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Automatic detection of epileptic seizures using Riemannian geometry from scalp EEG recordings

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Abstract

This paper proposes a new framework for epileptic seizure detection using non-invasive scalp electroencephalogram (sEEG) signals. The major innovation of the current study is using the Riemannian geometry for transforming the covariance matrices estimated from the EEG channels into a feature vector. The spatial covariance matrices are considered as features in order to extract the spatial information of the sEEG signals without applying any spatial filtering. Since these matrices are symmetric and positive definite (SPD), they belong to a special manifold called the Riemannian manifold. Furthermore, a kernel based on Riemannian geometry is proposed. This kernel maps the SPD matrices onto the Riemannian tangent space. The SPD matrices, obtained from all channels of the segmented sEEG signals, have high dimensions and extra information. For these reasons, the sequential forward feature selection method is applied to select the best features and reduce the computational burden in the classification step. The selected features are fed into a support vector machine (SVM) with an RBF kernel to classify the feature vectors into seizure and non-seizure classes. The performance of the proposed method is evaluated using two long-term scalp EEG (CHB-MIT benchmark and private) databases. Experimental results on all 23 subjects of the CHB-MIT database reveal an accuracy of 99.87%, a sensitivity of 99.91%, and a specificity of 99.82%. In addition, the introduced algorithm is tested on the private sEEG signals recorded from 20 patients, having 1380 seizures. The proposed approach achieves an accuracy, a sensitivity, and a specificity of 98.14%, 98.16%, and 98.12%, respectively. The experimental results on both sEEG databases demonstrate the effectiveness of the proposed method for automated epileptic seizure detection, especially for the private database which has noisier signals in comparison to the CHB-MIT database.

Block diagram of the proposed epileptic seizure detection algorithm

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References

  1. Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl-Based Syst 45:147–165

    Article  Google Scholar 

  2. Song J-L, Li Q, Pan M, Zhang B, Westover MB, Zhang R (2020) Seizure tracking of epileptic eegs using a model-driven approach. J Neural Eng 17(1):016024

    Article  Google Scholar 

  3. Vidyaratne LS, Iftekharuddin KM (2017) Real-time epileptic seizure detection using EEG. IEEE Trans Neural Syst Rehab Eng 25(11):2146–2156

    Article  Google Scholar 

  4. Islam MK, Rastegarnia A, Yang Z (2015) A wavelet-based artifact reduction from scalp EEG for epileptic seizure detection. IEEE J Biomed Health Inf 20(5):1321–1332

    Article  Google Scholar 

  5. Aldana YR, Hunyadi B, Reyes EJM, Rodríguez VR, Van Huffel S (2018) Nonconvulsive epileptic seizure detection in scalp EEG using multiway data analysis. IEEE J Biomed Health Inf 23(2):660–671

    Article  Google Scholar 

  6. Sadatnejad K, Rahmati M, Rostami R, Kazemi R, Ghidary SS, Müller A, Alimardani F (2019) Eeg representation using multi-instance framework on the manifold of symmetric positive definite matrices. J Neural Eng 16(3):036016

    Article  Google Scholar 

  7. Misulis KE (2013) Atlas of eeg, seizure semiology, and management. Oxford University Press

  8. Yuan Y, Xun G, Jia K, Zhang A (2018) A multi-view deep learning framework for EEG seizure detection. IEEE J Biomed Health Inf 23(1):83–94

    Article  Google Scholar 

  9. Zabihi M, Kiranyaz S, Jantti V, Lipping T, Gabbouj M (2019) Patient-specific seizure detection using nonlinear dynamics and nullclines. IEEE Journal of Biomedical and Health Informatics

  10. Li Y, Wang X-D, Luo M-L, Li K, Yang X-F, Guo Q (2017) Epileptic seizure classification of EEGs using time–frequency analysis based multiscale radial basis functions. IEEE J Biomed Health Inf 22(2):386–397

    Article  Google Scholar 

  11. Zavar M, Rahati S, Akbarzadeh-T M-R, Ghasemifard H (2011) Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection. Expert Syst Appl 38 (9):10751–10758

    Article  Google Scholar 

  12. Khan YU, Rafiuddin N, Farooq O (2012) Automated seizure detection in scalp EEG using multiple wavelet scales. In: 2012 IEEE International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, pp 1–5

  13. Xiang J, Li C, Li H, Cao R, Wang B, Han X, Chen J (2015) The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 243:18–25

    Article  Google Scholar 

  14. Ivanov A, Riccardi G (2012) Kolmogorov-smirnov test for feature selection in emotion recognition from speech. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. IEEE, pp 5125–5128

  15. Zandi AS, Javidan M, Dumont GA, Tafreshi R (2010) Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans Biomed Eng 57(7):1639–1651

    Article  Google Scholar 

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

  17. Barachant A, Bonnet S, Congedo M, Jutten C (2013) Classification of covariance matrices using a riemannian-based kernel for bci applications. Neurocomputing 112:172–178

    Article  Google Scholar 

  18. Kaleem M, Gurve D, Guergachi A, Krishnan S (2018) Patient-specific seizure detection in long-term eeg using signal-derived empirical mode decomposition (emd)-based dictionary approach. J Neural Eng 15(5):056004

