Page 130 - 2024-Vol20-Issue2
P. 130
126 | Assim & Mahmood
sample entropy,” Computational Intelligence and Neuro- [55] A. H. Shoeb, Application of machine learning to epilep-
science, vol. 2022, 2022. tic seizure onset detection and treatment. PhD thesis,
Massachusetts Institute of Technology, 2009.
[45] Y. Cao, Y. Guo, H. Yu, and X. Yu, “Epileptic seizure
auto-detection using deep learning method,” in 2017 4th [56] R. G. Andrzejak, K. Schindler, and C. Rummel, “Non-
International Conference on Systems and Informatics randomness, nonlinear dependence, and nonstationarity
(ICSAI), pp. 1076–1081, IEEE, 2017. of electroencephalographic recordings from epilepsy pa-
tients,” Physical Review E, vol. 86, no. 4, p. 046206,
[46] H. S. Nogay and H. Adeli, “Detection of epileptic seizure 2012.
using pretrained deep convolutional neural network and
transfer learning,” European neurology, vol. 83, no. 6,
pp. 602–614, 2021.
[47] M. S. Islam, K. Thapa, and S.-H. Yang, “Epileptic-net:
an improved epileptic seizure detection system using
dense convolutional block with attention network from
eeg,” Sensors, vol. 22, no. 3, p. 728, 2022.
[48] X. Tian, Z. Deng, W. Ying, K.-S. Choi, D. Wu, B. Qin,
J. Wang, H. Shen, and S. Wang, “Deep multi-view fea-
ture learning for eeg-based epileptic seizure detection,”
IEEE Transactions on Neural Systems and Rehabilita-
tion Engineering, vol. 27, no. 10, pp. 1962–1972, 2019.
[49] P. Detti, “Siena scalp eeg database,” PhysioNet. doi,
vol. 10, 2020.
[50] K. Das, D. Daschakladar, P. P. Roy, A. Chatterjee, and
S. P. Saha, “Epileptic seizure prediction by the detection
of seizure waveform from the pre-ictal phase of eeg sig-
nal,” Biomedical Signal Processing and Control, vol. 57,
p. 101720, 2020.
[51] G. Choi, C. Park, J. Kim, K. Cho, T.-J. Kim, H. Bae,
K. Min, K.-Y. Jung, and J. Chong, “A novel multi-scale
3d cnn with deep neural network for epileptic seizure
detection,” in 2019 IEEE International Conference on
Consumer Electronics (ICCE), pp. 1–2, IEEE, 2019.
[52] S. Raghu, N. Sriraam, Y. Temel, S. V. Rao, A. S. Hegde,
and P. L. Kubben, “Performance evaluation of dwt based
sigmoid entropy in time and frequency domains for au-
tomated detection of epileptic seizures using svm clas-
sifier,” Computers in biology and medicine, vol. 110,
pp. 127–143, 2019.
[53] N. J. Stevenson, K. Tapani, L. Lauronen, and S. Vanhat-
alo, “A dataset of neonatal eeg recordings with seizure
annotations,” Scientific data, vol. 6, no. 1, pp. 1–8, 2019.
[54] P. Swami, B. Panigrahi, S. Nara, M. Bhatia, and
T. Gandhi, “Eeg epilepsy datasets,” DOI: https://doi.
org/10.13140/RG, vol. 2, no. 14280.32006, 2016.