CISUC - Epileptic seizure prediction based on ratio and differential linear univariate features
CISUC

Epileptic seizure prediction based on ratio and differential linear univariate features

Authors



Related Project

iCIS - Intelligent Computing in the Internet of Services

Journal

Journal of Medical Signals and Sensors, Vol. 5, #1, January 2015

Cited by

Year 2016 : 15 citations

 Gadhoumi, K., Lina, J.M., Mormann, F. and Gotman, J., 2016. Seizure prediction for therapeutic devices: A review. Journal of neuroscience methods, 260, pp.270-282.

 Zhang, Z. and Parhi, K.K., 2016. Low-complexity seizure prediction from iEEG/sEEG using spectral power and ratios of spectral power. IEEE transactions on biomedical circuits and systems, 10(3), pp.693-706.

 Ulate-Campos, A., Coughlin, F., Gaínza-Lein, M., Fernández, I.S., Pearl, P.L. and Loddenkemper, T., 2016. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure, 40, pp.88-101.

 Behnam, M. and Pourghassem, H., 2016. Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search. Computer methods and programs in biomedicine, 132, pp.115-136.

 Zainuddin, Z., Lai, K.H. and Ong, P., 2016. An enhanced harmony search based algorithm for feature selection: Applications in epileptic seizure detection and prediction. Computers & Electrical Engineering, 53, pp.143-162.

 Shiao, H.T., Cherkassky, V., Lee, J., Veber, B., Patterson, N., Brinkmann, B. and Worrell, G., 2016. SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal. IEEE Transactions on Biomedical Engineering.

 Song, Y., Viventi, J. and Wang, Y., Unsupervised Learning of Spike Pattern for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic (?ECoG) Data. (http://vision.poly.edu/papers/technical_report/seizure_tech_report.pdf)

 Song, Y., Viventi, J. and Wang, Y., 2016. Diversity encouraged learning of unsupervised LSTM ensemble for neural activity video prediction. arXiv preprint arXiv:1611.04899.

 Lin, L.C., Chen, S.C.J., Chiang, C.T., Wu, H.C., Yang, R.C. and Ouyang, C.S., 2016. Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features. Clinical EEG and neuroscience, p.1550059416649076.

 Dong, H., Supratak, A., Pan, W., Wu, C., Matthews, P.M. and Guo, Y., 2016. Mixed Neural Network Approach for Temporal Sleep Stage Classification. arXiv preprint arXiv:1610.06421.

 Vinette, S.A., Premji, S., Beers, C.A., Gaxiola-Valdez, I., Pittman, D.J., Slone, E.G., Goodyear, B.G. and Federico, P., 2016. Pre-ictal BOLD alterations: Two cases of patients with focal epilepsy. Epilepsy Research, 127, pp.207-220.

 Cancelli, A., Cottone, C., Tecchio, F., Truong, D.Q., Dmochowski, J. and Bikson, M., 2016. A simple method for EEG guided transcranial electrical stimulation without models. Journal of neural engineering, 13(3), p.036022.

 Khalid, M.I., Aldosari, S.A., Alshebeili, S.A. and Alotaiby, T., 2016, December. Threshold based MEG data classification for healthy and epileptic subjects. In Electronic Devices, Systems and Applications (ICEDSA), 2016 5th International Conference on (pp. 1-3). IEEE.

 Zheng, Y., Wang, G. and Wang, J., 2016. Is Using Threshold-Crossing Method and Single Type of Features Sufficient to Achieve Realistic Application of Seizure Prediction?. Clinical EEG and neuroscience, 47(4), pp.305-316.

 Supratak, A., Wu, C., Dong, H., Sun, K. and Guo, Y., 2016. Survey on Feature Extraction and Applications of Biosignals. In Machine Learning for Health Informatics (pp. 161-182). Springer International Publishing.

Year 2015 : 7 citations

 Zhang, Z. and Parhi, K.K., 2015, August. Seizure prediction using polynomial SVM classification. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 5748-5751). IEEE.

 Zhang, Z. and Parhi, K.K., 2015, August. Seizure detection using regression tree based feature selection and polynomial SVM classification. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 6578-6581). IEEE.

 Zhang, Z., Henry, T.R. and Parhi, K.K., 2015, November. Seizure prediction using cross-correlation and classification. In Signals, Systems and Computers, 2015 49th Asilomar Conference on (pp. 775-779). IEEE.

 Panichev, O., Popov, A. and Kharytonov, V., 2015, June. Patient-specific epileptic seizure prediction using correlation features. In Signal Processing Symposium (SPSympo), 2015 (pp. 1-5). IEEE.

 Village, D., 2015. 2015 Signal Processing Symposium (SPSympo).

 Tewolde, S., Oommen, K., Lie, D.Y., Zhang, Y. and Chyu, M.C., 2015. Epileptic Seizure Detection and Prediction Based on Continuous Cerebral Blood Flow Monitoring–a Review. Journal of healthcare engineering, 6(2), pp.159-178.

 Khalid, M.I., Aldosari, S.A., Alshebeili, S.A. and Alotaiby, T., 2015, December. Enhancing the reliability of epileptic seizure alarms for scalp EEG signals. In Signal and Information Processing (GlobalSIP), 2015 IEEE Global Conference on (pp. 1302-1306). IEEE.