Skip to main content
SpringerLink
Log in

Computational Approaches for Diagnosis and Monitoring of Epilepsy from Scalp EEG

  • Reference work entry
  • First Online:
Handbook of Neuroengineering

  • 299 Accesses

Abstract

Epilepsy is a chronic brain disorder characterized by recurrent unprovoked seizures. It is caused by alterations in normal electrical activity in the brain, leading to various clinical manifestations depending on the regions that are affected. Scalp electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. It provides data that can pinpoint the foci of epileptiform disturbances and can characterize the epilepsy syndrome. However, providing a timely review of EEG data by clinical experts is a tedious and error-prone exercise. Moreover, there is a disparity in the global and national distribution of EEG experts. In order to assist EEG experts in reading EEGs, machine learning techniques can serve as valuable clinical tools to analyze EEG data in an objective and computationally efficient manner. Such methods have been developed mainly for two purposes in the context of epilepsy: for the detection of interictal epileptiform discharges (IED) and for the detection of electrographical epileptic seizures. Our aim is to concisely review state-of-the-art machine learning methods for IED and seizure detection, to elaborate on existing drawbacks and challenges for such approaches, and to provide guidance to physicians and researchers when designing an automated algorithm for the annotation of epileptic EEG. Furthermore, this chapter will outline potential future directions and opportunities for research in the diagnosis and monitoring of epilepsy from EEG recordings.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 949.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 999.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Abbreviations

AUC:

Area Under Curve

CHB–MIT:

Children Hospital Boston–Massachusetts Institute of Technology

CNN:

Convolutional Neural Network

CRF:

Conditional Random Field

DC:

Direct Current

ECG:

Electrocardiogram

EEG:

Electroencephalogram

FC-NLSTM:

Fully Convolutional Nested Long Short-Term Memory

FDR:

False Detection Rate

FPR:

False Positive Rate

GPED:

Generalized Periodic Epileptiform Discharges

GPU:

Graphical Processing Unit

HMM:

Hidden Markov Model

FFT:

Fast Fourier Transform

IED:

Interictal Epileptiform Discharges

MGH:

Massachusetts General Hospital

MUSC:

Medical University of South Carolina

NUH:

National University Hospital

NN:

Neural Network

PLED:

Periodic Lateralized Epileptiform Discharges

SSL:

Semi-supervised learning

SVM:

Support Vector Machine

SOP:

seizure occurrence period

TM:

Template Matching

1D:

One-dimensional

2D:

Two-dimensional

References

  1. World Health Organization: Epilepsy [fact sheet]. https://www.who.int/news-room/fact-sheets/detail/epilepsy (2019)

  2. McGrogan, N.: Neural Network Detection of Epileptic Seizures in the Electroencephalogram. Oxford University, Oxford (2001)

    Google Scholar 

  3. Bagheri, E., Dauwels, J., Dean, B.C., Waters, C.G., Westover, M.B., Halford, J.J.: Interictal epileptiform discharge characteristics underlying expert interrater agreement. Clin. Neurophysiol. 128(10), 1994–2005 (2017)

    Google Scholar 

  4. Vespa, P.M., McArthur, D.L., Xu, Y., Eliseo, M., Etchepare, M., Dinov, I., Alger, J., Glenn, T.P., Hovda, D.: Nonconvulsive seizures after traumatic brain injury are associated with hippocampal atrophy. Neurology. 75(9), 792–798 (2010)

    Google Scholar 

  5. Vespa, P.M., Miller, C., McArthur, D., Eliseo, M., Etchepare, M., Hirt, D., Glenn, T.C., Martin, N., Hovda, D.: Nonconvulsive electrographic seizures after traumatic brain injury result in a delayed, prolonged increase in intracranial pressure and metabolic crisis. Crit. Care Med. 35(12), 2830–2836 (2007)

    Google Scholar 

  6. Vespa, P.M., Nuwer, M.R., Nenov, V., Ronne-Engstrom, E., Hovda, D.A., Bergsneider, M., Kelly, D.F., Martin, N.A., Becker, D.P.: Increased incidence and impact of nonconvulsive and convulsive seizures after traumatic brain injury as detected by continuous electroencephalographic monitoring. J. Neurosurg. 91(5), 750–760 (1999)

    Google Scholar 

  7. Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M.S.: Deep learning for visual understanding: a review. Neurocomputing. 187, 27–48 (2016)

    Google Scholar 

  8. Boos, C.F., de Azevedo Geovani, R., Scolaro, F.M., Maria do Carmo, V.P.: Automatic Detection of Paroxysms in EEG Signals Using Morphological Descriptors and Artificial Neural Networks. INTECH Open Access Publisher (2011)

