The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
Open accessAbstract
Electroencephalography (EEG) is an important tool for studying the human brain activity and epileptic processes in particular. EEG signals provide important information about epileptogenic networks that must be analyzed and understood before the initiation of therapeutic procedures. Very small variations in EEG signals depict a definite type of brain abnormality. The challenge is to design and develop signal processing algorithms which extract this subtle information and use it for diagnosis, monitoring and treatment of patients with epilepsy. This paper presents a review of wavelet techniques for computer-aided seizure detection and epilepsy diagnosis with an emphasis on research reported during the past decade. A multiparadigm approach based on the integration of wavelets, nonlinear dynamics and chaos theory, and neural networks advanced by Adeli and associates is the most effective method for automated EEG-based diagnosis of epilepsy.
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The dual-tree complex wavelet transform
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EEG signal classification using PCA, ICA, LDA and support vector machines
Subasi, A., & Ismail Gursoy, M. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.06.065 · Cited by 1,209
EEG signal classification using wavelet feature extraction and a mixture of expert model.
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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
Self-citationGHOSH-DASTIDAR, S., & ADELI, H. (2009) · International Journal of Neural Systems · doi:10.1142/s0129065709002002 · Cited by 1,057
Self-citationWavelet transforms and the ECG: a review
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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-citationKhosla, A., Khandnor, P., & Chand, T. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2020.02.002 · Cited by 267
A Multi-View Deep Learning Framework for EEG Seizure Detection
Yuan, Y., Xun, G., Jia, K., & Zhang, A. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2871678 · Cited by 258
EEG-Based Seizure detection using linear graph convolution network with focal loss
Zhao, Y., Dong, C., Zhang, G., Wang, Y., Chen, X., Jia, W., Yuan, Q., Xu, F., & Zheng, Y. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106277 · Cited by 105
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
- Faust, O., Acharya, U. R., Adeli, H., & Adeli, A.
- Venue
- Seizure
- Publisher
- Elsevier BV
- Published
- 2015-03
- Volume
- 26
- Pages
- 56-64
- ISSN
- 1059-1311 (ISSN-L)
- OpenAlex
- W2043596210
- Cited by
- 572
- References
- 92
- FWCI
- 27.29
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
