Prediction and Recognition of Epileptic Seizures Based on Artificial Intelligence
DOI:
https://doi.org/10.54097/gp3axg67Keywords:
Epileptic seizures; machine learning; healthcare; artificial intelligence.Abstract
Repetitive seizures caused by abnormal brain activity are the hallmark of the common neurological illness epilepsy, which has a serious impact on the health of those affected. The introduction of artificial intelligence (AI) in recent years has ushered in novel ways of predicting and recognizing epileptic seizures, transforming the discipline of neurology. In particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Support Vector Machines (SVMs), and Extreme Learning Machines (ELMs) are examined in this research as potential AI approaches for the prediction and identification of epileptic episodes. The underlying working logic of these AI approaches is investigated, emphasizing their distinct strengths and capacities. RNNs and LSTMs are well-known for their ability to describe temporal dependencies in sequential data, whereas SVMs excel in robust classification and ELMs excel at rapid training and real-time prediction. Real-world case studies show how these AI models may be used to analyze electroencephalogram (EEG) data, extract relevant features, and distinguish between seizure and non-seizure states. Furthermore, the essay explores the ramifications of AI in the medical profession, emphasizing the possibility of prompt intervention, improved patient care, and resource optimization. The difficulties and ethical concerns regarding AI in healthcare are also discussed. Finally, this piece highlights AI's promising role in predicting and identifying epileptic seizures, providing a glimpse into the future of personalized, data-driven healthcare. The combination of AI approaches opens the door to more accurate and timely seizure forecasts, increasing the quality of life for those living with epilepsy and revolutionizing the landscape of neurological healthcare.
Downloads
References
Sutskever I, Martens J, Hinton G E. Generating text with recurrent neural networks[C]//Proceedings of the 28th international conference on machine learning (ICML-11). 2011, 1017-1024.
Chen B, Hao Z, Cai X, et al. Embedding logic rules into recurrent neural networks. IEEE Access, 2019, 7: 14938-14946.
Sivakumar M, Uyyala S R. Aspect-based sentiment analysis of mobile phone reviews using LSTM and fuzzy logic. International Journal of Data Science and Analytics, 2021, 12(4): 355-367.
Chowdhary C L, Mittal M, P K, et al. An efficient segmentation and classification system in medical images using intuitionist possibilistic fuzzy C-mean clustering and fuzzy SVM algorithm. Sensors, 2020, 20(14): 3903.
Manoharan J S. Study of variants of extreme learning machine (ELM) brands and its performance measure on classification algorithm. Journal of Soft Computing Paradigm (JSCP), 2021, 3(02): 83-95.
Najafi T, Jaafar R, Remli R, et al. A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy. Sensors, 2022, 22(19): 7269.
Jaffino G, Jose J P, Sundaram M. Grey wolf optimization with deep recurrent neural network for epileptic seizure detection in EEG signals[C]//2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2021: 1-5.
Singh K, Malhotra J. Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features. Complex & Intelligent Systems, 2022, 8(3): 2405-2418.
Saidi A, Othman S B, Saoud S B. A novel epileptic seizure detection system using scalp EEG signals based on hybrid CNN-SVM classifier[C]//2021 IEEE Symposium on Industrial Electronics & Applications (ISIEA). IEEE, 2021, 1-6.
Abbaszadeh B, Haddad T, Yagoub M C E. Probabilistic prediction of Epileptic Seizures using SVM[C]//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2019, 3442-3445.
Harikumar R, Ganesh Babu C, Gowri Shankar M. Extreme learning machine (ELM) based performance analysis and epilepsy identification from EEG signals. IETE Journal of Research, 2021, 1-11.
Zhou J, Zhang X, Jiang Z. Recognition of imbalanced epileptic EEG signals by a graph-based extreme learning machine. Wireless Communications and Mobile Computing, 2021, 1-12.
Downloads
Published
Versions
- 15-12-2023 (2)
- 15-12-2023 (1)
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
