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%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hagan, M., Demuth, H. B., Beale, M. H., & Jes?s, O. D. (2016) · Institutional Repository of the National University Open and Distance UNAD (Universidad Nacional Abierta y a Distancia) · Cited by 5,133
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
Multivariate Analysis: Methods and Applications
Dillon, W. R., & Goldstein, M. (1984) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,905
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-citationMultivariate Analysis-Methods and Applications.
Cranley, R., Dilon, W. R., & Goldstein, M. (1985) · Applied Statistics · doi:10.2307/2347476 · Cited by 942
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
Neural Network Fundamentals with Graphs, Algorithms and Applications
Bose, N. K., & Liang, P. (1995) · Cited by 589
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
Iasemidis, L. D. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.810705 · Cited by 363
Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems
Adeli, H., & Hung, S. (1994) · Medical Entomology and Zoology · Cited by 322
Self-citationRecurrent 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
Measuring Chaos in the Human Brain: Proceedings of the Conference
Duke, D. W., & Pritchard, W. S. (1991) · doi:10.1142/9789814538688 · Cited by 149
Wavelet‐Clustering‐Neural Network Model for Freeway Incident Detection
Ghosh‐Dastidar, S., & Adeli, H. (2003) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/1467-8667.t01-1-00311 · Cited by 147
Self-citationFrom wavelets to adaptive approximations: time-frequency parametrization of EEG
Durka, P. J. (2003) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-2-1 · Cited by 146
Time dependencies in the occurrences of epileptic seizures
Iasemidis, L. D., Olson, L. D., Savit, R. S., & Sackellares, J. C. (1994) · Epilepsy Research · doi:10.1016/0920-1211(94)90081-7 · Cited by 104
Dynamics of local neuronal networks: control parameters and state bifurcations in epileptogenesis
Lopes, F. H. D. S., Pijn, J. P. M., & Wadman, W. J. (1994) · Progress in brain research · doi:10.1016/s0079-6123(08)60552-x · Cited by 79
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
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
