Different Methods of EEG Signal Analysis Using Power Spectral Density, ChronoNet and ResNest



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Submitted Jun 17, 2023
Published Oct 3, 2023
10.24018/ejece.2023.7.5.551

Abstract viewed = 360 times

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Brain diseases like epilepsy can be identified using electroencephalograms (EEGs). Automated EEG data processing has the potential to improve patient care because manual interpretation requires a lot of time, resources, and money. The paper emphasizes that EEG data can be used to detect intellectual ability and human brain-related diseases like epilepsy. We propose one method ResNest for estimating Intelligence Quotient and two methods (Power spectral density (PSD) and ChronoNet) for detecting epilepsy. The first approach involves utilizing the datasets found at zenodo organization with 5-fold cross validation using the Welch PSD for feature extraction and various classifiers (Kernel SVM, Naive Bayes, Random Forest, Decision Tree). It was found that 99.1% accuracy could be achieved using Kernel SVM. We also propose ChronoNet, a cutting-edge architecture for recurrent neural networks that has been constructed using the Keras framework. It achieves an accuracy of roughly 98.89% by utilizing the data found at Temple University Hospital EEG corpus. The required datasets from Kaggle have been utilized in conjunction with the ResNest method. Using ResNest50d (Epochs=10), we achieved a maximum accuracy of 91%.

