A deep convolutional neural network model for automated identification of abnormal EEG signals

  • Özal Yıldırım
  • Ulas Baran Baloglu
  • U. Rajendra Acharya
Recent Advances in Deep Learning for Medical Image Processing


Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual examination of these signals by experts is strenuous and time consuming. Hence, machine learning techniques can be used to improve the accuracy of detection. Nowadays, deep learning methodologies have been used in medical field to diagnose the health conditions precisely and aid the clinicians. In this study, a new deep one-dimensional convolutional neural network (1D CNN) model is proposed for the automatic recognition of normal and abnormal EEG signals. The proposed model is a complete end-to-end structure which classifies the EEG signals without requiring any feature extraction. In this study, we have used the EEG signals from temporal to occipital (T5–O1) single channel obtained from Temple University Hospital EEG Abnormal Corpus (v2.0.0) EEG dataset to develop the 1D CNN model. Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.


Convolutional neural network Abnormal EEG EEG classification Deep learning 


Compliance with ethical standards

Conflict of interest

There is no conflict of interest in this work.


  1. 1.
    Smith SJM (2005) EEG in the diagnosisclassification, and management of patients with epilepsy. J Neurol Neurosurg Psychiatry. CrossRefGoogle Scholar
  2. 2.
    Acharya UR, Vinitha Sree S, Swapna G et al (2013) Automated EEG analysis of epilepsy: a review. Knowl Based Syst 45:147–165CrossRefGoogle Scholar
  3. 3.
    Işik H, Sezer E (2012) Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman artificial neural networks and wavelet transform. J Med Syst 36:1–13CrossRefGoogle Scholar
  4. 4.
    Acharya UR, Oh SL, Hagiwara Y et al (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278CrossRefGoogle Scholar
  5. 5.
    Chen G (2014) Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features. Expert Syst Appl 41(5):2391–2394CrossRefGoogle Scholar
  6. 6.
    Lehmann C, Koenig T, Jelic V et al (2007) Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). J Neurosci Methods 161(2):342–350CrossRefGoogle Scholar
  7. 7.
    Ahmadlou M, Adeli H, Adeli A (2011) Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of alzheimer disease. Alzheimer Dis Assoc Disord 25(1):85–92CrossRefGoogle Scholar
  8. 8.
    Kulkarni N, Bairagi V (2018) EEG-based diagnosis of alzheimer disease: a review and novel approaches for feature extraction and classification techniques. Academic Press, CambridgeGoogle Scholar
  9. 9.
    Oh SL, Hagiwara Y, Raghavendra U et al (2018) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Appl. CrossRefGoogle Scholar
  10. 10.
    Acharya UR, Oh SL, Hagiwara Y et al (2018) Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed 161:103–113CrossRefGoogle Scholar
  11. 11.
    Acharya UR, Bhat S, Faust O et al (2015) Nonlinear dynamics measures for automated EEG-based sleep stage detection. Eur Neurol 74(5–6):268–287CrossRefGoogle Scholar
  12. 12.
    Jasper HH, Proctor LD, Knighton RS, Noshay WC, Costello RT (1958) Reticular formation of the brain. Little, Brown & Company, BostonGoogle Scholar
  13. 13.
    Chatrian GE, Lettich E, Nelson PL (1985) Ten percent electrode system for topographic studies of spontaneous and evoked EEG activity. Am J EEG Technol. CrossRefGoogle Scholar
  14. 14.
    Medithe JWC, Nelakuditi UR (2016) Study of normal and abnormal EEG. In: 2016 3rd International conference on advanced computing and communication systems (ICACCS), vol 1. IEEE, pp. 1–4Google Scholar
  15. 15.
    Phillips N (2016) Epilepsy with generalized seizures: symptoms, causes, and treatments. Available:
  16. 16.
    Acharya UR, Hagiwara Y, Deshpande SN, Suren S, Koh JEW, Oh SL, Arunkumar N, Ciaccio EJ, Lim CM (2018) Characterization of focal EEG signals: a review. Future Gener Comput Syst. CrossRefGoogle Scholar
  17. 17.
    Boggs JG (2009) Generalized EEG waveform abnormalities. Retrieved April 25, 2010, from
  18. 18.
    Bhattacharyya A, Pachori RB (2017) A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform. IEEE Trans Biomed Eng 64(9):2003–2015CrossRefGoogle Scholar
  19. 19.
    Hassan AR, Subasi A (2016) Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput Methods Programs Biomed. CrossRefGoogle Scholar
  20. 20.
    Zandi AS, Tafreshi R, Javidan M, Dumont GA (2013) Predicting epileptic seizures in scalp EEG based on a variational bayesian gaussian mixture model of zero-crossing intervals. IEEE Trans Biomed Eng 60(5):1401–1413CrossRefGoogle Scholar
  21. 21.
    Aarabi A, He B (2017) Seizure prediction in patients with focal hippocampal epilepsy. Clin Neurophysiol 128(7):1299–1307CrossRefGoogle Scholar
  22. 22.
    Truong ND, Nguyen AD, Kuhlmann L et al (2018) Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw 105:104–111CrossRefGoogle Scholar
  23. 23.
    Alotaiby TN, Alshebeili SA, Alotaibi FM, Alrshoud SR (2017) Epileptic seizure prediction using CSP and LDA for scalp EEG signals. Comput Intell Neurosci. CrossRefGoogle Scholar
  24. 24.
    Parvez MZ, Paul M (2017) Seizure prediction using undulated global and local features. IEEE Trans Biomed Eng 64(1):208–217CrossRefGoogle Scholar
  25. 25.
    Faust O, Hagiwara Y, Hong TJ et al (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Programs Biomed. CrossRefGoogle Scholar
  26. 26.
