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Multiclass classification of epileptic seizure phases using a novel HFO-based feature extraction model

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Abstract

Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal neuronal activity in the brain. It can manifest at any age, from childhood to adulthood, with symptoms varying significantly among individuals. Common symptoms include muscle spasms, loss of consciousness, and fainting, which can profoundly impact daily life and routine activities. While epilepsy may persist throughout life, appropriate treatment and medication can often control seizures, enabling most individuals to lead normal lives. Electroencephalography (EEG) signals are a crucial tool for monitoring epileptic seizures and analyzing their underlying patterns. In this study, a novel feature extraction model based on high-frequency oscillations (HFOs) is proposed for the detection and classification of epileptic seizures using intracranial EEG signals. Unlike conventional seizure detection approaches frequently reported in the literature, this study specifically focuses on identifying distinct seizure phases. Experimental results indicate that the proposed model achieved an accuracy of 77% for HFO-based features and 83% for common features in a multi-class classification task using the Random Forest classifier. Notably, HFO-based features demonstrated robust and consistent performance across classifiers, underscoring their potential as reliable markers for seizure phase detection. Additionally, phase-level analysis revealed that the prodromal phase exhibited the highest detection accuracy, suggesting its utility for early seizure prediction, while the postictal phase showed the lowest accuracy due to structural similarities with other phases. These findings emphasize the feasibility of automatically identifying seizure phases, offering valuable insights and a solid foundation for future research in epilepsy diagnosis and management.

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References

  1. Diykh, M., Li, Y., Wen, P.: EEG sleep stages classification based on time domain features and structural graph similarity. IEEE Trans. Neural Syst. Rehabil. Eng. 24(11), 1159–1168 (2016)

    Article  MATH  Google Scholar 

  2. Harpale, V.K., Bairagi, V.K.: Time and frequency domain analysis of EEG signals for seizure detection: a review. In: 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), pp. 1–6. IEEE (2016)

  3. Hernández, D., Trujillo, L., Z-Flores, E., Villanueva, O., Romo-Fewell, O.: Detecting epilepsy in eeg signals using time, frequency and time-frequency domain features. In: Computer science and engineering-theory and applications, pp. 167–182 (2018)

  4. Thangarajoo, R.G., Reaz, M.B.I., Srivastava, G., Haque, F., Ali, S.H.M., Bakar, A.A.A., Bhuiyan, M.A.S.: Machine learning-based epileptic seizure detection methods using wavelet and EMD-based decomposition techniques: A review. Sensors 21(24), 8485 (2021)

    Article  Google Scholar 

  5. Logesparan, L., Rodriguez-Villegas, E., Casson, A.J.: The impact of signal normalization on seizure detection using line length features. Med. Biol. Eng. Comput. 53, 929–942 (2015)

    Article  MATH  Google Scholar 

  6. Zeiler, M.: Visualizing and understanding convolutional networks. In: European Conference on Computer vision/arXiv, vol. 1311 (2014)

  7. Zhang, Y., Zhang, Y., Wang, J., Zheng, X.: Comparison of classification methods on EEG signals based on wavelet packet decomposition. Neural Comput. Appl. 26, 1217–1225 (2015)

    Article  MATH  Google Scholar 

  8. Chen, S., Zhang, X., Chen, L., Yang, Z.: Automatic diagnosis of epileptic seizure in electroencephalography signals using nonlinear dynamics features. IEEE Access 7, 61046–61056 (2019)

    Article  MATH  Google Scholar 

  9. Mursalin, M., Islam, S.S., Noman, M.K., Al-Jumaily, A.A.: Epileptic seizure classification using statistical sampling and a novel feature selection algorithm. arXiv preprint arXiv:1902.09962 (2019)

  10. Yu, D., Deng, L.: Deep learning and its applications to signal and information processing [exploratory dsp]. IEEE Signal Process. Mag. 28(1), 145–154 (2010)

    Article  MATH  Google Scholar 

  11. Kurgan, L.A., Cios, K.J.: Caim discretization algorithm. IEEE Trans. Knowl. Data Eng. 16(2), 145–153 (2004)

    Article  MATH  Google Scholar 

  12. Hussein, R., Palangi, H., Ward, R.K., Wang, Z.J.: Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals. Clin. Neurophysiol. 130(1), 25–37 (2019)

