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Automated diagnosis of EEG abnormalities with different classification techniques

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Abstract

Automatic seizure detection and prediction using clinical Electroencephalograms (EEGs) are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high variance in epileptic seizures among patients, and limited clinical data constraints. To overcome these challenges, this paper presents two approaches for EEG signal classification. One of these approaches depends on Machine Learning (ML) tools. The used features are different types of entropy, higher-order statistics, and sub-band energies in the Hilbert Marginal Spectrum (HMS) domain. The classification is performed using Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbor (KNN) classifiers. Both seizure detection and prediction scenarios are considered. The second approach depends on spectrograms of EEG signal segments and a Convolutional Neural Network (CNN)-based residual learning model. We use 10000 spectrogram images for each class. In this approach, it is possible to perform both seizure detection and prediction in addition to a 3-state classification scenario. Both approaches are evaluated on the Children’s Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) dataset, which contains 24 EEG recordings for 6 males and 18 females. The results obtained for the HMS-based model showed an accuracy of 100%. The CNN-based model achieved accuracies of 97.66%, 95.59%, and 94.51% for Seizure (S) versus Pre-Seizure (PS), Non-Seizure (NS) versus S, and NS versus S versus PS classes, respectively. These results demonstrate that the proposed approaches can be effectively used for seizure detection and prediction. They outperform the state-of-the-art techniques for automatic seizure detection and prediction.

Graphical Abstract

Block diagram of proposed epileptic seizure detection method using HMS with different classifiers.

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References

  1. Russ SA, Larson K, Halfon N (2012) A national profile of childhood epilepsy and seizure disorder. Pediatrics 129(2):256–264

    Article  PubMed  Google Scholar 

  2. Tomson T, Battino D, Bonizzoni E, Craig J, Lindhout D, Sabers A, Perucca E, Vajda F, Group ES (2011) Dose-dependent risk of malformations with antiepileptic drugs: an analysis of data from the eurap epilepsy and pregnancy registry. The Lancet Neurology 10(7):609–617

    Article  Google Scholar 

  3. Cohen KB, Glass B, Greiner HM, Holland-Bouley K, Standridge S, Arya R, Faist R, Morita D, Mangano F, Connolly B et al (2016) Methodological issues in predicting pediatric epilepsy surgery candidates through natural language processing and machine learning. Biomedical informatics insights vol 8, pp BII–S38308

  4. Yaffe R, Burns S, Gale J, Park H-J, Bulacio J, Gonzalez-Martinez J, Sarma SV (2012) Brain state evolution during seizure and under anesthesia: A network-based analysis of stereotaxic eeg activity in drug-resistant epilepsy patients. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 5158–5161

  5. Yu P-N, Naiini SA, Heck CN, Liu CY, Song D, Berger TW (2016) A sparse laguerre-volterra autoregressive model for seizure prediction in temporal lobe epilepsy. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 1664–1667

  6. Mishra M, Jones B, Simonotto JD, Furman M, Norman WM, Liu Z, DeMarse TB, Carney PR, Ditto WL (2006) Pre-ictal entropy analysis of microwire data from an animal model of limbic epilepsy. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 1605–1607

  7. WHO (2017) Programmes and projects. http://www.who.int/mediacentre/factsheets/fs999/en/. Accessed 20 May 2021

  8. Glauser T, Shinnar S, Gloss D, Alldredge B, Arya R, Bainbridge J, Bare M, Bleck T, Dodson WE, Garrity L et al (2016) Evidence-based guideline: treatment of convulsive status epilepticus in children and adults: report of the guideline committee of the american epilepsy society. Epilepsy currents 16(1):48–61

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pedram MZ, Shamloo A, Alasty A, Ghafar-Zadeh E (2015) Mri-guided epilepsy detection. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 4001–4004

  10. Simonotto JD, Myers SM, Furman MD, Norman WM, Liu Z, DeMarse TB, Carney PR, Ditto WL (2006) Coherence analysis over the latent period of epileptogenesis reveal that high-frequency communication is increased across hemispheres in an animal model of limbic epilepsy. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 1154–1156

  11. Taher F, Shoaib MR, Emara HM, Abdelwahab KM, El-Samie FEA, Haweel MT (2022) Efficient framework for brain tumor detection using different deep learning techniques. Frontiers in Public Health 10:959667

