Seizure Detection Via Time Series Classification Using Modified Metaheuristic Optimized Recurrent Networks
DOI:
https://doi.org/10.31181/taci1120238Keywords:
Epilepsy, Seizure, Sinh Cosh Optimizer, Recurrent Neural Networks, HybridizationAbstract
Epilepsy, colloquially termed seizure disorder, constitutes a neurological condi-tion characterized by unpredictable and sudden episodes of heightened electrical activity in the brain. The inherent risks associated with seizures, particularly in their nascent stages, stem from their challenging predictability, often leading to individuals endangering themselves through falls or lapses into unconsciousness in precarious settings. Additionally, the potential for neurological damage further compounds the complexity of managing this condition. This research endeavors to explore the viability of Recurrent Neural Networks (RNNs) for the early detection of seizures using electroencephalogram (EEG) recordings derived from real-world scenarios. Acknowledging the pivotal role of network topology and training parameters in achieving optimal performance, this study employs meta-heuristic algorithms to fine-tune performance through judicious hyperparameter selection. Notably, a modified iteration of a recently introduced algorithm is introduced herein. The application of RNNs to seizure detection yields promising outcomes, with constructed models achieving an accuracy surpassing 99%. The results obtained underscore the potential of employing metaheuristic-optimized RNNs in seizure detection as a means of substantially enhancing the quality of life for individuals afflicted by epilepsy. The demonstrated accuracy of these models suggests a robust and reliable methodology, paving the way for future advancements in the realm of neurotechnological interventions. This investigation contributes valuable insights into the intersection of machine learning, neuroscience, and healthcare, offering a foundation for further research and technological developments aimed at ameliorating the challenges posed by epilepsy.
Downloads
References
Beghi, E. (2020). The epidemiology of epilepsy. Neuroepidemiology, 54(2), 185–191.
Benbadis, S. R., Beniczky, S., Bertram, E., MacIver, S., & Moshe, S. L. (2020). The role of eeg in patients with suspected epilepsy. Epileptic Disorders, 22(2), 143–155.
Noachtar, S., & Remi, J. (2009). The role of eeg in epilepsy: A critical review [Management of Epilepsy: Hope and Hurdles]. Epilepsy & Behavior, 15(1), 22–33.
Benarous, L., Gavaret, M., Diop, M. S., Tobarias, J., de Bourmont, S. d. G., Allez, C., Bouzana, F., Gainnier, M., & Trebuchon, A. (2019). Sources of interrater variability and prognostic value of standardized eeg features in post-anoxic coma after resuscitated cardiac arrest. Clinical neuro-physiology practice, 4, 20–26.
Kaur, T., Diwakar, A., Mirpuri, P., Tripathi, M., Chandra, P. S., Gandhi, T. K., et al. (2021). Artificial intelligence in epilepsy. Neurology India, 69(3), 560.
Jovanovic, L., Bacanin, N., Zivkovic, M., Antonijevic, M., Jovanovic, B., Sretenovic, M. B., & Strumberger, I. (2023). Machine learning tuning by diversity oriented firefly metaheuristics for industry 4.0. Expert Systems, e13293.
Medsker, L. R., & Jain, L. (2001). Recurrent neural networks. Design and Applications, 5(64-67), 2.
Bacanin, N., Stoean, C., Zivkovic, M., Rakic, M., Strulak-Wojcikiewicz, R., & Stoean, R. (2023). On the benefits of using metaheuristics in the hyperparameter tuning of deep learning models for energy load forecasting. Energies, 16(3), 14–34.
Amin, U., & Benbadis, S. R. (2019). The role of eeg in the erroneous diagnosis of epilepsy. Journal of Clinical Neurophysiology, 36(4), 294–297.
Benbadis, S., & Kaplan, P. (2019). The dangers of over-reading an eeg. Journal of Clinical Neurophysiology, 36(4), 249.
Nelson, S. E., & Varelas, P. N. (2018). Status epilepticus, refractory status epilepticus, and superrefractory status epilepticus. Continuum: Lifelong Learning in Neurology, 24(6), 1683–1707.
Kidokoro, H., Yamamoto, H., Kubota, T., Motobayashi, M., Miyamoto, Y., Nakata, T., Takano, K., Shiba, N., Okai, Y., Tanaka, M., et al. (2020). High-amplitude fast activity in eeg: An early diagnostic marker in children with beta-propeller protein-associated neurodegeneration (bpan). Clinical Neurophysiology, 131(9), 2100–2104.
Al-Qazzaz, N. K., Ali, S. H. B., Ahmad, S. A., Chellappan, K., Islam, M. S., Escudero, J., et al. (2014). Role of eeg as biomarker in the early detection and classification of dementia. The Scientific World Journal, 2014.
