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Contribution of Application of Deep Learning Approaches on Biomedical Data in the Diagnosis of Neurological Disorders: A Review on Recent Findings

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Advances in Computational Intelligence, Security and Internet of Things (ICCISIoT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1192))

Abstract

Recent developments in Artificial intelligence and their applications in Medical health diagnosis have shown promising results especially in the diagnosis of brain disorders, thereby paving the way for better healthcare and early detection of Neural disorders. Several diagnostic tests are routinely performed on humans to evaluate the nature of brain signals and images. These diagnostic tests typically rely on the physiological signals and neuroimaging analysis commonly performed by an expert manually. Large databases of clinical data of recorded brain signals and images are available. Secondary use of these medical records for precision medicine and predictive analysis involves machine learning which has fueled the excitement of medical experts and data scientists equally. For an effective medical diagnosis, intelligent behavior is the prerequisite for learning, and there has been a deep insight in this particular area for small diagnostic problems. Symbolic learning, statistical methods and neural networks in a broader perspective have contributed to the development of machine learning in general and medical diagnosis in particular. Deep learning, a recent trend in machine learning has been able to produce exceptionally accurate results in the diagnosis and treatment of various diseases. This review article covers the exciting contributions of the many researchers involved in this field and highlights the challenges considered critical in this area of research.

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References

  1. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2018)

    Article  Google Scholar 

  2. Acharya, U.R., Sree, S.V., Suri, J.S.: Automatic detection of epileptic EEG signals using higher order cumulant features. International J. Neural Syst. 21(05), 403–414 (2011)

    Article  Google Scholar 

  3. Jabardi, M.H., Al-Fatlawi, A.H., Ling, S.H.: Efficient diagnosis system for parkinson’s disease using deep belief network. IEEE Congress on Evolutionary Computation (CEC), Vancouver, Canada (2016)

    Google Scholar 

  4. Association, Alzheimer’s, et al. 2018 alzheimer’s disease facts and figures. Alzheimer’s Dement. 14(3), 367–429 (2018)

    Google Scholar 

  5. Avcu, M.T., Zhang, Z., Chan, D.W.S.: Seizure detection using least eeg channels by deep convolutional neural network. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1120–1124. IEEE (2019)

    Google Scholar 

  6. Bi, X., Wang, H.: Early alzheimer’s disease diagnosis based on eeg spectral images using deep learning. Neural Networks 114, 119–135 (2019)

    Article  Google Scholar 

  7. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    MATH  Google Scholar 

  8. Butler, K.T., Davies, D.W., Cartwright, H., Isayev, O., Walsh, A.: Machine learning for molecular and materials science. Nature 559(7715), 547 (2018)

    Article  Google Scholar 

  9. Choi, H., Ha, S., Im, H.J., Paek, S.H., Lee, D.S.: Refining diagnosis of parkinson’s disease with deep learning-based interpretation of dopamine transporter imaging. NeuroImage Clin. 16, 586–594 (2017)

    Article  Google Scholar 

  10. de Lau, L., Breteler, M.M.B.: Epidemiology of parkinson’s disease. Lancet Neurol. 5(6), 525–535 (2006)

    Article  Google Scholar 

  11. Deng, L., Yu, D., et al.: Deep learning: methods and applications. Found. Trends® Sign. Process. 7(3–4), 197–387 (2014)

    Article  MathSciNet  Google Scholar 

  12. Devasahayam, S.R.: Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-5332-1

    Book  Google Scholar 

  13. Faust, O., Bairy, M.G.: Nonlinear analysis of physiological signals: a review. J. Mech. Med. Biol. 12(04), 1240015 (2012)

    Article  Google Scholar 

  14. Foster, K.R., Koprowski, R., Skufca, J.D.: Machine learning, medical diagnosis, and biomedical engineering research-commentary. Biomed. Eng. Online 13(1), 94 (2014)

    Article  Google Scholar 

  15. Grover, S., Bhartia, S., Yadav, A., Seeja, K.R., et al.: Predicting severity of parkinson’s disease using deep learning. Procedia Comput. Sci. 132, 1788–1794 (2018)

    Article  Google Scholar 

  16. Hammerla, N.Y., Fisher, J., Andras, P., Rochester, L., Walker, R., Plötz, T.: PD disease state assessment in naturalistic environments using deep learning. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  17. Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Networks 4(2), 251–257 (1991)

    Article  MathSciNet  Google Scholar 

  18. Hosseini-Asl, E., Keynton, R., El-Baz, A.: Alzheimer’s disease diagnostics by adaptation of 3D convolutional network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 126–130. IEEE (2016)

    Google Scholar 

  19. Ahmad, M.O., Swamy, M.S., Jabason, E.: Deep structural and clinical feature learning for semi-supervised multiclass prediction of alzheimer’s disease. In: 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 791–794. IEEE (2018)