    Article  PubMed  PubMed Central  Google Scholar 

  12. Shoaib MR, Elshamy MR, Taha TE, El-Fishawy AS, Abd El-Samie FE (2022) Efficient deep learning models for brain tumor detection with segmentation and data augmentation techniques. Concurrency and Computation: Practice and Experience 34(21):e7031

    Article  Google Scholar 

  13. Shoaib MR, Emara HM, Elwekeil M, El-Shafai W, Taha TE, El-Fishawy AS, El-Rabaie E-SM, El-Samie E-SM (2022) Hybrid classification structures for automatic covid-19 detection. Journal of Ambient Intelligence and Humanized Computing 13(9):4477–4492

    Article  PubMed  PubMed Central  Google Scholar 

  14. Emara HM, Shoaib MR, Elwekeil M, El-Shafai W, Taha TE, El-Fishawy AS, El-Rabaie E-SM, Alshebeili SA, Dessouky MI, Abd El-Samie FE (2022) Deep convolutional neural networks for covid-19 automatic diagnosis. Microscopy Research and Technique 84(11):2504–2516

    Article  Google Scholar 

  15. Alam SS, Bhuiyan MIH (2013) Detection of seizure and epilepsy using higher order statistics in the emd domain. IEEE journal of biomedical and health informatics 17(2):312–318

    Article  PubMed  Google Scholar 

  16. Bizopoulos PA, Tsalikakis DG, Tzallas AT, Koutsouris DD, Fotiadis DI (2013) Eeg epileptic seizure detection using k-means clustering and marginal spectrum based on ensemble empirical mode decomposition. In 13th IEEE International Conference on BioInformatics and BioEngineering, IEEE,pp 1–4

  17. Fu K, Qu J, Chai Y, Zou T (2015) Hilbert marginal spectrum analysis for automatic seizure detection in eeg signals. Biomedical Signal Processing and Control 18:179–185

    Article  Google Scholar 

  18. Ibrahim FE, Emara HM, El-Shafai W, Elwekeil M, Rihan M, Eldokany IM, Taha TE, El-Fishawy AS, El-Rabaie E-SM, Abdellatef E et al (2022) Deep learning-based seizure detection and prediction from eeg signals. International Journal for Numerical Methods in Biomedical Engineering, p e3573

  19. Riaz F, Hassan A, Rehman S, Niazi IK, Dremstrup K (2015) Emd-based temporal and spectral features for the classification of eeg signals using supervised learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering 24(1):28–35

    Article  PubMed  Google Scholar 

  20. Hassan AR, Subasi A, Zhang Y (2019) Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowledge-Based Systems, p 105333

  21. Bouaziz B, Chaari L, Batatia H, Quintero-Rincón A (2019) Epileptic seizure detection using a convolutional neural network. In Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine, Springer, pp 79–86

  22. Rajaguru H, Prabhakar SK (2018) Multilayer autoencoders and em-pca with genetic algorithm for epilepsy classification from eeg. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, pp 353–358

  23. Roy S, Kiral-Kornek I, Harrer S (2019) Chrononet: a deep recurrent neural network for abnormal eeg identification. In Conference on Artificial Intelligence in Medicine in Europe, Springer, pp 47–56

  24. Choi G, Park C, Kim J, Cho K, Kim T-J, Bae H, Min K, Jung K-Y, Chong J (2019) A novel multi-scale 3d cnn with deep neural network for epileptic seizure detection. In 2019 IEEE International Conference on Consumer Electronics (ICCE), IEEE, pp 1–2

  25. Shoeb AH (2009) Application of machine learning to epileptic seizure onset detection and treatment. Ph.D. dissertation, Massachusetts Institute of Technology

  26. Thodoroff P, Pineau J, Lim A (2016) Learning robust features using deep learning for automatic seizure detection. In Machine Learning for Healthcare Conference, Springer, pp 178–190

  27. Cura OK, Atli SK, Türe HS, Akan A (2020) Epileptic seizure classifications using empirical mode decomposition and its derivative. BioMedical Engineering OnLine 19(1):1–22

    Google Scholar 

  28. Truong ND, Nguyen AD, Kuhlmann L, Bonyadi MR, Yang J, Ippolito S, Kavehei O (2018) Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105:104–111

    Article  PubMed  Google Scholar 

  29. Ozdemir N, Yildirim E (2014) Patient specific seizure prediction system using hilbert spectrum and bayesian networks classifiers. Computational and mathematical methods in medicine, vol 2014

