The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks
Open accessAbstract
The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.
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Cites
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Cited by
A review of epileptic seizure detection using machine learning classifiers
Siddiqui, M. K., Morales-Menendez, R., Huang, X., & Hussain, N. (2020) · Brain Informatics · doi:10.1186/s40708-020-00105-1 · Cited by 432
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Riaz, F., Hassan, A., Rehman, S., Niazi, I. K., & Dremstrup, K. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2441835 · Cited by 358
Bhattacharyya, A., Pachori, R., Upadhyay, A., & Acharya, U. (2017) · Applied Sciences · doi:10.3390/app7040385 · Cited by 284
1D-local binary pattern based feature extraction for classification of epileptic EEG signals
Kaya, Y., Uyar, M., Tekin, R., & Yıldırım, S. (2014) · Applied Mathematics and Computation · doi:10.1016/j.amc.2014.05.128 · Cited by 279
Metadata
- Authors
- Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I.
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2007
- Volume
- 2007
- Pages
- 1-13
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- OpenAlex
- W2078760541
- Cited by
- 473
- References
- 39
- FWCI
- 2.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2007). Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2007/80510
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