    Google Scholar 

  20. Joshi, V., Pachori, R.B., Vijesh, A.: Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed. Sign. Process. Control 9, 1–5 (2014)

    Article  Google Scholar 

  21. Kireeva II, N., Baskin, H.A.G., Horvath, D., Marcou, G., Varnek, A.: Generative topographic mapping (GTM): universal tool for data visualization, structure-activity modeling and dataset comparison. Molecular Inform. 31(3–4), 301–312 (2012)

    Article  Google Scholar 

  22. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17 (2015)

    Article  Google Scholar 

  23. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  24. Lee, G., Nho, K., Kang, B., Sohn, K.-A., Kim, D.: Predicting alzheimer’s disease progression using multi-modal deep learning approach. Sci. Rep. 9(1), 1952 (2019)

    Article  Google Scholar 

  25. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  26. Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D.: Early diagnosis of alzheimer’s disease with deep learning. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI), pp. 1015–1018. IEEE (2014)

    Google Scholar 

  27. McAndrew, P.D., Potash, D.L., Higgins, B., Wayand, J., Held, J.: Expert system for providing interactive assistance in solving problems such as health care management, 14 May 1996. US Patent 5,517,405

    Google Scholar 

  28. Mitchell, T.M.: The discipline of machine learning, vol. 9. Carnegie Mellon University, School of Computer Science, Machine Learning (2006)

    Google Scholar 

  29. Oh, S.L., et al.: A deep learning approach for parkinson’s disease diagnosis from EEG signals. Neural Comput. Appl. 1–7 (2018)

    Google Scholar 

  30. Peek, N., Combi, C., Marin, R., Bellazzi, R.: Thirty years of artificial intelligence in medicine (aime) conferences: a review of research themes. Artif. Intell. Med. 65(1), 61–73 (2015)

    Article  Google Scholar 

  31. Quaglini, S., Barahona, P., Andreassen, S. (eds.): AIME 2001. LNCS (LNAI), vol. 2101. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-48229-6

    Book  Google Scholar 

  32. Sajda, P.: Machine learning for detection and diagnosis of disease. Annu. Rev. Biomed. Eng. 8, 537–565 (2006)

    Article  Google Scholar 

  33. Spasov, S., Passamonti, L., Duggento, A., Liò, P., Toschi, N., Initiative, A.D.N., et al.: A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to alzheimer’s disease. Neuroimage 189, 276–287 (2019)

    Article  Google Scholar 

  34. Tan, J.H., Acharya, U.R., Bhandary, S.V., Chua, K.C., Sivaprasad, S.: Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J. Comput. Sci. 20, 70–79 (2017)

    Article  Google Scholar 

  35. Ullah, I., Hussain, M., Aboalsamh, H., et al.: An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst. Appl. 107, 61–71 (2018)

    Article  Google Scholar 

  36. Van Den Heuvel, M.P., Pol, H.E.H.: Exploring the brain network: a review on resting-state fmri functional connectivity. European Neuropsychopharmacol. 20(8), 519–534 (2010)

    Article  Google Scholar 

  37. WHO. Neurological disorders public health disorders. Neurological disorders

    Google Scholar 

  38. Yang, H.-C., Islam, M.M., Li, Y.-C.J.: Potentiality of deep learning application in healthcare. Comput. Methods Programs Biomed. 161, 1 (2018)

    Article  Google Scholar 

  39. Yuan, Y., et al.: A novel channel-aware attention framework for multi-channel eeg seizure detection via multi-view deep learning. In: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 206–209. IEEE (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Aligarh Muslim University, Aligarh, 202002, Uttar Pradesh, India

    Waseem Ahmad Mir &  Izharuddin

  2. Jamia Millia Islamia, New Delhi, 110025, India

    Iqra Nissar

Authors
  1. Waseem Ahmad Mir
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  2. Izharuddin
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  3. Iqra Nissar
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Corresponding author

Correspondence to Waseem Ahmad Mir .

Editor information

Editors and Affiliations

  1. National Institute of Technology Agartala, Agartala, India

    Ashim Saha

  2. National Institute of Technology Agartala, Agartala, India

    Nirmalya Kar

  3. National Institute of Technology Agartala, Agartala, India

    Suman Deb

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Ahmad Mir, W., Izharuddin, Nissar, I. (2020). Contribution of Application of Deep Learning Approaches on Biomedical Data in the Diagnosis of Neurological Disorders: A Review on Recent Findings. In: Saha, A., Kar, N., Deb, S. (eds) Advances in Computational Intelligence, Security and Internet of Things. ICCISIoT 2019. Communications in Computer and Information Science, vol 1192. Springer, Singapore. https://doi.org/10.1007/978-981-15-3666-3_8

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  • DOI: https://doi.org/10.1007/978-981-15-3666-3_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3665-6

  • Online ISBN: 978-981-15-3666-3

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