    Google Scholar 

  9. İnan, Z.H., Kuntalp, M.: A study on fuzzy C-means clustering-based systems in automatic spike detection. Comput. Biol. Med. 37(8), 1160–1166 (2007)

    Google Scholar 

  10. Tzallas, A.T., Karvelis, P.S., Katsis, C.D., Fotiadis, D.I., Giannopoulos, S., Konitsiotis, S.: A method for classification of transient events in EEG recordings: application to epilepsy diagnosis. Methods Inf. Med. 45(6), 610–621 (2006)

    Google Scholar 

  11. Exarchos, T.P., Tzallas, A.T., Fotiadis, D.I., Konitsiotis, S., Giannopoulos, S.: EEG transient event detection and classification using association rules. IEEE Trans. Inf. Technol. Biomed. 10(3), 451–457 (2006)

    Google Scholar 

  12. Gotman, J., Lves, J.R., Gloor, P.: Automatic recognition of inter-ictal epilepsy activity in prolonged EEG recordings. Electroencephalogr. Clin. Neurophysiol. 46, 510–520 (1979)

    Google Scholar 

  13. Hostetler, W.E., Doller, H.J., Homan, R.W.: Assessment of a computer program to detect epileptiform spikes. Electroencephalogr. Clin. Neurophysiol. 83, 1–11 (1992)

    Google Scholar 

  14. Sugi, T., Nakamura, M., Ikeda, A., Shibasaki, H.: Adaptive EEG spike detection: determination of threshold values based on conditional probability. Front. Med. Biol. Eng. 11, 261–277 (2001)

    Google Scholar 

  15. Adjouadi, M., Cabrerizo, M., Ayala, M., Sanchez, D., Yaylali, I., Jayakar, P., Barreto, A.: A new mathematical approach based on orthogonal operators for the detection of interictal spikes in epileptogenic data. Biomed. Sci. Instrum. 40, 175–180 (2003)

    Google Scholar 

  16. Sankar, R., Natour, J.: Automatic computer analysis of transients in EEG. Comput. Biol. Med. 22(6), 407–422 (1992)

    Google Scholar 

  17. Tzallas, A., Oikonomou, V.P., Fotiadis, D.I.: Epileptic spike detection using a Kalman filter based approach. Paper presented at the International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA (2006)

    Google Scholar 

  18. Adjouadi, M., Sanchez, D., Cabrerizo, M., Ayala, M., Jayakar, P., Yaylali, I., Barreto, A.: Interictal spike detection using the Walsh transform. IEEE Trans. Biomed. Eng. 51, 868–872 (2004)

    Google Scholar 

  19. Feucht, M., Hoffmann, K., Steinberger, K., Witte, H., Benninger, F., Arnold, M., Doering, A.: Simultaneous spike detection and topographic classification in pediatric surface EEGs. Neuroreport. 8, 2193–2197 (1997)

    Google Scholar 

  20. Indiradevi, K.P., Elias, E., Sathidevi, P.S., Nayak, S.D., Radhakrishnan, K.: A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. Comput. Biol. Med. 38, 805–816 (2008)

    Google Scholar 

  21. Chavakula, V., Fernández, I.S., Peters, J.M., Popli, G., Bosl, W., Rakhade, S., Rotenberg, A., Loddenkemper, T.: Automated quantification of spikes. Epilepsy Behav. 26, 143–152 (2013)

    Google Scholar 

  22. Bagheri, E., Jin, J., Dauwels, J., Cash, S., Westover, M.B.: A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram. J. Neurosci. Methods. 326, 108362 (2019)

    Google Scholar 

  23. Lodder, S.S., van Putten, M.J.A.M.: A self-adapting system for the automated detection of inter-ictal epileptiform discharges. PLoS One. 9(1), e85180–e85180 (2014)

    Google Scholar 

  24. Nonclercq, A., Foulon, M., Verheulpen, D., De Cock, C., Buzatu, M., Mathys, P., Van Bogaert, P.: Cluster-based spike detection algorithm adapts to interpatient and intrapatient variation in spike morphology. J. Neurosci. Methods. 210(2), 259–265 (2012)

    Google Scholar 

  25. Zacharaki, E.I., Mporas, I., Garganis, K., Megalooikonomou, V.: Spike pattern recognition by supervised classification in low dimensional embedding space. Brain Inform. 3(2), 73–83 (2016)

    Google Scholar 

  26. Argoud, F.I.M., De Azevedo, F.M., Neto, J.M., Grillo, E.: SADE3: an effective system for automated detection of epileptiform events in long-term EEG based on context information. Med. Biol. Eng. Comput. 44(6), 459–470 (2006)

    Google Scholar 

  27. Halford, J.J., Schalkoff, R.J., Zhou, J., Benbadis, S.R., Tatum, W.O., Turner, R.P., Sinha, S.R., Fountain, N.B., Arain, A., Pritchard, P.B., et al.: Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis. J. Neurosci. Methods. 212(2), 308–316 (2013)