Keywords: ChronoNet EEG ResNest Welch Power Spectral Density

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References

  1. Wang G, Deng Z, and Choi KS. Detection of epileptic seizures in EEG signals with rule-based interpretation by random forest approach. In Huang DS, Han K, editors. Advanced Intelligent Computing Theories and Applications. Springer; 2015, pp. 738–744, vol. 9227.
     Google Scholar
  2. Zhang Y, Liu B, Ji X, and Huang D. Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition. Neural Processing Letters. 2017;45(201):365-378. doi: 10.1007/s11063-016-9530-1.
     Google Scholar
  3. Yang C, Deng Z, Choi KS, and Wang S, Takagi–Sugeno–Kang. Transfer learning fuzzy logic system for the adaptive recognition of epileptic electroencephalogram signals. IEEE Transactions on Fuzzy Systems. 2016;224:1079-1094. doi: 10.1109/TFUZZ.2015.2501438.
     Google Scholar
  4. Yao X, Li X, Ye Q, Huang Y, Cheng Q, and Zhang G. A robust deep learning approach for automatic seizure detection. Biomedical Signal Processing and Control. 2021 Feb;64. doi: 10.1016/j.bspc.2020.102215.
     Google Scholar
  5. Yao X, Cheng Q, and Zhang G-Q. A novel independent rnn approach to classification of seizures against non-seizures. arXiv: Machine Learning. 2019 Mar. doi: 10.48550/arXiv.1903.09326.
     Google Scholar
  6. Ke H, Chen D, Li X, Tang Y, Shah T, and Ranjan R. Towards brain big data classification: epileptic EEG identification with a lightweight VGGNet on global MIC. IEEE Access. 2018;6:14722-14733. doi: 10.1109/ACCESS.2018.2810882.
     Google Scholar
  7. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. EEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, Las Vegas, NV, USA; 2016,.
     Google Scholar
  8. Li G, Muller M, Thabet A, Ghanem B. Deepgcns: Can GCNS go as deep as cnns. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9267–9276, Seoul, Korea (South), 2019.
     Google Scholar
  9. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., et al. Attention is all you need. Advances in neural information processing system. 2017:5998–6008. doi: 10.48550/arXiv.1706.03762.
     Google Scholar
  10. Van Hees VT, van Diessen E, Sinke MR, Buitenhuis JW, van der Maas F, Ridder L, et al. Reliable and automatic epilepsy classification with affordable, consumer-grade electroencephalography in rural sub-Saharan Africa. BioRxiv. 2018;324954. doi: 10.1101/324954.
     Google Scholar
  11. Heaton J, Goodfellow I, Bengio Y, and Courville A. Deep learning. Genetic Programming and Evolvable Machines. 2017 Mar;19:305-307. doi: 10.1007/s10710-017-9314-z.
     Google Scholar
  12. Cho K, van Merriënboer B, Bahdanau D, and Bengio Y. On the properties of neural machine translation: encoder-decoder approaches. In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. 2014:103–111. doi: 10.3115/v1/W14-4012.
     Google Scholar
  13. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA. pp. 1–9, 2015. doi: 10.1109/CVPR.2015.7298594.
     Google Scholar
  14. Huang G, Liu Z, Van Der Maaten L and Weinberger KQ. Densely connected convolutional networks. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu. pp. 4700-470821, 26 July 2017.
     Google Scholar
  15. Roy S, Kiral-Kornek I, Harrer S. Chrononet: A deep recurrent neural network for abnormal eeg identification. Artifical Intelligence in Medicine. 2019; 11526:47-56. doi: 10.1007/978-3-030-21642-9_8.
     Google Scholar
  16. Zhang H, Wu C, Zhang Z, Zhu Y, Lin H, Zhang Z, et al. Resnest: Split-attention networks. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 2735-2745, 2022.
     Google Scholar
  17. Zhou W, Liu Y, Yuan Q, Li X. Epileptic seizure detection using lacunarity and bayesian linear discriminant analysis in intracranial EEG. IEEE Transactions on Biomedical Engineering. 2013 Dec;60:3375-81. doi: 10.1109/TBME.2013.2254486.
     Google Scholar
  18. Shoeb AH. Application of machine learning to epileptic seizure onset detection and treatment. International Conference on Machine Learning, pp. 975-982, 2009.
     Google Scholar
  19. Zabihi M, Kiranyaz S, Rad AB, Katsaggelos AK, Gabbouj M, Ince T. Analysis of high-dimensional phase space via Poincaré section for patient-specific seizure detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2015 Dec 18;24(3):386-98.
     Google Scholar
  20. Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. NIPS 2014 Workshop on Deep Learning. 2014 Dec. doi: 10.48550/arXiv.1412.3555.
     Google Scholar
  21. Nigam VP, Graupe D. A neural-network-based detection of epi lepsy. Neurol. Res. 2024;26:55-60. doi: 10.1179/016164104773026534.
     Google Scholar
  22. Bizopoulos P, George I. and Koutsouris D. Signal2image modules in deep neural networks for eeg classification. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Berlin, Germany. 2019:702-705. doi: 10.1109/EMBC.2019.8856620.
     Google Scholar
  23. Yıldırım UB, Baloglu, and Acharya UR. A deep convolutional neural network model for automated identification of abnormal eeg signals. Neural Computing and Applications. 2020;32:15857–15868. doi: 10.1007/s00521-018-3889-z.
     Google Scholar
  24. Yao X, Cheng Q, and Zhang G-Q. A novel independent RNN approach to classification of seizures against non-seizures. arXiv: Machine Learning. 2019 Mar. doi: 10.48550/arXiv.1903.09326.
     Google Scholar
  25. Wei Z, Zou J, Zhang J, and Xu J. Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain. Biomedical Signal Processing and Control. 2019 Aug;53:101551. doi: :10.1038/s41598-020-78784-3.
     Google Scholar
  26. Meisel C, Atrache RE, Jackson M, Schubach S, Ufongene C, Loddenkemper T, et al. Deep learning from wristband sensor data: towards wearable, non-invasive seizure forecasting. arXiv: Neurons and Cognition. 2019 Jun. doi: 10.48550/arXiv.1906.00511.
     Google Scholar
  27. Abou Jaoude M, Jing J, Sun H, Jacobs CS, Pellerin KR, Westover MB, et al. Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning. Clinical Neurophysiology. 2020;131(1):133–141. doi: 10.1016/j.clinph.2019.09.031.
     Google Scholar
  28. RaviPrakash H, Korostenskaja M, Castillo EM, Lee KH, Salinas CM, Baumgartner J, et al. eep learning provides exceptional accuracy to ecog-based functional language mapping for epilepsy surgery. Frontiers in Neuroscience. 2020 May (14). doi: org/10.3389/fnins.2020.00409
     Google Scholar
  29. He K, Zhang X, Ren S and Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770-778, 2016. doi: 10.1109/CVPR.2016.90.
     Google Scholar
  30. He T, Zhang Z, Zhang H, Zhang Z, Xie J, Li M, et al. Bag of tricks for image classification with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 558–567, 2019. doi: 10.1109/CVPR.2019.00065.
     Google Scholar