    Article  MATH  Google Scholar 

  13. Talathi, S.S., Vartak, A.: Improving performance of recurrent neural network with relu nonlinearity. arXiv preprint arXiv:1511.03771 (2015)

  14. Ahmedt-Aristizabal, D., Fernando, T., Denman, S., Robinson, J.E., Sridharan, S., Johnston, P.J., Laurens, K.R., Fookes, C.: Identification of children at risk of schizophrenia via deep learning and EEG responses. IEEE J. Biomed. Health Inform. 25(1), 69–76 (2020)

    Article  Google Scholar 

  15. Yao, X., Cheng, Q., Zhang, G.-Q.: Automated classification of seizures against nonseizures: a deep learning approach. arXiv preprint arXiv:1906.02745 (2019)

  16. Liang, W., Pei, H., Cai, Q., Wang, Y.: Scalp EEG epileptogenic zone recognition and localization based on long-term recurrent convolutional network. Neurocomputing 396, 569–576 (2020)

    Article  Google Scholar 

  17. Roy, S., Kiral-Kornek, I., Harrer, S.: Deep learning enabled automatic abnormal EEG identification. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2756–2759 (2018). IEEE

  18. Choi, G., Park, C., Kim, J., Cho, K., Kim, T.-J., Bae, H., Min, K., Jung, K.-Y., Chong, J.: 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 (2019). IEEE

  19. Zhou, M., Tian, C., Cao, R., Wang, B., Niu, Y., Hu, T., Guo, H., Xiang, J.: Epileptic seizure detection based on EEG signals and CNN. Front. Neuroinform. 12, 95 (2018)

    Article  Google Scholar 

  20. Shoka, A.A.E., Dessouky, M.M., El-Sayed, A., Hemdan, E.E.-D.: An efficient CNN based epileptic seizures detection framework using encrypted EEG signals for secure telemedicine applications. Alex. Eng. J. 65, 399–412 (2023)

    Article  Google Scholar 

  21. Naseem, S., Javed, K., Khan, M.J., Rubab, S., Khan, M.A., Nam, Y.: Integrated cwt-CNN for epilepsy detection using multiclass EEG dataset. Comput., Mater. Contin. 69(1), 471–486 (2021)

    MATH  Google Scholar 

  22. Gliske, S.V., Irwin, Z.T., Chestek, C., Stacey, W.C.: Effect of sampling rate and filter settings on high frequency oscillation detections. Clin. Neurophysiol. 127(9), 3042–3050 (2016)

    Article  Google Scholar 

  23. Huang, L., Ni, X., Ditto, W.L., Spano, M., Carney, P.R., Lai, Y.-C.: Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis. Royal Soc. Open Sci. 4(1), 160741 (2017)

    Article  Google Scholar 

  24. Rosmalen, F.: Automatic detection of the HFO zone in epilepsy using magnetoencephalography. Master’s thesis, University of Twente (2015)

  25. Chaitanya, G., Sinha, S., Narayanan, M., Satishchandra, P.: Scalp high frequency oscillations (hfos) in absence epilepsy: an independent component analysis (ica) based approach. Epilepsy Res. 115, 133–140 (2015)

    Article  Google Scholar 

  26. Papadelis, C., Tamilia, E., Stufflebeam, S., Grant, P.E., Madsen, J.R., Pearl, P.L., Tanaka, N.: Interictal high frequency oscillations detected with simultaneous magnetoencephalography and electroencephalography as biomarker of pediatric epilepsy. J. Vis. Exp.: JoVE 118, 54883 (2016)

    Google Scholar 

  27. Kocadagli, O., Ozer, E., Batista, A.G.: Preictal phase detection on EEG signals using hybridized machine learning classifiers with a novel feature selection procedure based gas and icomp. Expert Syst. Appl. 212, 118825 (2023)