Stam, C., van Nifterick, A., de Haan, W., & Gouw, A. (2023). Network hyperexcitability in early alzheimer’s disease: Is functional connectivity a potential biomarker? Brain Topography, 1–18.
Kaskie, R. E., & Ferrarelli, F. (2020). Sleep disturbances in schizophrenia: What we know, what still needs to be done. Current opinion in psychology, 34, 68–71.
Ajinkya, S., Fox, J., Houston, P., Greenblatt, A., Lekoubou, A., Lindhorst, S., Cachia, D., Olar, A., & Kutluay, E. (2021). Seizures in patients with metastatic brain tumors: Prevalence, clinical characteristics, and features on eeg. Journal of Clinical Neurophysiology, 38(2), 143–148.
Pilcevic, D., Djuric Jovicic, M., Antonijevic, M., Bacanin, N., Jovanovic, L., Zivkovic, M., Dragovic, M., & Bisevac, P. (n.d.). Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection. Frontiers in Physiology, 14, 1267011.
Sorin, V., Barash, Y., Konen, E., & Klang, E. (2020). Deep learning for natural language processing in radiology—fundamentals and a systematic review. Journal of the American College of Radiology, 17(5), 639–648.
Jelodar, H., Wang, Y., Orji, R., & Huang, S. (2020). Deep sentiment classification and topic discovery on novel coronavirus or covid-19 online discussions: Nlp using lstm recurrent neural network approach. IEEE Journal of Biomedical and Health Informatics, 24(10), 2733–2742.
Zhou, X., Li, Y., & Liang, W. (2020). Cnn-rnn based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(3), 912–921.
Nasir, J. A., Khan, O. S., & Varlamis, I. (2021). Fake news detection: A hybrid cnn-rnn based deep learning approach. International Journal of Information Management Data Insights, 1(1), 100007.
Saheed, Y. K., & Arowolo, M. O. (2021). Efficient cyber attack detection on the internet of medical things-smart environment based on deep recurrent neural network and machine learning algorithms. IEEE Access, 9, 161546–161554.
Rajeev, R., Samath, J. A., & Karthikeyan, N. (2019). An intelligent recurrent neural network with long short-term memory (lstm) based batch normalization for medical image denoising. Journal of medical systems, 43, 1–10.
Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295–316.
Andonie, R. (2019). Hyperparameter optimization in learning systems. Journal of Membrane Computing, 1(4), 279–291.
Mirjalili, S., & Mirjalili, S. (2019). Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications, 43–55.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95-international conference on neural networks, 4, 1942–1948.
Yang, X.-S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International journal of bio-inspired computation, 2(2), 78–84.
Mirjalili, S. (2016). Sca: A sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, 120–133.
Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67–82.
Jovanovic, L., Zivkovic, M., Antonijevic, M., Jovanovic, D., Ivanovic, M., & Jassim, H. S. (2022). An emperor penguin optimizer application for medical diagnostics. 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), 191–196.
Jovanovic, L., Hajdarevic, Z., Jovanovic, D., Jassim, H. S., Strumberger, I., Bacanin, N., Zivkovic, M., & Antonijevic, M. (2022). Tuning extreme learning machine by hybrid planet optimization algorithm for diabetes classification. Congress on Intelligent Systems, 23–36.
Jovanovic, L., Jovanovic, G., Perisic, M., Alimpic, F., Stanisic, S., Bacanin, N., Zivkovic, M., & Stojic, A. (2023). The explainable potential of coupling metaheuristics-optimized-xgboost and shap in revealing vocs’ environmental fate. Atmosphere, 14(1), 109.
Bacanin, N., Jovanovic, L., Zivkovic, M., Kandasamy, V., Antonijevic, M., Deveci, M., & Strumberger, I. (2023). Multivariate energy forecasting via metaheuristic tuned long-short term memory and gated recurrent unit neural networks. Information Sciences, 642, 119122.
Jovanovic, L., Jovanovic, D., Antonijevic, M., Nikolic, B., Bacanin, N., Zivkovic, M., & Strumberger, I. (2023). Improving phishing website detection using a hybrid two-level framework for feature selection and xgboost tuning. Journal of Web Engineering, 22(3), 543–574.
Bai, J., Li, Y., Zheng, M., Khatir, S., Benaissa, B., Abualigah, L., & Wahab, M. A. (2023). A sinh cosh optimizer. Knowledge-Based Systems, 282, 111081.
Jiang, S., Yang, S., Yao, X., Tan, K. C., Kaiser, M., & Krasnogor, N. (2018). Benchmark functions for the cec’2018 competition on dynamic multiobjective optimization (tech. rep.). Newcastle University.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm. Journal of global optimization, 39, 459–471.
Bacanin, N., Budimirovic, N., Venkatachalam, K., Jassim, H. S., Zivkovic, M., Askar, S., & Abouhawwash, M. (2023). Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection. Heliyon, 9(4).
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6), 61–97.