    Google Scholar 

  28. Song, Y., Zhang, J.: Automatic recognition of epileptic EEG patterns via extreme learning machine and multiresolution feature extraction. Expert Syst. Appl. 40(14), 5477–5489 (2013)

    Google Scholar 

  29. Wilson, S.B., Turner, C.A., Emerson, R.G., Scheuer, M.L.: Spike detection II: automatic, perception-based detection and clustering. Clin. Neurophysiol. 110(3), 404–411 (1999)

    Google Scholar 

  30. Carey, H.J., Manic, M., Arsenovic, P.: Epileptic spike detection with EEG using artificial neural networks. In: Human System Interactions (HSI), 2016 9th International Conference on, pp. 89–95. IEEE, Piscataway (2016)

    Google Scholar 

  31. Carey, H.J., Manic, M., Arsenovic, P.: Epileptic spike detection with EEG using artificial neural networks. Paper presented at the 9th International Conference on Human System Interactions (HSI), Portsmouth, UK (2016)

    Google Scholar 

  32. Sommer, D., Golz, M.: Clustering of EEG-segments using hierarchical agglomerative methods and self-organizing maps. Paper presented at the International Conference on Artificial Neural Networks, Berlin, Heidelberg (2001)

    Google Scholar 

  33. Wahlberg, P., Salomonsson, G.: Feature extraction and clustering of EEG epileptic spikes. Comput. Biomed. Res. 29, 382–394 (1996)

    Google Scholar 

  34. Liu, H.S., Zhang, T., Yang, F.S.: A multistage, multimethod approach for automatic detection and classification of epileptiform EEG. IEEE Trans. Biomed. Eng. 49(49), 1557–1566 (2002)

    Google Scholar 

  35. Glover, J.R., Raghaven, N., Ktonas, P.Y., Frost, J.D.: Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives. IEEE Trans. Biomed. Eng. 36, 519–527 (1989)

    Google Scholar 

  36. Ozdamar, O., Yaylali, I., Jayaker, P., Lopez, C.N.: Multilevel neural network system for EEG spike detection. In: Computer Based Medical Systems Proceedings of the Fourth Annual IEEE Symposium, Baltimore, MD, USA. IEEE (1991)

    Google Scholar 

  37. Johansen, A.R., Jin, J., Maszczyk, T., Dauwels, J., Cash, S.S., Westover, M.B.: Epileptiform spike detection via convolutional neural networks. In: Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, pp. 754–758. IEEE, Piscataway (2016)

    Google Scholar 

  38. Tjepkema-Cloostermans, M.C., de Carvalho, R.C.V., van Putten, M.J.A.M.: Deep learning for detection of focal epileptiform discharges from scalp EEG recordings. Clin. Neurophysiol. 129(10), 2191–2196 (2018)

    Google Scholar 

  39. Jing, J., Sun, H., Kim, J.A., Herlopian, A., Karakis, I., Ng, M., Halford, J.J., Maus, D., Chan, F., Dolatshahi, M., Muniz, C., Chu, C., Sacca, V., Pathmanathan, J., Ge, W., Dauwels, J., Lam, A., Cole, A.J., Cash, S.S., Westover, M.B.: Development of expert-level automated detection of epileptiform discharges during electroencephalogram interpretation. JAMA Neurol. 77(1), 103–108 (2020)

    Google Scholar 

  40. Clarke, S., Karoly, P., Nurse, E., Seneviratne, U., Taylor, J., Knight-Sadler, R., Kerr, R., Moore, B., Hennessy, P., Mendis, D., Lim, C., Miles, J., Cook, M., Freestone, D., D’Souz, W.: Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy. Epilepsy Behav. 121(Pt B), 106556 (2019)

    Google Scholar 

  41. Lourenco, C., Tjepkema-Cloostermans, M.C., Teixeira, L.F., van Putten, M.J.: Deep learning for interictal epileptiform discharge detection from scalp EEG recordings. In: Mediterranean Conference on Medical and Biological Engineering and Computing, pp. 1984–1997. Springer, Cham (2019)

    Google Scholar 

  42. Furbass, F., Kural, M.A., Gritsch, G., Hartmann, M., Kluge, T., Beniczky, S.: An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: validation against the diagnostic gold standard. Clin. Neurophysiol. 131(6), 1174–1179 (2020)

    Google Scholar 

  43. Thomas, J., Jin, J., Thangavel, P., Bagheri, E., Yuvaraj, R., Dauwels, J., Rathakrishnan, R., Halford, J.J., Cash, S.S., Westover, B.: Automated detection of interictal epileptiform discharges from scalp electroencephalograms by convolutional neural networks. Int. J. Neural Syst. 30(11), 2050030 (2020)