    Article  MATH  Google Scholar 

  28. Chapatwala, N., Paunwala, C.N., Doctor, A.: Svm-based pre-ictal phase detection for epileptic seizure analysis. In: 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC), pp. 284–289 (2023). IEEE

  29. Liu, S., Wang, J., Li, S., Cai, L.: Epileptic seizure detection and prediction in EEGs using power spectra density parameterization. IEEE Trans. Neural Syst. Rehabilit. Eng. 31, 3884–3894 (2023)

    Article  MATH  Google Scholar 

  30. Jiang, X., Liu, X., Liu, Y., Wang, Q., Li, B., Zhang, L.: Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis. Front. Neurosci. 17, 1191683 (2023)

    Article  MATH  Google Scholar 

  31. Zeng, W., Shan, L., Su, B., Du, S.: Epileptic seizure detection with deep EEG features by convolutional neural network and shallow classifiers. Front. Neurosci. 17, 1145526 (2023)

    Article  Google Scholar 

  32. Lu, X., Zhang, J., Huang, S., Wang, T., Wang, M., Ye, M.: Nonlinear analysis and recognition of epileptic EEG signals in different stages. J. Neurophysiol. 132(3), 685–694 (2024)

    Article  MATH  Google Scholar 

  33. Dutta, K.K., Manohar, P., Krishnappa, I.: Seizure stage detection of epileptic seizure using convolutional neural networks. Int. J. Electr. Comput. Eng. (IJECE) 14(2), 2226–2233 (2024)

    Article  MATH  Google Scholar 

  34. Zhang, J., Zheng, S., Chen, W., Du, G., Fu, Q., Jiang, H.: A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction. Sci. Rep. 14(1), 16916 (2024)

    Article  Google Scholar 

  35. Feizbakhsh, B., Omranpour, H.: Cluster-based phase space density feature in multichannel scalp EEG for seizure prediction by deep learning. Biomed. Signal Process. Control 86, 105276 (2023)

  36. Bhattacharya, S., Bennett, A., Kriukova, K., Alba, C., Duncan, D.: A machine learning approach to preictal phase detection in EEG (2024). Preprints. https://doi.org/10.20944/preprints202402.1441.v1

  37. Mera-Gaona, M., Lopez, D.M., Vargas-Canas, R.: An ensemble feature selection approach to identify relevant features from EEG signals. Appl. Sci. 11(15), 6983 (2021)

    Article  MATH  Google Scholar 

  38. Ocak, H.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36(2), 2027–2036 (2009)

    Article  MATH  Google Scholar 

  39. Jiang, D., Yu, M., Yuanyuan, W.: Sleep stage classification using covariance features of multi-channel physiological signals on riemannian manifolds. Comput. Methods Programs Biomed. 178, 19–30 (2019)

    Article  MATH  Google Scholar 

  40. Boonyakitanont, P., Lek-Uthai, A., Chomtho, K., Songsiri, J.: A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed. Signal Process. Control 57, 101702 (2020)

    Article  Google Scholar 

  41. Jacobs, J., Staba, R., Asano, E., Otsubo, H., Wu, J., Zijlmans, M., Mohamed, I., Kahane, P., Dubeau, F., Navarro, V., et al.: High-frequency oscillations (hfos) in clinical epilepsy. Prog. Neurobiol. 98(3), 302–315 (2012)

    Article  Google Scholar 

  42. Zijlmans, M., Jiruska, P., Zelmann, R., Leijten, F.S., Jefferys, J.G., Gotman, J.: High-frequency oscillations as a new biomarker in epilepsy. Ann. Neurol. 71(2), 169–178 (2012)

    Article  Google Scholar 

  43. Bragin, A., Engel, J., Jr., Wilson, C.L., Fried, I., Buzsaki, G.: High-frequency oscillations in human brain. Hippocampus 9(2), 137–142 (1999)

    Article  MATH  Google Scholar 

  44. Benar, C.G., Chauviere, L., Bartolomei, F., Wendling, F.: Pitfalls of high-pass filtering for detecting epileptic oscillations: a technical note on false ripples. Clin. Neurophysiol. 121(3), 301–310 (2010)

