The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection
Abstract
A novel wavelet-chaos-neural network methodology is presented for classification of electroencephalograms (EEGs) into healthy, ictal, and interictal EEGs. Wavelet analysis is used to decompose the EEG into delta, theta, alpha, beta, and gamma sub-bands. Three parameters are employed for EEG representation: standard deviation (quantifying the signal variance), correlation dimension, and largest Lyapunov exponent (quantifying the non-linear chaotic dynamics of the signal). The classification accuracies of the following techniques are compared: (1) unsupervised k-means clustering; (2) linear and quadratic discriminant analysis; (3) radial basis function neural network; (4) Levenberg-Marquardt backpropagation neural network (LMBPNN). To reduce the computing time and output analysis, the research was performed in two phases: band-specific analysis and mixed-band analysis. In phase two, over 500 different combinations of mixed-band feature spaces consisting of promising parameters from phase one of the research were investigated. It is concluded that all three key components of the wavelet-chaos-neural network methodology are important for improving the EEG classification accuracy. Judicious combinations of parameters and classifiers are needed to accurately discriminate between the three types of EEGs. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy, a high value of 96.7%.
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Cites
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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
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A 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-citationHagan (2008) · Cited by 657
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Adaptive epileptic seizure prediction system
Iasemidis, L. D., Shiau, D.-S., Chaovalitwongse, W., Sackellares, J. C., Pardalos, P. M., Principe, J. C., Carney, P. R., Prasad, A., Veeramani, B., & Tsakalis, K. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.810689 · Cited by 418
Epileptic seizure prediction and control
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Cited by
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-citationGhosh-Dastidar, S., Adeli, H., & Dadmehr, N. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.905490 · Cited by 466
Self-citationSong, Y., & Liò, P. (2010) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2010.36078 · Cited by 191
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
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Metadata
- Authors
- Ghosh-Dastidar, S., Adeli, H., & Dadmehr, N.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2007-09
- Volume
- 54
- Issue
- 9
- Pages
- 1545-1551
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2113894235
- Cited by
- 470
- References
- 18
- FWCI
- 9.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ghosh-Dastidar, S., Adeli, H., & Dadmehr, N. (2007). Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2007.891945
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