    Acharya UR, Hagiwara Y, Adeli H (2018) Automated seizure prediction. Epilepsy Behav 88:251–261CrossRefGoogle Scholar
  27. 27.
    Acharya UR, Sree SV, Alvin AP et al (2012) Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int J Neural Syst 22(02):1250002CrossRefGoogle Scholar
  28. 28.
    Tzimourta KD, Tzallas AT, Giannakeas N, et al (2018) Epileptic seizures classification based on long-term EEG signal wavelet analysis. In: IFMBE proceedingsGoogle Scholar
  29. 29.
    Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods. CrossRefGoogle Scholar
  30. 30.
    Yuan Q, Zhou W, Xu F et al (2018) Epileptic EEG identification via LBP operators on wavelet coefficients. Int J Neural Syst. CrossRefGoogle Scholar
  31. 31.
    Oweis RJ, Abdulhay EW (2011) Seizure classification in EEG signals utilizing Hilbert–Huang transform. Biomed Eng Online 10(1):38. CrossRefGoogle Scholar
  32. 32.
    Acharya UR, Sree SV, Suri JS (2011) Automatic detection of epileptic eeg signals using higher order cumulant features. Int J Neural Syst 21(5):403–414CrossRefGoogle Scholar
  33. 33.
    Acharya UR, Yanti R, Zheng JW et al (2013) Automated diagnosis of epilepsy using cwt, hos and texture parameters. Int J Neural Syst 23(03):1350009CrossRefGoogle Scholar
  34. 34.
    Acharya UR, Vinitha Sree S, Alvin APC, Suri JS (2012) Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework. Expert Syst Appl 10:9072–9078CrossRefGoogle Scholar
  35. 35.
    George ST, Balakrishnan R, Johnson JS, Jayakumar J (2017) Application and evaluation of independent component analysis methods to generalized seizure disorder activities exhibited in the brain. Clin EEG Neurosci. CrossRefGoogle Scholar
  36. 36.
    Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666CrossRefGoogle Scholar
  37. 37.
    Alotaiby TN, Alshebeili SA, Alshawi T et al (2014) EEG seizure detection and prediction algorithms: a survey. EURASIP J Adv Signal Process 2014(1):183CrossRefGoogle Scholar
  38. 38.
    Najafabadi MM, Villanustre F, Khoshgoftaar TM et al (2015) Deep learning applications and challenges in big data analytics. J Big Data 2(1):1CrossRefGoogle Scholar
  39. 39.
    Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefGoogle Scholar
  40. 40.
    Coşkun M, Yildirim Ö, Uçar A, Demir Y (2017) An overview of popular deep learning methods. Eur J Tech 7(2):165–176CrossRefGoogle Scholar
  41. 41.
    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. CrossRefGoogle Scholar
  42. 42.
    LeCun Y, Boser B, Denker JS et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput. CrossRefGoogle Scholar
  43. 43.
    Uçar A, Demir Y, Güzeliş C (2017) Object recognition and detection with deep learning for autonomous driving applications. Simulation 93(9):759–769CrossRefGoogle Scholar
  44. 44.
    Beşer F, Kizrak MA, Bolat B, Yildirim T (2018) Recognition of sign language using capsule networks. In: 2018 26th IEEE signal processing and communications applications conference (SIU)Google Scholar
  45. 45.
    Sarikaya R, Hinton GE, Deoras A (2014) Application of deep belief networks for natural language understanding. IEEE/ACM Trans Audio Speech Lang Process. CrossRefGoogle Scholar
  46. 46.
    Abdel-hamid O, Deng L, Yu D (2013) Exploring convolutional neural network structures and optimization techniques for speech recognition. In: 14th Annual conference of the international speech communication association (INTERSPEECH 2013), pp 3366–3370Google Scholar
  47. 47.
    Mnih V, Kavukcuoglu K, Silver D et al (2015) Playing atari with deep reinforcement learning Volodymyr. Nature. CrossRefGoogle Scholar
  48. 48.
    Yildirim O, Tan RS, Acharya UR (2018) An efficient compression of ECG signals using deep convolutional autoencoders. Cogn Syst Res. CrossRefGoogle Scholar
  49. 49.
    Yildirim Ö (2018) A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput Biol Med 96:189–202CrossRefGoogle Scholar
  50. 50.
    Acharya UR, Oh SL, Hagiwara Y et al (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med. CrossRefGoogle Scholar
  51. 51.
    Yıldırım Ö, Pławiak P, Tan RS, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med. CrossRefGoogle Scholar
  52. 52.
    Obeid I, Picone J (2016) The temple university hospital EEG data corpus. Front Neurosci. CrossRefGoogle Scholar
  53. 53.
    Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. MathSciNetCrossRefzbMATHGoogle Scholar
  54. 54.
    Lopez S, Suarez G, Jungreis D et al (2016) Automated identification of abnormal adult EEGs. In: 2015 IEEE signal processing in medicine and biology symposium—proceedingsGoogle Scholar
  55. 55.
    American Clinical Neurophysiology Society (2006) Guideline 6: a proposal for standard montages to be used in clinical EEG. J Clin Neurophysiol 23(2):111CrossRefGoogle Scholar
  56. 56.
    Chollet F (2015) Keras: Deep learning library for theano and tensorflow., 7(8)
  57. 57.
    Lopez S (2017) Automated identification of abnormal EEGs. MS thesis, Temple University. Available:

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Engineering, Engineering FacultyMunzur UniversityTunceliTurkey
  2. 2.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  3. 3.Department of Biomedical Engineering, School of Science and TechnologySingapore School of Social SciencesSingaporeSingapore
  4. 4.Faculty of Health and Medical Sciences, School of MedicineTaylor’s UniversitySubang JayaMalaysia

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