    Google Scholar 

  44. Hartmann, M.M., Schindlerb, K., Gebbink, T.A., Gritsch, G., Kluge, T.: PureEEG: automatic EEG artifact removal for epilepsy monitoring. Clin. Neurophysiol. 44, 479–490 (2014)

    Google Scholar 

  45. Thomas, J., Maszczyk, T., Sinha, N., Kluge, T., Dauwels, J.: Deep learning-based classification for brain-computer interfaces. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada. IEEE (2017)

    Google Scholar 

  46. Aznan, N.K.N., Bonner, S., Connolly, J., Moubayed, N.A., Breckon, T.: On the classification of SSVEP-based dry-EEG signals via convolutional neural networks. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3726–3731. IEEE, Piscataway (2018)

    Google Scholar 

  47. Thomas, J., Comoretto, L., Jin, J., Dauwels, J., Cash, S.S., Westover, M.B.: EEG CLassification via convolutional neural network-based interictal epileptiform event detection. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3148–3151. IEEE, Piscataway (2018)

    Google Scholar 

  48. Thomas, J., Jin, J., Dauwels, J., Cash, S.S., Westover, M.B.: Automated epileptiform spike detection via affinity propagation-based template matching. Paper presented at the Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, Seogwipo, South Korea (2017)

    Google Scholar 

  49. Lodder, S.S., Askamp, J., van Putten, M.J.: Inter-ictal spike detection using a database of smart templates. Clin. Neurophysiol. 124(12), 2328–2335 (2013)

    Google Scholar 

  50. Dao, N.T.A., Dung, N.V., Trung, N.L., Abed-Meraim, K.: Multi-channel EEG epileptic spike detection by a new method of tensor decomposition. J. Neural Eng. 17(1), 016023 (2020)

    Google Scholar 

  51. Prasanth, T., Thomas, J., Yuvaraj, R., Jing, J., Cash, S.S., Chaudhari, R., Leng, T.Y., Rathakrishnan, R., Rohit, S., Saini, V., Westover, B.M., Dauwels, J.: Deep learning for interictal epileptiform spike detection from scalp EEG frequency sub bands. Paper presented at the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada (2020)

    Google Scholar 

  52. Le, T.X., Le, T.T., Dinh, V.V., Tran, Q.L., Nguyen, L.T., Nguyen, D.T.: Deep learning for epileptic spike detection. VNU J. Sci. Comput. Sci. Commun. Eng. 33(2), 1–13 (2018)

    Google Scholar 

  53. Fukami, T., Shimada, T., Ishikawa, B.: Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution. J. Neural Eng. 15(3), 036030 (2018)

    Google Scholar 

  54. Ganglberger, W., Gritsch, G., Hartmann, M.M., Fürbass, F., Perko, H., Skupch, A., Kluge, T.: A comparison of rule-based and machine learning methods for classification of spikes in EEG. J. Commun. 12(10), 589–595 (2017)

    Google Scholar 

  55. Thomas, J., Jin, J., Dauwels, J., Cash, S.S., Westover, M.B.: Automated epileptiform spike detection via affinity propagation-based template matching. In: Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, pp. 3057–3060. IEEE, Piscataway (2017)

    Google Scholar 

  56. Le Douget, J.E., Fouad, A., Filali, M.M., Pyrzowski, J., Le Van Quyen, M.: Surface and intracranial EEG spike detection based on discrete wavelet decomposition and random forest classification. In: Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, pp. 475–478. IEEE, Piscataway (2017)

    Google Scholar 

  57. Scheuer, M.L., Bagic, A., Wilson, S.B.: Spike detection: inter-reader agreement and a statistical Turing test on a large data set. Clin. Neurophysiol. 128(1), 243–250 (2017)

    Google Scholar 

  58. Rosado, A., Rosa, A.C.: Automatic detection of epileptiform discharges in the EEG. arXiv preprint arXiv:160506708 (2016)

    Google Scholar 

  59. Liu, Y.-C., Lin, C.-C.K., Tsai, J.-J., Sun, Y.-N.: Model-based spike detection of epileptic EEG data. Sensors. 13(9), 12536–12547 (2013)

    Google Scholar 

  60. Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. 64(6 (Pt 1)), 061907 (2001)

    Google Scholar 

  61. Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 101(23), e215–e220 (2000)

    Google Scholar 

  62. Shah, V., von Weltin, E., Lopez, S., McHugh, J.R., Veloso, L., Golmohammadi, M., Obeid, I., Picone, J.: The Temple University Hospital seizure detection corpus. Front. Neuroinform. 12(83), 1–8 (2018)

    Google Scholar 

  63. Ihle, M., Feldwisch-Drentrup, H., Teixeira, C.A., Witon, A., Schelter, B., Timmer, J., Schulze-Bonhage, A.: EPILEPSIAE – a European epilepsy database. Comput. Methods Prog. Biomed. 106(3), 127–138 (2012)