    Article  MATH  Google Scholar 

  45. Staba, R.J., Wilson, C.L., Bragin, A., Fried, I., Engel, J., Jr.: Quantitative analysis of high-frequency oscillations (80–500 hz) recorded in human epileptic hippocampus and entorhinal cortex. J. Neurophysiol. 88(4), 1743–1752 (2002)

    Article  Google Scholar 

  46. Worrell, G.A., Gardner, A.B., Stead, S.M., Hu, S., Goerss, S., Cascino, G.J., Meyer, F.B., Marsh, R., Litt, B.: High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings. Brain 131(4), 928–937 (2008)

    Article  Google Scholar 

  47. Zhao, X., Zhao, Q., Tanaka, T., Sole-Casals, J., Zhou, G., Mitsuhashi, T., Sugano, H., Yoshida, N., Cao, J.: Classification of the epileptic seizure onset zone based on partial annotation. Cogn. Neurodyn. 17(3), 703–713 (2023)

    Article  Google Scholar 

  48. Sicardi Rosell, I.L.: Detection of pathological HFO using supervised machine learning and IEEG data. PhD thesis, Technological University Dublin (2020)

  49. Migliorelli, C., Bachiller, A., Alonso, J.F., Romero, S., Aparicio, J., Van Jacobs-Le, J., Mananas, M.A., San Antonio-Arce, V.: SGM: a novel time-frequency algorithm based on unsupervised learning improves high-frequency oscillation detection in epilepsy. J. Neural Eng. 17(2), 026032 (2020)

    Article  Google Scholar 

  50. Kumar, G., Chander, S., Almadhor, A.: An intelligent epilepsy seizure detection system using adaptive mode decomposition of EEG signals. Phys. Eng. Sci. Med. 45(1), 261–272 (2022)

    Article  MATH  Google Scholar 

  51. Bandarabadi, M., Teixeira, C.A., Rasekhi, J., Dourado, A.: Epileptic seizure prediction using relative spectral power features. Clin. Neurophysiol. 126(2), 237–248 (2015)

    Article  Google Scholar 

  52. Aarabi, A., He, B.: Seizure prediction in patients with focal hippocampal epilepsy. Clin. Neurophysiol. 128(7), 1299–1307 (2017)

    Article  MATH  Google Scholar 

  53. Wan, X., Fang, Z., Wu, M., Du, Y.: Automatic detection of HFOs based on singular value decomposition and improved fuzzy c-means clustering for localization of seizure onset zones. Neurocomputing 400, 1–10 (2020)

  54. Sadek, Z., Hadriche, A., Jmail, N.: Clustering of high frequency oscillations HFO in epilepsy using pretrained neural networks. In: International Conference on Intelligent Systems Design and Applications, pp. 100–107 (2022). Springer

  55. Broti, N.M., Sawada, M., Takayama, Y., Iwasaki, M., Ono, Y.: Detection of high-frequency biomarker signals of epilepsy by combined deep-learning feature selection and linear discrimination analysis. In: Proceedings of the 37th National Conference of the Japanese Society for Artificial Intelligence(2023), pp. 1–51803151803 (2023). Japanese Society for Artificial Intelligence

  56. Rafik, D., Larbi, B.: Autoregressive modeling based empirical mode decomposition (EMD) for epileptic seizures detection using EEG signals. Traitement du Signal 36(3), 273–279 (2019)

    Article  MATH  Google Scholar 

  57. Ahmad, I., Wang, X., Javeed, D., Kumar, P., Samuel, O.W., Chen, S.: A hybrid deep learning approach for epileptic seizure detection in EEG signals. IEEE J. Biomed. Health Inf (2023). https://doi.org/10.1109/JBHI.2023.3265983

    Article  MATH  Google Scholar 

  58. Qiu, X., Yan, F., Liu, H.: A difference attention resnet-lstm network for epileptic seizure detection using EEG signal. Biomed. Signal Process. Control 83, 104652 (2023)

    Article  Google Scholar 

  59. Singh, M., Jagyasi, G., Garg, M., Jain, A., Gupta, N., Kumar, K., Kumar, A.: A machine learning framework for robust epileptic seizure detection from EEG sign. In: 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), pp. 1–4 (2023). IEEE

