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Disease Prediction Using Artificial Intelligence: A Case Study on Epileptic Seizure Prediction

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Enhanced Telemedicine and e-Health

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 410))

Abstract

Artificial Intelligence uses statistical theory to generate mathematical models from samples. After a model is generated, its depiction and algorithmic solution for understanding require being competent as well. Biomedical data related to different diseases are recorded from a body, which can be at the organ level, cell level or molecular level. Biomedical data is mainly utilized to predict, diagnose or identify particular physiological or pathological conditions. The goal of biomedical data analysis is exact modelling of data by employing feature extraction, feature selection and dimension reduction for the prediction and detection of upcoming pathological problems by utilizing artificial intelligence algorithms. This chapter explains the steps of biomedical data analysis and how artificial intelligence techniques are utilized in disease prediction. An automated epileptic seizure prediction and detection approach based on deep learning is also presented. Since Deep Learning can automatically extract and learn features, the electroencephalography (EEG) time series are fed into the deep learning model. Deep Learning has been utilized in the prediction and detection of epileptic seizures. Since EEG recordings are high dimensional data, a Convolutional Neural Network (CNN) is suitable for this use. The results show that CNN achieved a testing accuracy of 99.09% accuracy for the prediction of epileptic seizures from EEG signals.

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References

  1. J. Muthuswamy, Biomedical signal analysis, in Standard Handbook of Biomedical Engineering and Design, vol. 14, ed. by M. Kutz (McGraw-Hill Education, New York, 2004), pp. 18

    Google Scholar 

  2. L.T. Mainardi, A.M. Bianchi, S. Cerutti, Digital biomedical signal acquisition and processing, in Medical Devices and Systems (CRC Press, 2006), pp. 49–72

    Google Scholar 

  3. S. Palaniappan, R. Awang, Intelligent Heart Disease Prediction System Using Data Mining Techniques (2008), pp. 108–115

    Google Scholar 

  4. R. Wu, W. Peters, M.W. Morgan, The next generation of clinical decision support: linking evidence to best practice. J. Healthc. Inf. Manag. JHIM 16(4), 50 (2002)

    Google Scholar 

  5. S.D. Culler, M.L. Parchman, M. Przybylski, Factors related to potentially preventable hospitalizations among the elderly. Med. Care, 804–817 (1998)

    Google Scholar 

  6. N. Yiannakoulias, D. Schopflocher, L. Svenson, Using administrative data to understand the geography of case ascertainment. Chronic Dis. Can. 30(1), 20–28 (2009)

    Google Scholar 

  7. T. McCormick, C. Rudin, D. Madigan, A hierarchical model for association rule mining of sequential events: an approach to automated medical symptom prediction (2011)

    Google Scholar 

  8. E.S. Fisher, D.J. Malenka, J.E. Wennberg, N.P. Roos, Technology assessment using insurance claims: example of prostatectomy. Int. J. