Advertisement

Identifying brain abnormalities from electroencephalogram using evolutionary gravitational neocognitron neural network

  • P. GomathiEmail author
  • S. Baskar
  • P. Mohamed Shakeel
  • V. R. Sarma Dhulipala
Article

Abstract

Now-a-day’s brain abnormality is one among the dangerous neurological disorders that occurs because of the birth defects, brain stroke, brain injuries, genetic mutation and brain tumor. This brain disorder creates continue melancholia, bipolar disorder, stress disorder (PTSD) and so on. Due to this serious impact of the brain abnormalities, need to be identified within the beginning stage for eliminating the difficulties in humans day to day life. So, the automatic brain abnormality prediction process is created by utilizing electroencephalogram (EEG) for avoiding risk factor in future. As per the discussion, this paper introduces the evolutionary gravitational Neocognitron neural network(GNNN) for recognizing brain abnormalities with effective manner and it is especially suited for humans in war field. Initially, EEG signal is collected from patient; unwanted signal information is eliminated by using multi-linear principal component analysis from pre-processed signal, various features are extracted using affine invariant component analysis method and greedy global optimized features are chosen. The chosen features are analyzed using multi-layer virtual cortex model for predicting abnormal features. Finally the potency of the brain related abnormality prediction process developed using MATLAB tool and efficiency is examined using F-measure, Mathew correlation coefficient error rate, sensitivity, specificity, and accuracy. Along these lines the proposed framework effectively perceives the cerebrum variation from the norm with most astounding precision up to 99.48% with error rate.

Keywords

Brain abnormality Electroencephalogram (EEG) Multi-linear principal component Affine invariant component analysis Multi-layer virtual cortex Mathew correlation coefficient and accuracy 

Notes

Acknowledgements

The authors would like to thank SERB, Science and Engineering Research Board, New Delhi, India for the funding to carry out the research work from N.S.N College of Engineering and Technology, Karur, Tamil Nadu,India.

Compliance with ethical standards

Conflicts of interest

None.

References

  1. 1.
    Baskar S, Dhulipala VR (2018) Biomedical rehabilitation: data error detection and correction using two dimensional linear feedback shift register based cyclic redundancy check. J Med Imag Health Inform 8(4):805–808CrossRefGoogle Scholar
  2. 2.
    Baskar S, Dhulipala VR (2018) M-CRAFT-modified multiplier algorithm to reduce overhead in fault tolerance algorithm in wireless sensor networks. J Comput Theor Nanosci 15(4):1395–1401CrossRefGoogle Scholar
  3. 3.
    Black PE (2005) Greedy algorithm. Dictionary of algorithms and data structures. U.S.National Institute of Standards and Technology (NIST)Google Scholar
  4. 4.
    De Lucia M, Fritschy J, Dayan P, Holder DS (2007) A novel method for automated classification of epileptic form activity in the human electroencephalogram-based on independent component analysis. Med Bio EngComput: 1–11Google Scholar
  5. 5.
    Fong S, Deb S, Xin-She Y, Jinyan L Metaheuristic swarm search for feature selection in life science classification. IEEE IT Prof Mag 16(4):24–29Google Scholar
  6. 6.
    Kalaivani V, Kalaivani V, Devi A (2014) Analysis of EEG signal for the detection of brain abnormalities. Int J Comput Applic: 1–7Google Scholar
  7. 7.
    Kang D, Zhizeng L (2012) A method of denoising multi-channel EEG signals fast based on PCA and DEBSS algorithm. Comput Sci Electron Eng (ICCSEE), 2012 Int Conf 3:322–326CrossRefGoogle Scholar
  8. 8.
    Khan RA, Mandwi I (2017) An approach on multi-objective unsupervised feature selection using genetic algorithm. Innovations in Information Embedded and Communication Systems (ICIIECS) 2017 International Conf: 1–5Google Scholar
  9. 9.
    Kumar U, Inbarani H (2016) PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task. Neural Comput Appl Springer: 1–20Google Scholar
  10. 10.
    Kumari P, Vaish A (2015) Information-theoretic measures on intrinsic mode function for the individual identification using EEG sensors. IEEE Sensors J 15(9):4950–4960CrossRefGoogle Scholar
  11. 11.
    Lehnertz F, Mormann T, Kreuz R, Andrzejak C, Rieke PD, Elger C (2003) Seizure prediction by nonlinear EEG analysis. IEEE Eng Med Biol MagGoogle Scholar
  12. 12.
    Lowe DG (2004) Distinctive image features from scale-invariant Keypoints. Int J Comput Vis 60Google Scholar
  13. 13.
    Martinez-Leon J-A, Cano-Izquierdo J-M, Ibarrola J (2015) Feature selection applying statistical and Neurofuzzy methods to EEG-based BCI. Comput Intell Neurosci 2015Google Scholar
  14. 14.
    B. S. Mashford; A. JimenoYepes; I. Kiral-Kornek; J. Tang; S. Harrer, “Neural-network-based analysis of EEG data using the neuromorphicTrueNorth chip for brain-machine interfaces”, IBM J Res Dev, IEEE, 2017, Volume: 61, Issue: 2/3, Pages: 7:1–7:6Google Scholar
  15. 15.
    Mohamed Shakeel P, Tobely TEE, Al-Feel H, Manogaran G, Baskar, S (2019) Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access: 1Google Scholar
  16. 16.
    Murugesan and Dr.(Mrs.).R. Sukanesh (2009) Towards detection of brain tumor in electroencephalogram signals using support vector machines. Int J Comput Theory Eng 1(5):622–631CrossRefGoogle Scholar
  17. 17.
    Nguyen H, Franke K, Petrovic S (2010) Towards a generic feature-selection measure for intrusion detection. Proc Int Conf Pattern Recogn (ICPR), Istanbul, TurkeyGoogle Scholar
  18. 18.
    Panagakis Y, Kotropoulos C, Arce GR (2010) Non-negative multilinear principal component analysis of auditory temporal modulations for music genre classification. IEEE Trans Audio Speech Lang Process 18(3):576–588CrossRefGoogle Scholar
  19. 19.
    Pavan Kumar K, Prasad K, Ramakrishna MV (2013) Feature extraction using sparse SVD for biometric fusion in multimodal authentication. Int J Netw Sec Appl (IJNSA) 5(4)Google Scholar
  20. 20.
    Pazhanirajan D (2014) Epileptic seizure classification of EEG image using SVM. Int J Innov Res Sci Eng Technol 3(8):15391–15395CrossRefGoogle Scholar
  21. 21.
    Peng HC, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238CrossRefGoogle Scholar
  22. 22.
    Rao DDV(2014) Detecting sleep disorders based on EEG signals by using discrete wavelet transform. Green computing communication and electrical engineering in IEEEGoogle Scholar
  23. 23.
    Satapathy SK, Dehuri S (2016) An empirical analysis of different machine learning techniques for classification of EEG signal to detect epileptic seizure. Int J Appl Eng Res ISSN 0973–4562 11(1):120–126Google Scholar
  24. 24.
    Shah JA, Kucic M (2014) Fast detection of brain disorders using EEG signal. http://www.i-scholar.in/index.php/CiiTDSP/article/view/105169, volume 6, issue 6, pp. 194–197
  25. 25.
    Shakeel PM, Manogaran G. (2018) Prostate cancer classification from prostate biomedical data using ant rough set algorithm with radial trained extreme learning neural network. Heal Technol: 1–9.  https://doi.org/10.1007/s12553-018-0279-6
  26. 26.
    Shakeel PM, Baskar S, Dhulipala VS, Mishra S, Jaber MM (2018) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42(10):186.  https://doi.org/10.1007/s10916-018-1045-z CrossRefGoogle Scholar
  27. 27.
    Shakeel PM, Baskar S, Dhulipala VS, Jaber MM (2018) Cloud based framework for diagnosis of diabetes mellitus using K-means clustering. Health Inform Sci Syst 6(1):16.  https://doi.org/10.1007/s13755-018-0054-0 CrossRefGoogle Scholar
  28. 28.
    Shakeel PM, Tolba A, Al-Makhadmeh A-MZ, Jaber MM (2019) Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks. Neural Comput & Applic: 1–14.  https://doi.org/10.1007/s00521-018-03972-2
  29. 29.
    Sridhar KP, Baskar S, Shakeel PM, Dhulipala VS (2018) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Humaniz Compu:1–9.  https://doi.org/10.1007/s12652-018-1058-y
  30. 30.
    Tuytelaars T, Van Gool L (2004) Matching widely separated views based on affine invariant regions. IJCV 59(1):61–85CrossRefGoogle Scholar
  31. 31.
    Vatankhah M, Toliyat A (2016) Pain level measurement using discrete wavelet transform. Int J Eng Technol 8(5):380–384CrossRefGoogle Scholar
  32. 32.
    Vikhar PA (2016) Evolutionary algorithms: a critical review and its future prospects. Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). Jalgaon: 261–265. ISBN 978-1-5090-0467-6Google Scholar
  33. 33.
    Wen T, Zhang Z (2018) Deep convolution neural network and autoencoders-based unsupervised feature learning of EEG signals. IEEE J Mag 6:25399–25410Google Scholar
  34. 34.
    Xue B, Qin AK, Zhang M (2014) An archive based particle swarm optimisation for feature selection in classification. Evol Comput (CEC) 2014 IEEE Congress: 3119–3126Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • P. Gomathi
    • 1
    Email author
  • S. Baskar
    • 2
  • P. Mohamed Shakeel
    • 3
  • V. R. Sarma Dhulipala
    • 4
  1. 1.Department of Electrical and Electronics EngineeringN.S.N. College of Engineering and TechnologyKarurIndia
  2. 2.Department of Electronics and Communication Engineering/Centre for Interdisciplinary ResearchKarpagam Academy of Higher EducationCoimbatoreIndia
  3. 3.Faculty of Information and Communication TechnologyUniversiti Teknikal MalaysiaMelakaMalaysia
  4. 4.Department of PhysicsAnna University, BIT-CampusTiruchirappalliIndia

Personalised recommendations