  60. Anita, M., Kowshalya, A.M.: Automatic epileptic seizure detection using MSA-DCNN and LSTM techniques with EEG signals. Expert Syst. Appl. 238, 121727 (2024)

    Article  Google Scholar 

  61. Ahmad, I., Yao, C., Li, L., Chen, Y., Liu, Z., Ullah, I., Shabaz, M., Wang, X., Huang, K., Li, G., et al.: An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection. J. Inf. Secur. Appl. 80, 103654 (2024)

    Google Scholar 

  62. Wang, Y., Yuan, S., Liu, J.-X., et al.: Combining EEG features and convolutional autoencoder for neonatal seizure detection. Int. J. Neural Syst. 34(8), 2450040 (2024). https://doi.org/10.1142/s0129065724500400

  63. Wu, M., Peng, H., Liu, Z., Wang, J.: Seizure detection of EEG signals based on multi-channel long-and short-term memory-like spiking neural model. Int. J. Neural Syst. 34(10), 2450051 (2024)

    Article  MATH  Google Scholar 

  64. Wang, Z., Sperling, M.R., Wyeth, D., Guez, A.: Automated seizure detection based on state-space model identification. Sensors 24(6), 1902 (2024)

  65. Urbina Fredes, S., Dehghan Firoozabadi, A., Adasme, P., Zabala-Blanco, D., Palacios Játiva, P., Azurdia-Meza, C.: Enhanced epileptic seizure detection through wavelet-based analysis of EEG signal processing. Appl. Sci. 14(13), 5783 (2024)

  66. Shoeibi, A., Khodatars, M., Alinejad-Rorky, H., Heras, J., Bagherzadeh, S., Beheshti, A., Gorriz, J.M.: Diagnosis of schizophrenia in EEG signals using DDTF effective connectivity and new pretrained CNN and transformer models. In: International Work-Conference on the Interplay Between Natural and Artificial Computation, pp. 150–160 (2024). Springer

  67. Shoeibi, A., Jafari, M., Sadeghi, D., Alizadehsani, R., Alinejad-Rokny, H., Beheshti, A., Gorriz, J.M.: Early diagnosis of schizophrenia in EEG signals using one dimensional transformer model. In: International Work-Conference on the Interplay Between Natural and Artificial Computation, pp. 139–149 (2024). Springer

  68. Bagherzadeh, S., Shalbaf, A., Shoeibi, A., Jafari, M., San Tan, R., Acharya, U.R.: Developing an EEG-based emotion recognition using ensemble deep learning methods and fusion of brain effective connectivity maps. IEEE Access 12, 50949–50965 (2024). https://doi.org/10.1109/ACCESS.2024.3384303

  69. Bdaqli, M., Shoeibi, A., Moridian, P., Sadeghi, D., Pouyani, M.F., Shalbaf, A., Gorriz, J.M.: Diagnosis of parkinson disease from eeg signals using a cnn-lstm model and explainable ai. In: International Work-Conference on the Interplay Between Natural and Artificial Computation, pp. 128–138 (2024). Springer

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  1. Pelin Sari Tekten, Soner Kotan and Firat Kacar have contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey

    Pelin Sari Tekten, Soner Kotan & Firat Kacar

  2. Turkish Health Data Research and Artificial Intelligence Applications Institute, Health Institutes of Türkiye, 34718, Kadikoy, Istanbul, Turkey

    Soner Kotan

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Contributions

P.S. participated in data collection, examination of previous studies on similar topics, and contributed to the overall writing of the manuscript. S.K. and F.K. were responsible for data organization, analysis, and the creation of figures and tables. The manuscript was written with equal contributions from all three authors.

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Correspondence to Soner Kotan.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Informed consent was obtained from all patients for being included in the study.

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Sari Tekten, P., Kotan, S. & Kacar, F. Multiclass classification of epileptic seizure phases using a novel HFO-based feature extraction model. SIViP 19, 331 (2025). https://doi.org/10.1007/s11760-025-03896-0

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