  30. Consul S, Morshed BI, Kozma R (2013) Hardware efficient seizure prediction algorithm. In Nanosensors, Biosensors, and Info-Tech Sensors and Systems 2013 International Society for Optics and Photonics, vol 8691, p 86911J

  31. Chu H, Chung CK, Jeong W, Cho K-H (2017) Predicting epileptic seizures from scalp eeg based on attractor state analysis. Computer methods and programs in biomedicine 143:75–87

    Article  PubMed  Google Scholar 

  32. Sedik A, Emara HM, Hamad A, Shahin EM, El-Hag NA, Khalil A, Ibrahim F, Elsherbeny ZM, Elreefy M, Zahran O et al (2019) Efficient anomaly detection from medical signals and images. International Journal of Speech Technology 22(3):739–767

    Article  Google Scholar 

  33. Emara HM, Elwekeil M, Taha TE, El-Fishawy AS, El-Rabaie E-SM, El-Shafai W, El Banby GM, Alotaiby T, Alshebeili SA, El-Samie A et al (2021) Efficient frameworks for eeg epileptic seizure detection and prediction. Annals of Data Science, pp 1–36

  34. Emara HM, Elwekeil M, Taha TE, El-Fishawy AS, El-Rabaie E-SM, Alotaiby T, Alshebeili SA, El-Samie A, Fathi E (2021) Hilbert transform and statistical analysis for channel selection and epileptic seizure prediction. Wireless Personal Communications 116(4):3371–3395

    Article  Google Scholar 

  35. Yoo J, Yan L, El-Damak D, Altaf MAB, Shoeb AH, Chandrakasan AP (2012) An 8-channel scalable eeg acquisition soc with patient-specific seizure classification and recording processor. IEEE journal of solid-state circuits 48(1):214–228

    Article  Google Scholar 

  36. Rana P, Lipor J, Lee H, Van Drongelen W, Kohrman MH, Van Veen B (2012) Seizure detection using the phase-slope index and multichannel ecog. IEEE Transactions on Biomedical Engineering 59(4):1125–1134

    Article  PubMed  Google Scholar 

  37. Khamis H, Mohamed A, Simpson S (2013) Frequency-moment signatures: a method for automated seizure detection from scalp eeg. Clinical Neurophysiology 124(12):2317–2327

    Article  PubMed  Google Scholar 

  38. Zhou W, Liu Y, Yuan Q, Li X (2013) Epileptic seizure detection using lacunarity and bayesian linear discriminant analysis in intracranial eeg. IEEE Transactions on Biomedical Engineering 60(12):3375–3381

    Article  PubMed  Google Scholar 

  39. Liu Y, Zhou W, Yuan Q, Chen S (2012) Automatic seizure detection using wavelet transform and svm in long-term intracranial eeg. IEEE transactions on neural systems and rehabilitation engineering 20(6):749–755

    Article  PubMed  Google Scholar 

  40. Vidyaratne L, Glandon A, Alam M, Iftekharuddin KM (2016) Deep recurrent neural network for seizure detection. In 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1202–1207

  41. Shoeb AH, Guttag JV (2010) Application of machine learning to epileptic seizure detection. In ICML

  42. Pramod S, Page A, Mohsenin T, Oates T (2014) Detecting epileptic seizures from eeg data using neural networks. arXiv preprint arXiv:1412.6502

  43. Turner J, Page A, Mohsenin T, Oates T (2014) Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection. In 2014 AAAI Spring Symposium Series

  44. Khan KA, Shanir P, Khan YU, Farooq O (2020) A hybrid local binary pattern and wavelets based approach for eeg classification for diagnosing epilepsy. Expert Systems with Applications 140:112895

    Article  Google Scholar 

  45. Orosco L, Correa AG, Diez P, Laciar E (2016) Patient non-specific algorithm for seizures detection in scalp eeg. Computers in biology and medicine 71:128–134

    Article  PubMed  Google Scholar 

  46. Al Safi A, Beyer C, Unnikrishnan V, Spiliopoulou M (2020) Multivariate time series as images: Imputation using convolutional denoising autoencoder. In International Symposium on Intelligent Data Analysis, Springer, pp 1–13

  47. Wang Z, Oates T (2015) Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In Workshops at the twenty-ninth AAAI conference on artificial intelligence

  48. Barra S, Carta SM, Corriga A, Podda AS, Recupero DR (2020) Deep learning and time series-to-image encoding for financial forecasting. IEEE/CAA Journal of Automatica Sinica 7(3):683–692