    Google Scholar 

  64. Paul, Y.: Various epileptic seizure detection techniques using biomedical signals: a review. Brain Inform. 5(6), 1–19 (2018)

    Google Scholar 

  65. Chakrabarti, S., Swetapadma, A., Pattnaik, P.K.A.: Review on epileptic seizure detection and prediction using soft computing techniques. In: Mishra, M., Mishra, B., Patel, Y., Misra, R. (eds.) Smart Techniques for a Smarter Planet Studies in Fuzziness and Soft Computing, vol. 374. Springer, Cham

    Google Scholar 

  66. Acharya, U.R., Sree, S.V., Swapna, G., Martis, R.J., Suri, J.S.: Automated EEG analysis of epilepsy: a review. Knowl.-Based Syst. 45, 147–165 (2013)

    Google Scholar 

  67. Binder, D.K., Haut, S.R.: Toward new paradigms of seizure detection. Epilepsy Behav. 26(3), 247–252 (2013)

    Google Scholar 

  68. Hunyadi, B., Signoretto, M., Paesschen, W.V., Suykens, J.A., Huffel, S.V., Vos, M.D.: Incorporating structural information from the multichannel EEG improves patient-specific seizure detection. Clin. Neurophysiol. 123(12), 2352–2361 (2012)

    Google Scholar 

  69. Gotman, J.: Automatic recognition of epileptic seizures in the EEG. Clin. Neurophysiol. 54(5), 530–540 (1982)

    Google Scholar 

  70. Gotman, J.: Automatic detection of seizures and spikes. J. Clin. Neurophysiol. 16(2), 130–140 (1999)

    Google Scholar 

  71. Yang, S., Li, B., Zhang, Y., Duan, M., Liu, S., Zhang, Y., Feng, X., Tan, R., Huang, L., Zhou, F.: Selection of features for patient-independent detection of seizure events using scalp EEG signals. Comput. Biol. Med. 119, 103671 (2020)

    Google Scholar 

  72. Chua, K.C., Chandran, V., Acharya, U.R., Lim, C.M.: Analysis of epileptic EEG signals using higher order spectra. J. Med. Eng. Technol. 33(1), 42–50 (2009)

    Google Scholar 

  73. Iscan, Z., Dokur, Z., Tamer, D.: Classification of electroencephalogram signals with combined time and frequency features. Expert Syst. Appl. 38, 10499–10505 (2011)

    Google Scholar 

  74. Fergus, P., Hignett, D., Hussain, A., Al-Jumeily, D., Abdel-Aziz, K.: Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques. Biomed. Res. Int. 2015, 17–17 (2015)

    Google Scholar 

  75. Subasi, A., Gursoy, I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37, 8659–8666 (2010)

    Google Scholar 

  76. Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput. Intell. Neurosci. 2007, 805–510 (2007)

    Google Scholar 

  77. Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703–710 (2009)

    Google Scholar 

  78. Anand, S.V., Selvakumari, R.S.: Noninvasive method of epileptic detection using DWT and generalized regression neural network. Soft. Comput. 23, 2645–2653 (2019)

    Google Scholar 

  79. Pascual, D., Aminifar, A., Atienza, D.: A self-learning methodology for epileptic seizure detection with minimally-supervised edge labeling. Paper presented at the Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019)

    Google Scholar 

  80. Ubeyli, E.D.: Probabilistic neural networks combined with wavelet coefficients for analysis of EEG signals. Expert. Syst. 26(2), 147–159 (2009)

    Google Scholar 

  81. Adjouadi, M., Cabrerizo, M., Ayala, M., Sanchez, D., Yaylali, I., Jayakar, P., Barreto, A.: Detection of interictal spikes and artifactual data through orthogonal transformations. J. Clin. Neurophysiol. 22(1), 53–64 (2005)

    Google Scholar 

  82. Tzallas, A.T., Tsipouras, M.G., Tsalikakis, D.G., Karvounis, E.C., Astrakas, L., Konitsiotis, S., Tzaphlidou, M.: Automated epileptic seizure detection methods: a review study. In: Epilepsy-Histological, Electroencephalographic and Physiological Aspects, pp. 75–98. InTech (2012)

    Google Scholar 

  83. Kannathal, N., Acharya, U.R., Lim, C.M., Sadasivam, P.K.: Characterization of EEG – a comparative study. Comput. Methods Prog. Biomed. 80, 17–23 (2005)

    Google Scholar 

  84. McSharry, P.E., He, T., Smith, L.A., Tarassenko, L.: Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings. Med. Biol. Eng. Comput. 40(4), 447–461 (2002)

