K. (2007). Seizure prediction: The long and winding road. Brain, 130(Pt 2), 314–333. no https://doi.org/10.1093/brain/awl241
  • Mormann, F., Kreuz, T., Rieke, C., Andrzejak, R. G., Kraskov, A., David, P., Elger, C. E., & Lehnertz, K. (2005). On the predictability of epileptic seizures. Clinical Neurophysiology, 116(3), 569–587. https://doi.org/10.1016/j.clinph.2004.08.025
  • Mursalin, M., Zhang, Y., Chen, Y., & Chawla, N. V. (2017). Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. Neurocomputing, 241, 204–214. https://doi.org/10.1016/j.neucom.2017.02.053
  • National Institutes of Health (NIH). (1985). NIH guide for the care and use of laboratory animals (NIH Publication No. 85-23). Author.
  • Oppenheim, A. V., & Schafer, R. W. (2004). From frequency to quefrency: A history of the cepstrum. IEEE Signal Processing Magazine, 21(5), 95–106. https://doi.org/10.1109/MSP.2004.1328092
  • Portnoff, M. (1980). Time-frequency representation of digital signals and systems based on short-time Fourier analysis. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(1), 55–69. https://doi.org/10.1109/TASSP.1980.1163359
  • Reddy, D. S., Zaayman, M., Kuruba, R., & Wu, X. (2021). Comparative profile of refractory status epilepticus models following exposure of cholinergic agents pilocarpine, DFP, and soman. Neuropharmacology, 191, 108571. https://doi.org/10.1016/j.neuropharm.2021.108571 33878303
  • Reddy, D. S., Perumal, D., Golub, V., Habib, A., Kuruba, R., & Wu, X. (2020). Phenobarbital as alternate anticonvulsant for organophosphate-induced benzodiazepine-refractory status epilepticus and neuronal injury. Epilepsia Open, 5(2), 198–212. https://doi.org/10.1002/epi4.12389
  • Reddy, D., & Kuruba, R. (2013). Experimental models of status epilepticus and neuronal injury for evaluation of therapeutic interventions. International Journal of Molecular Sciences, 14(9), 18284–18318. https://doi.org/10.3390/ijms140918284
  • Reddy, S. D., Reagan, K., Ngo, A., Arnold, B., Manoharan, S., Patel, S., Kancharia, S., Vasandani, I., & Bukkapatnam, S. (2017). Point-of-Care Health Informatics for Proactive Epilepsy Seizure Alert. Texas A&M Engineering Project Showcase (April 28, 2017), College Station, Texas.
  • Schulze-Bonhage, A., & Kühn, A. (2008). Unpredictability of seizures and the burden of epilepsy. In Björn Schelter, Jens Timmer, and Andreas Schulze-Bonhage (Eds.), Seizure prediction in epilepsy: From basic mechanisms to clinical applications, (pp. 1–10). Wiley-VCH.
  • Segal, M. R. (1988). Regression trees for censored data. Biometrics, 44(1), 35–47. https://doi.org/10.2307/2531894
  • Shackman, A. J., McMenamin, B. W., Maxwell, J. S., Greischar, L. L., & Davidson, R. J. (2010). Identifying robust and sensitive frequency bands for interrogating neural oscillations. NeuroImage, 51(4), 1319–1333. https://doi.org/10.1016/j.neuroimage.2010.03.037
  • Shiao, H.-T., Cherkassky, V., Lee, J., Veber, B., Patterson, E. E., Brinkmann, B. H., & Worrell, G. A. (2017). SVM-based system for prediction of epileptic seizures from iEEG signal. IEEE Transactions on Bio-Medical Engineering, 64(5), 1011–1022. https://doi.org/10.1109/TBME.2016.2586475
  • Srinivasan, V., Eswaran, C., & Sriraam, N. (2005). Artificial neural network based epileptic detection using time-domain and frequency-domain features. Journal of Medical Systems, 29(6), 647–660. https://doi.org/10.1007/s10916-005-6133-1
  • Subasi, A., Kevric, J., & Canbaz, M. A. (2019). Epileptic seizure detection using hybrid machine learning methods. Neural Computing and Applications, 31(1), 317–325. https://doi.org/10.1007/s00521-017-3003-y
  • Tran, H. M., Bukkapatnam, S. T., & Garg, M. (2019). Detecting changes in transient complex systems via dynamic network inference. IISE Transactions, 51(3), 337–353. https://doi.org/10.1080/24725854.2018.1491075
  • Tuncer, T., Dogan, S., Ertam, F., & Subasi, A. (2020). A novel ensemble local graph structure based feature extraction network for EEG signal analysis. Biomedical Signal Processing and Control, 61, 102006. https://doi.org/10.1016/j.bspc.2020.102006
  • Tzallas, A. T. (2012). Automated epileptic seizure detection methods: A review study. In Dejan Stevanovic (Ed.), Epilepsy-histological, electroencephalographic and psychological aspects. InTech (pp. 75–98).
  • Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2009). Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Transactions on Information Technology in Biomedicine, 13(5), 703–710. https://doi.org/10.1109/TITB.2009.2017939
  • Varatharajah, Y., Iyer, R. K., Berry, B. M., Worrell, G. A., & Brinkmann, B. H. (2017). Seizure forecasting and the preictal state in canine epilepsy. International Journal of Neural Systems, 27(01), 1650046. https://doi.org/10.1142/S0129065716500465
  • Wang, N., & Lyu, M. R. (2015). Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction. IEEE Journal of Biomedical and Health Informatics, 19(5), 1648–1659. https://doi.org/10.1109/JBHI.2014.2358640
  • Wang, S., Chaovalitwongse, W. A., & Wong, S. (2013). Online seizure prediction using an adaptive learning approach. IEEE Transactions on Knowledge and Data Engineering, 25(12), 2854–2866. https://doi.org/10.1109/TKDE.2013.151
  • Wang, Z., & Bukkapatnam, S. T. (2018). A Dirichlet process Gaussian state machine model for change detection in transient processes. Technometrics, 60(3), 373–385. https://doi.org/10.1080/00401706.2017.1371079
  • World Health Organization (WHO). (2005). Atlas: Epilepsy care in the world.
  • Wu, X., Kuruba, R., & Reddy, D. S. (2018). Midazolam-resistant seizures and brain injury following acute intoxication of diisopropylfluorophosphate, an organophosphate pesticide and surrogate for nerve agents. Journal of Pharmacology and Experimental Therapeutics, 367(2), 302–321. https://doi.org/10.1124/jpet.117.247106
  • Younus, I., & Reddy, D. S. (2018). A resurging boom in new drugs for epilepsy and brain disorders. Expert Review of Clinical Pharmacology, 11(1), 27–45. https://doi.org/10.1080/17512433.2018.1386553
  • Zhao, S., Yang, J., & Sawan, M. (2022). Energy-efficient neural network for epileptic seizure prediction. IEEE Transactions on Bio-Medical Engineering, 69(1), 401–411. https://doi.org/10.1109/TBME.2021.3095848
  • Zhou, M., Tian, C., Cao, R., Wang, B., Niu, Y., Hu, T., Guo, H., & Xiang, J. (2018). Epileptic seizure detection based on EEG signals and CNN. Frontiers in Neuroinformatics, 12, 95. https://doi.org/10.3389/fninf.2018.00095

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.