    Article  Google Scholar 

  49. Kukker A, Sharma R (2021) A genetic algorithm assisted fuzzy q-learning epileptic seizure classifier. Computers & Electrical Engineering 92:107154

    Article  Google Scholar 

  50. Jareda MK, Sharma R, Kukker A (2019) Eeg signal based seizure classification using wavelet transform. In 2019 International Conference on Computing, Power and Communication Technologies (GUCON), IEEE, pp 537–539

  51. PhysioNet (2000) CHB-MIT Scalp EEG Database. https://www.physionet.org/pn6/chbmit/. Accessed 1 Jan 2017

  52. Hassan AR (2015) A comparative study of various classifiers for automated sleep apnea screening based on single-lead electrocardiogram. In 2015 International Conference on Electrical & Electronic Engineering (ICEEE), IEEE, pp 45–48

  53. Hassan AR, Bhuiyan MIH (2016) Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomedical Signal Processing and Control 24:1–10

    Article  Google Scholar 

  54. Zamanian H, Farsi H (2018) A new feature extraction method to improve emotion detection using eeg signals. ELCVIA Electronic Letters on Computer Vision and Image Analysis 17(1):29–44

    Article  Google Scholar 

  55. Wang H, Ji Y (2018) A revised hilbert-huang transform and its application to fault diagnosis in a rotor system. Sensors 18(12):4329

    Article  PubMed  PubMed Central  Google Scholar 

  56. Toh AM, Togneri R, Nordholm S (2005) Spectral entropy as speech features for speech recognition. Proceedings of PEECS 1:92

    Google Scholar 

  57. Kannathal N, Choo ML, Acharya UR, Sadasivan P (2005) Entropies for detection of epilepsy in eeg. Computer methods and programs in biomedicine 80(3):187–194

    Article  CAS  PubMed  Google Scholar 

  58. Lehman A (1964) A solution of the shannon switching game. Journal of the Society for Industrial and Applied Mathematics 12(4):687–725

    Article  Google Scholar 

  59. Rényi A, Vekerdi L (1970) Calcul des probabilités. North-Holland Publishing Company, vol 10

  60. Tsallis C (1988) Possible generalization of boltzmann-gibbs statistics. Journal of statistical physics 52(1–2):479–487

    Article  Google Scholar 

  61. Bajaj V, Pachori RB (2013) Automatic classification of sleep stages based on the time-frequency image of eeg signals. Computer methods and programs in biomedicine 112(3):320–328

    Article  PubMed  Google Scholar 

  62. Omerhodzic I, Avdakovic S, Nuhanovic A, Dizdarevic K (2013) Energy distribution of eeg signals: Eeg signal wavelet-neural network classifier. arXiv preprint arXiv:1307.7897

  63. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences 454(1971):903–995

    Article  Google Scholar 

  64. Khoshnevis SA, Sankar R (2019) Applications of higher order statistics in electroencephalography signal processing: a comprehensive survey. IEEE Reviews in biomedical engineering 13:169–183

    Article  PubMed  Google Scholar 

  65. Šimundić A-M (2008) Measures of diagnostic accuracy: basic definitions. Medical and biological sciences 22(4):61–65

    Google Scholar 

  66. Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Computing and Applications 24(5):1163–1177

    Article  Google Scholar 

  67. Salam MT, Sawan M, Nguyen DK (2010) Low-power implantable device for onset detection and subsequent treatment of epileptic seizures: A review. Journal of Healthcare Engineering 1(2):169–184

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communications, Delta Higher Institute for Engineering and Technology (DHIET), 35511, Mansoura, Egypt

    Essam Abdellatef

  2. Faculty of Electronic Engineering, Menoufia University, 32952, Menouf, Egypt

    Heba M. Emara, Fatma E. Ibrahim, Mohamed Elwekeil, Walid El-Shafai, Taha E. Taha, Adel S. El-Fishawy, El-Sayed M. El-Rabaie, Ibrahim M. Eldokany & Fathi E. Abd El-Samie

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Mohamed R. Shoaib

  4. Security Engineering Laboratory, Department of Computer Science College of Engineering, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Walid El-Shafai

  5. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

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  1. Essam Abdellatef
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Correspondence to Walid El-Shafai.

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Abdellatef, E., Emara, H.M., Shoaib, M.R. et al. Automated diagnosis of EEG abnormalities with different classification techniques. Med Biol Eng Comput 61, 3363–3385 (2023). https://doi.org/10.1007/s11517-023-02843-w

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