    Google Scholar 

  85. Li, Y., Liu, Y., Cui, W.-G., Guo, Y.-Z., Huang, H., Hu, Z.-Y.: Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network. IEEE Trans. Neural Syst. Rehabil. Eng. 28(4), 782–794 (2020)

    Google Scholar 

  86. Vidyaratne, L.S., Iftekharuddin, K.M.: Real-time epileptic seizure detection using EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 2146–2156 (2017)

    Google Scholar 

  87. Elger, C.E., Lehnertz, K.: Seizure prediction by non-linear time series analysis of brain electrical activity. Eur. J. Neurosci. 10(2), 786–789 (1998)

    Google Scholar 

  88. Guler, I., Ubeyli, E.D.: Multiclass support vector machines for EEG-signals classification. IEEE Trans. Inf. Technol. Biomed. 11(2), 117–126 (2007)

    Google Scholar 

  89. Ubeyli, E.D.: Lyapunov exponents/probabilistic neural networks for analysis of EEG signals. Expert Syst. Appl. 37, 985–992 (2010)

    Google Scholar 

  90. Xie, S., Lawniczak, A.T., Song, Y., Lio, P.: Feature extraction via dynamic PCAfor epilepsy diagnosis and epileptic seizure detection. Paper presented at the International Workshop on Machine Learning for Signal Processing, Kittila, Finland (2010)

    Google Scholar 

  91. Fergus, P., Hussain, A., Hignett, D.: A machine learning system for automat-ed wholebrain seizure detection. Appl. Comput. Inform. 12(1), 70–89 (2016)

    Google Scholar 

  92. Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Comput. Methods Prog. Biomed. 80(3), 187–194 (2005)

    Google Scholar 

  93. Naghsh-Nilchi, A.R., Aghashahi, M.: Epilepsy seizure detection using eigensystem spectral estimation and multiple layer perceptron neural network. Biomed. Signal Process. Control. 5, 147–157 (2010)

    Google Scholar 

  94. van Mierlo, P., Papadopoulou, M., Carrette, E., Boon, P., Vandenberghe, S., Vonck, K., Marinazzo, D.: Functional brain connectivity from EEG in epilepsy: seizure prediction and epileptogenic focus localization. Prog. Neurobiol. 121, 19–35 (2014)

    Google Scholar 

  95. Yaffe, R.B., Borger, P., Megevand, P., Groppe, D.M., Kramer, M.A., Chu, C.J., Santaniello, S., Meisel, C., Mehta, A.D., Sarma, S.V.: Physiology of functional and effective networks in epilepsy. Clin. Neurophysiol. 126(2), 227–236 (2015)

    Google Scholar 

  96. Wani, S.M., Sabut, S., Nalbalwar, S.L.: Detection of epileptic seizure using wavelet transform and neural network classifier. In: Computing, Communication and Signal Processing. Springer, Singapore (2019)

    Google Scholar 

  97. Subasi, A., Ercelebi, E.: Classification of EEG signals using neural network and logistic regression. Comput. Methods Prog. Biomed. 78(2), 87–99 (2005)

    MATH  Google Scholar 

  98. Sridevi, V., Reddy, M.R., Srinivasan, K., Radhakrishnan, H., Rathore, C., Nayak, D.S.: Improved patient-independent system for detection of electrical onset of seizures. J. Clin. Neurophysiol. 36(1), 14–24 (2019)

    Google Scholar 

  99. Jaiswal, A.K., Banka, H.: Epileptic seizure detection in EEG signal using machine learning techniques. Australas. Phys. Eng. Sci. Med. 41, 81–94 (2018)

    Google Scholar 

  100. Wang, D., Miao, D., Xie, C.: Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst.Appl. 38(11), 14314–14320 (2011)

    Google Scholar 

  101. Subasi, A., Kevric, J., Canbaz, M.A.: Epileptic seizure detection using hybrid machine learning methods. Neural Comput. & Applic. 31, 317–325 (2019)

    Google Scholar 

  102. Martinez-Vargas, J.D., Avendano-Valencia, L.D., Giraldo, E., Castellanos-Dominguez, G.: Comparative analysis of time frequency representations for discrimination of epileptic activity in EEG signals. In: 5th International IEEE EMBS Conference on Neural Engineering, Cancun, Mexico. IEEE/EMBS (2011)

    Google Scholar 

  103. Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32, 1084–1093 (2007)

    Google Scholar 

  104. Yuan, Y., Xun, G., Jia, K., Zhang, A.: A multi-view deep learning framework for EEG seizure detection. IEEE J. Biomed. Health Inform. 23(1), 83–94 (2019)

    Google Scholar 

  105. Zhao, J., Xie, X., Xu, X., Sun, S.: Multi-view learning overview: recent progress and new challenges. Inf. Fusion. 38, 43–54 (2017)