    Article  Google Scholar 

  19. Wang Y, Qi Y, Wang Y, Lei Z, Zheng X, Pan G (2016) Delving into α-stable distribution in noise suppression for seizure detection from scalp eeg. J Neural Eng 13(5):056009

    Article  Google Scholar 

  20. Yuan S, Zhou W, Wu Q, Zhang Y (2016) Epileptic seizure detection with log-euclidean gaussian kernel-based sparse representation. Int J Neural Syst 26(03):1650011

    Article  Google Scholar 

  21. Bolagh SNG, Clifford G, et al. (2017) Subject selection on a riemannian manifold for unsupervised cross-subject seizure detection. arXiv:1712.00465

  22. Bakhshali MA, Khademi M, Ebrahimi-Moghadam A, Moghimi S (2020) Eeg signal classification of imagined speech based on riemannian distance of correntropy spectral density. Biomed Signal Process Control 59:101899

    Article  Google Scholar 

  23. Goldberger AL, Amaral Luis AN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

    Article  CAS  Google Scholar 

  24. Fingelkurts AA, Fingelkurts AA, Kaplan AY (2006) Interictal eeg as a physiological adaptation. part i. composition of brain oscillations in interictal eeg. Clin Neurophysiol 117(1):208–222

    Article  Google Scholar 

  25. Tuzel O, Porikli F, Meer P (2008) Pedestrian detection via classification on riemannian manifolds. IEEE Trans Pattern Anal Mach Intell (10):1713–1727

  26. Barachant A, Bonnet S, Congedo M, Jutten C (2012) Multiclass brain–computer interface classification by riemannian geometry. IEEE Trans Biomed Eng 59(4):920–928

    Article  Google Scholar 

  27. Zarei A, Asl BM (2020) Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals. Biomed Signal Process Control 59:101927

    Article  Google Scholar 

  28. Zarei A, Asl BM (2018) Automatic detection of obstructive sleep apnea using wavelet transform and entropy-based features from single-lead ECG signal. IEEE J Biomed Health Inf 23(3):1011–1021

    Article  Google Scholar 

  29. Gu X, Guo J, Xiao L, Ming T, Li C (2019) A feature selection algorithm based on equal interval division and minimal-redundancy–maximal-relevance. Neural Process Lett:1–27

  30. Kaya GT, Kaya H (2014) Optimization of svm parameters using high dimensional model representation and its application to hyperspectral images. In: Signal Processing and Communications Applications Conference (SIU), 2014 22nd. IEEE, pp 642–645

  31. Wang D, Miao D, Blohm G (2013) A new method for EEG-based concealed information test. IEEE Trans Inf Forensic Secur 8(3):520–527

    Article  CAS  Google Scholar 

  32. De Sá JPM (2007) Applied statistics using spss, statistica, matlab and r. Springer Science & Business Media

  33. Hunyadi B, Signoretto M, Van Paesschen W, Suykens JAK, Van Huffel S, De Vos M (2012) Incorporating structural information from the multichannel eeg improves patient-specific seizure detection. Clin Neurophysiol 123(12):2352–2361

    Article  Google Scholar 

  34. Chen L-L, Zhang J, Zou J-Z, Zhao C-J, Wang G-S (2014) A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection. Biomed Signal Process Control 10:1–10

    Article  CAS  Google Scholar 

  35. Kiranyaz S, Ince T, Zabihi M, Ince D (2014) Automated patient-specific classification of long-term electroencephalography. J Biomed Inf 49:16–31

    Article  Google Scholar 

  36. Fürbass F, Ossenblok P, Hartmann M, Perko H, Skupch AM, Lindinger G, Elezi L, Pataraia E, Colon AJ, Baumgartner C, et al. (2015) Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units. Clin Neurophysiol 126(6):1124–1131

    Article  Google Scholar 

  37. Fergus P, Hignett D, Hussain A, Al-Jumeily D, Abdel-Aziz K (2015) Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques. Biomed Research International 2015

  38. Samiee K, Kiranyaz S, Gabbouj M, Saramäki T (2015) Long-term epileptic EEG classification via 2d mapping and textural features. Expert Syst Appl 42(20):7175–7185

    Article  Google Scholar 

  39. Zabihi M, Kiranyaz S, Rad AB, Katsaggelos A K, Gabbouj M, Ince T (2016) Analysis of high-dimensional phase space via poincaré section for patient-specific seizure detection. IEEE Trans Neural Syst Rehab Eng 24(3):386–398

    Article  Google Scholar 

  40. Zarei A, Asl B M (2021) Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals. Comput Biol Med 131:104250

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran

    Atefeh Shariat, Asghar Zarei & Babak Mohammadzadeh Asl

  2. Department of Neurology, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran

    Sanaz Ahmadi Karvigh

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  1. Atefeh Shariat
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  2. Asghar Zarei
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  3. Sanaz Ahmadi Karvigh
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  4. Babak Mohammadzadeh Asl
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Correspondence to Babak Mohammadzadeh Asl.

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Shariat, A., Zarei, A., Karvigh, S.A. et al. Automatic detection of epileptic seizures using Riemannian geometry from scalp EEG recordings. Med Biol Eng Comput 59, 1431–1445 (2021). https://doi.org/10.1007/s11517-021-02385-z

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