The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection
Abstract
A novel principal component analysis (PCA)-enhanced cosine radial basis function neural network classifier is presented. The two-stage classifier is integrated with the mixed-band wavelet-chaos methodology, developed earlier by the authors, for accurate and robust classification of electroencephalogram (EEGs) into healthy, ictal, and interictal EEGs. A nine-parameter mixed-band feature space discovered in previous research for effective EEG representation is used as input to the two-stage classifier. In the first stage, PCA is employed for feature enhancement. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network (RBFNN) employed in the second stage significantly. The classification accuracy and robustness of the classifier are validated by extensive parametric and sensitivity analysis. The new wavelet-chaos-neural network methodology yields high EEG classification accuracy (96.6%) and is quite robust to changes in training data with a low standard deviation of 1.4%. For epilepsy diagnosis, when only normal and interictal EEGs are considered, the classification accuracy of the proposed model is 99.3%. This statistic is especially remarkable because even the most highly trained neurologists do not appear to be able to detect interictal EEGs more than 80% of the times.
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Cites
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001) · Physical Review E · doi:10.1103/physreve.64.061907 · Cited by 3,032
Analysis of EEG records in an epileptic patient using wavelet transform
Adeli, H., Zhou, Z., & Dadmehr, N. (2003) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(02)00340-0 · Cited by 1,154
Self-citationA Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy
Adeli, H., Ghosh-Dastidar, S., & Dadmehr, N. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.886855 · Cited by 686
Self-citationRecurrent neural networks employing Lyapunov exponents for EEG signals classification
GULER, N., UBEYLI, E., & GULER, I. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.04.011 · Cited by 503
Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection
Ghosh-Dastidar, S., Adeli, H., & Dadmehr, N. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.891945 · Cited by 470
Self-citationEpileptic seizure prediction and control
Iasemidis, L. D. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.810705 · Cited by 363
Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG
Petrosian, A., Prokhorov, D., Homan, R., Dasheiff, R., & Wunsch, D. (2000) · Neurocomputing · doi:10.1016/s0925-2312(99)00126-5 · Cited by 269
Seizure prediction by non‐linear time series analysis of brain electrical activity
Elger, C. E., & Lehnertz, K. (1998) · European Journal of Neuroscience · doi:10.1046/j.1460-9568.1998.00090.x · Cited by 263
Automatic Detection of Seizures and Spikes
Gotman, J. (1999) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-199903000-00005 · Cited by 232
Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection
Adeli, H., & Karim, A. (2000) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(2000)126:6(464 · Cited by 224
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Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.017 · Cited by 1,647
Self-citationAI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713
Automated EEG-based screening of depression using deep convolutional neural network
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.012 · Cited by 650
Self-citationAn electroencephalographic signature predicts antidepressant response in major depression
Wu, W., Zhang, Y., Jiang, J., Lucas, M. V., Fonzo, G. A., Rolle, C. E., Cooper, C., Chin-Fatt, C., Krepel, N., Cornelssen, C. A., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0397-3 · Cited by 302
Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
Raghavendra, U., Acharya, U. R., & Adeli, H. (2019) · European Neurology · doi:10.1159/000504292 · Cited by 174
Self-citationImproving human activity recognition and its application in early stroke diagnosis
Villar, J. R., González, S., Sedano, J., Chira, C., & Trejo-Gabriel-Galan, J. M. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065714500361 · Cited by 75
Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN
Chen, X., Zheng, Y., Niu, Y., & Li, C. (2020) · 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl51233.2020.00050 · Cited by 34
Metadata
- Authors
- Ghosh-Dastidar, S., Adeli, H., & Dadmehr, N.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2008-02
- Volume
- 55
- Issue
- 2
- Pages
- 512-518
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2122923860
- Cited by
- 466
- References
- 14
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Open access
- closed
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