    Google Scholar 

  106. Asif, U., Roy, S., Tang, J., Harrer, S.: SeizureNet: a deep convolutional neural network for accurate seizure type classification and seizure detection (2019)

    Google Scholar 

  107. Yuvaraj, R., Thomas, J., Dauwels, J.: Hybrid deep convolutional neural network and hidden Markov model for automatic seizure detection from long-term scalp EEG. F1000 Res. 7 (2018)

    Google Scholar 

  108. Yuvaraj, R., Thomas, J., Kluge, T., Dauwels, J.: A deep learning scheme for automatic seizure detection from long-term scalp EEG. Paper presented at the 52nd IEEE Asilomar Conference on Signals, Systems, and Computers, USA (2018)

    Google Scholar 

  109. Li, Y., Yu, Z., Chen, Y., Yang, C., Li, Y., Li, X.A., Li, B.: Automatic seizure detection using fully convolutional nested LSTM. Int. J. Neural Syst. 30(4), 2050019 (2020)

    Google Scholar 

  110. Zhao, W., Zhao, W., Wang, W., Jiang, X., Zhang, X., Peng, Y., Zhang, B., Zhang, G.: A novel deep neural network for robust detection of seizures using EEG signals. Comput. Math. Methods Med. 2020, 9689821 (2020)

    Google Scholar 

  111. Karthick, P.A., Tanaka, H., Khoo, H.M., Gotman, J.: Prediction of secondary generalization from a focal onset seizure in intracerebral EEG. Clin. Neurophysiol. 129(5), 1030–1040 (2018)

    Google Scholar 

  112. Schiff, S.J., Colella, D., Jacyna, G.M., Hughes, E., Creekmore, J.W., Marshall, A., Bozek-Kuzmicki, M., Benke, G., Gaillard, W.D., Conry, J., Weinstein, S.R.: Brain chirps: spectrographic signatures of epileptic seizures. Clin. Neurophysiol. 111(6), 953–958 (2000)

    Google Scholar 

  113. Lange, H.H., Lieb, J.P., Engel, J.J., Crandall, P.H.: Temporo-spatial patterns of pre-ictal spike activity in human temporal lobe epilepsy. Electroencephalogr. Clin. Neurophysiol. 56(6), 543–555 (1983)

    Google Scholar 

  114. Lehnertz, K., Andrzejak, R.G., Arnhold, J., Kreuz, T., Mormann, F., Rieke, C., Widman, G., Elger, C.E.: Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention. J. Clin. Neurophysiol. 18(3), 209–222 (2001)

    Google Scholar 

  115. Lehnertz, K., Elger, C.E.: Can epileptic seizures be predicted? Evidence from nonlinear time series analysis of brain electrical activity. Phys. Rev. Lett. 80(22), 5019–5022 (1998)

    Google Scholar 

  116. Quyen, M.L.V., Martinerie, J., Navarro, V., Boon, P., D'Have, M., Adam, C., Renault, B., Varela, F., Baulac, M.: Anticipation of epileptic seizures from standard EEG recordings. Lancet. 357(9251), 183–188 (2001)

    Google Scholar 

  117. Correa, A.G., Orosco, L.L., Diez, P., Leber, E.L.: Adaptive filtering for epileptic event detection in the EEG. J. Med. Biol. Eng. 39, 912–918 (2019)

    Google Scholar 

  118. Wang, X., Gong, G., Li, N., Qiu, S.: Detection analysis of epileptic EEG using a novel random Forest model combined with grid search optimization. Front. Hum. Neurosci. 13, 52 (2019)

    Google Scholar 

  119. Chandel, G., Upadhyaya, P., Farooq, O., Khan, Y.U.: Detection of seizure event and its onset/offset using orthonormal triadic wavelet based features. IRBM. 40(2), 103–112 (2019)

    Google Scholar 

  120. Choi, G., Park, C., Kim, J., Cho, K., Kim, T.-J., Bae, H., Min, K.-Y., Jung, K.-Y., Chong, J.-W.: A novel multi-scale 3D CNN with deep neural network for epileptic seizure detection. In: IEEE International Conference on Consumer Electronics. IEEE, Piscataway (2019)

    Google Scholar 

  121. Solaija, M.S.J., Saleem, S., Khurshid, K., Hassan, S.A., Kamboh, A.M.: Dynamic mode decomposition based epileptic seizure detection from scalp EEG. IEEE Access. 6, 38683–38692 (2018)

    Google Scholar 

  122. Alickovic, E., Kevric, J., Subasi, A.: Performance evaluation of empirical mode decomposition, discretewavelet transform, and wavelet packed decomposition for automatedepileptic seizure detection and prediction. Biomed. Signal Process. Control. 39, 94–102 (2018)

