• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 25, Pages: 1898-1909

Original Article

Machine Learning-Driven Robust Optimization of Communication Signals in Sensor Wearable Devices for Early Stage Epilepsy Seizure Prediction using EPCA

Received Date:28 May 2023, Accepted Date:10 June 2023, Published Date:28 June 2023


Objectives: To introduce a novel EEG signal optimization and epilepsy seizure detection method at an early stage with the aid of sensor wearable devices in order to treat the epilepsy in advance. For effective optimization and to boost the detection accuracy, the ROCS-EDS (Robust Optimization of Communication Signals for Early Detection of Seizures) technique is employed with EPCA (Enhanced Principal Component Analysis). Methods: EEG signal optimization, feature selection, and extraction such as time-domain, frequency-domain, time-frequency, movements, and statistical features are done by EPCA. IQAM and CPSK techniques are utilized for data transfer and reliability. The BCI CIV (Brain Computer Interface Dataset from California) dataset is used in this study with a record of 4922 patients, a 400 Hz sample rate, 4 target classes, and 2 subjects. LD parity check, fast Fourier, and wavelet transform methods are deployed to track signals and optimize them at frequent intervals. For filtering the signals and reliability rate monitoring, the MMSEF error filtering method is employed. MATLAB software is used to measure the performance of the suggested model with baseline models such as VPSOGA-SVM, 2L-LSTM, and IoMT-ESD. Findings: With an accuracy rate of 98%, 96% and 97% sensitivity and specificity, a 98% F-score, 98.65% and 96.80% precision and recall, and 97% and 91% TPR and TNR, 5.46% and 7.10% FPR and FNR, ROCS-EDS outperforms baseline models in terms of signal optimization and seizure detection at an early stage. The method is deployed and tested in MF2Epi-alert wearable device for seizure activity. Novelty: The novel hybrid model ROCS-EDS with EPCA shown evident results and has capability in optimizing EEG signals and detect ES accurately at an early stage which helps the clinical experts to treat the epilepsy in advance. ROCS-EDS outperform the currently prevailing methods VPSOGASVM (1), 2L-LSTM (2), and IoMT-ESD (3).

Keywords: Epilepsy Seizure Detection; Machine Learning; Deep Learning; PCA; EEG Signals; Signal Optimization; Data Mining


  1. 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. Available from: https://doi.org/10.1007/s40747-021-00627-z
  2. Al-Hajjar ALN, Al-Qurabat AKM. An overview of machine learning methods in enabling IoMT-based epileptic seizure detection. The Journal of Supercomputing. 2023;11:1–19. Available from: https://doi.org/10.1007/s11227-023-05299-9
  3. Natu M, Bachute M, Gite S, Kotecha K, Vidyarthi A. Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches. Computational and Mathematical Methods in Medicine. 2022;2022:1–17. Available from: https://doi.org/10.1155/2022/7751263
  4. Xu X, Zhang Y, Zhang R, Xu T. Patient-specific method for predicting epileptic seizures based on DRSN-GRU. Biomedical Signal Processing and Control. 2023;81:104449. Available from: https://doi.org/10.1016/j.bspc.2022.104449
  5. Emara HM, Elwekeil M, Taha TE, El-Fishawy AS, El-Rabaie ESM, El-Shafai W, et al. Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction. Annals of Data Science. 2022;9(2):393–428. Available from: https://doi.org/10.1007/s40745-020-00308-7
  6. Kapoor B, Nagpal B, Jain PK, Abraham A, Gabralla LA. Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals. Sensors. 2023;23(1):423. Available from: https://doi.org/10.3390/s23010423
  7. Zhang Q, Ding J, Kong W, Liu Y, Wang Q, Jiang T. Epilepsy prediction through optimized multidimensional sample entropy and Bi-LSTM. Biomedical Signal Processing and Control. 2021;64:102293. Available from: https://doi.org/10.1016/j.bspc.2020.102293
  8. Jing J, Pang X, Pan Z, Fan F, Meng Z. Classification and identification of epileptic EEG signals based on signal enhancement. Biomedical Signal Processing and Control. 2022;71:103248. Available from: https://doi.org/10.1016/j.bspc.2021.103248
  9. Al-Hajjar ALN, Al-Qurabat AKM. An overview of machine learning methods in enabling IoMT-based epileptic seizure detection. The Journal of Supercomputing. 2023. Available from: https://doi.org/10.1007/s11227-023-05299-9
  10. Gore ER, Mohini, Rathi. Biomedical Deep Model for Classification of ‘Clinical Correction Requiring Patient Detection. EEG Signals & Alzheimer's Disease, SSRN Journals. 2022;18:1–10. Available from: http://dx.doi.org/10.2139/ssrn.4116076
  11. Ali I, Zhuanghanqi K, Emmanuelle T, Muhamed A, Ali E. Epileptic seizure prediction based on multiresolution convolutional neural networks. Frontiers in Signal Processing. 2023;3:1175305. Available from: https://www.frontiersin.org/articles/10.3389/frsip.2023
  12. Nithyanandh S, Jaiganesh V. Dynamic Link Failure Detection using Robust Virus Swarm Routing Protocol in Wireless Sensor Network. International Journal of Recent Technology and Engineering. 2020;8(2):1574–1578. Available from: https://doi.org/10.35940/ijrte.b2271.078219
  13. Pinto MF, Leal A, Lopes F, Dourado A, Martins P, Teixeira CA. A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction. Scientific Reports. 2021;11(1). Available from: https://doi.org/10.1038/s41598-021-82828-7
  14. Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R, et al. An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works. Computers in Biology and Medicine. 2022;149:106053. Available from: https://doi.org/10.1016/j.compbiomed.2022.106053
  15. Ren Z, Han X, Wang BX. The performance evaluation of the state-of-the-art EEG-based seizure prediction models. Frontiers in Neurology. 2022;13:1016224. Available from: https://doi.org/10.3389/fneur.2022.1016224
  16. El-Gindy SAEA, Hamad A, El-Shafai W, Khalaf AAM, El-Dolil SM, Taha TE, et al. Efficient communication and EEG signal classification in wavelet domain for epilepsy patients. Journal of Ambient Intelligence and Humanized Computing. 2021;12(10):9193–9208. Available from: https://www.researchgate.net/publication/344155662_Efficient_Communication_and_Classification_in_Wavelet_Domain_for_Epileptic_Patients
  17. Jagadesh T, Reethika A, Jaishankar B, Kanivarshini MS. Early Prediction of Epileptic Seizure Using Deep Learning Algorithm. Brain-Computer Interface. 2023;20:157–177. Available from: https://doi.org/10.1002/9781119857655.ch7
  18. Zambrana-Vinaroz D, Vicente-Samper JM, Manrique-Cordoba J, Sabater-Navarro JM. Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals. Sensors. 2022;22(23):9372. Available from: https://doi.org/10.3390/s22239372
  19. Kaur T, Diwakar A, Kirandeep, Mirpuri P, Tripathi M, Chandra PS, et al. Artificial Intelligence in Epilepsy. Neurol India. 2021;69:560–566. Available from: https://doi.org/10.4103/0028-3886.317233
  20. Rahman AA, Faisal F, Nishat MM, Siraji MI, Khalid LI, Khan MRH, et al. Detection of Epileptic Seizure from EEG Signal Data by Employing Machine Learning Algorithms with Hyperparameter Optimization. 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART). 2021. Available from: https://doi.org/10.1109/BioSMART54244.2021.9677770


© 2023 Banupriya & Kowsalya. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.