    Google Scholar 

  123. Shanir, P.P.M., Khan, K.A., Khan, Y.U., Farooq, O., Adeli, H.: Automatic seizure detection based on morphological features using one-dimensional local binary pattern on long-term EEG. Clin. EEG Neurosci. 49(5), 351–362 (2018)

    Google Scholar 

  124. Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Integer convolutional neural network for seizure detection. IEEE J. Emerg. Sel. Top. Circuits Syst. 8(4), 849–857 (2018)

    Google Scholar 

  125. Thodoroff, P., Pineau, J., Lim, A.: Learning robust features using deep learning for automatic seizure detection. In: Proceedings of the 1st Machine Learning for Healthcare Conference, Los Angeles (2016)

    Google Scholar 

  126. Smith, S.J.M.: EEG in the diagnosis, classification, and management of patients with epilepsy. J. Neurol. Neurosurg. Psychiatry. 76(suppl 2), ii2–ii7 (2005)

    Google Scholar 

  127. Gregory, R.P., Oates, T., Merry, R.T.G.: Electroencephalogram epileptiform abnormalities in candidates for aircrew training. Electroencephalogr. Clin. Neurophysiol. 86(1), 75–77 (1993)

    Google Scholar 

  128. Sundaram, M., Hogan, T., Hiscock, M., Pillay, N.: Factors affecting interictal spike discharges in adults with epilepsy. Electroencephalogr. Clin. Neurophysiol. 75(4), 358–360 (1990)

    Google Scholar 

  129. King, M.A., Newton, M.R., Jackson, G.D., Fitt, G.J., Mitchell, L.A., Silvapulle, M.J., Berkovic, S.F.: Epileptology of the first-seizure presentation: a clinical, electroencephalographic, and magnetic resonance imaging study of 300 consecutive patients. Lancet. 352(9133), 1007–1011 (1998)

    Google Scholar 

  130. Binnie, C.D.: Epilepsy in adults: diagnostic EEG investigation. In: Recent Advances in Clinical Neurophysiology, pp. 217–222. Elsevier, Amsterdam (1996)

    Google Scholar 

  131. Hassanzadeh, H., Kholghi, M., Nguyen, A., Chu, K.: Clinical document classification using labeled and unlabeled data across hospitals. arXiv:181200677v2 [csCL] (2018)

    Google Scholar 

  132. Hopfengartner, R., Kerling, F., Bauer, V., Stefan, H.: An efficient, robust and fast method for the offline detection of epileptic seizures in long-term scalp EEG recordings. Clin. Neurophysiol. 118, 2332–2343 (2007)

    Google Scholar 

  133. Herta, J., Koren, J., Furbass, F., Hartmann, M., Gruber, A., Baumgartner, C.: Reduced electrode arrays for the automated detection of rhythmic and periodic patterns in the intensive care unit: frequently tried, frequently failed? Clin. Neurophysiol. 128(8), 1524–1531 (2017)

    Google Scholar 

  134. Gu, Y., Cleeren, E., Dan, J., Claes, K., Paesschen, W.V., Huffel, S.V., Hunyadi, B.: Comparison between scalp EEG and behind-the-ear EEG for development of a wearable seizure detection system for patients with focal epilepsy. Sensors. 18(1), 29 (2018)

    Google Scholar 

  135. Furbass, F., Kampusch, S., Kaniusas, E., Koren, J., Pirker, S., Hopfengärtner, R., Stefan, H., Kluge, T., Baumgartner, C.: Automatic multimodal detection for long-term seizure documentation in epilepsy. Clin. Neurophysiol. 128(8), 1466–1472 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore, Singapore

    Rajamanickam Yuvaraj, John Thomas, Elham Bagheri & Justin Dauwels

  2. Science of Learning in Education Centre (SoLEC), National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore, Singapore

    Rajamanickam Yuvaraj

  3. Delft University of Technology, Delft, The Netherlands

    Justin Dauwels

  4. Division of Neurology, National University Hospital (NUH), Singapore, Singapore

    Rahul Rathakrishnan

  5. National Neuroscience Institute (NNI), Singapore, Singapore

    Yee Leng Tan

Authors
  1. Rajamanickam Yuvaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elham Bagheri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Justin Dauwels
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rahul Rathakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yee Leng Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Justin Dauwels .

Editor information

Editors and Affiliations

  1. Neuroengineering and Biomedical Instrumentation Laboratory Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Neurology, Johns Hopkins University, Baltimore, MD, USA

    Nitish V. Thakor

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Singapore Pte Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Yuvaraj, R., Thomas, J., Bagheri, E., Dauwels, J., Rathakrishnan, R., Tan, Y.L. (2023). Computational Approaches for Diagnosis and Monitoring of Epilepsy from Scalp EEG. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-5540-1_68

Download citation

Publish with us

Policies and ethics

Search

Navigation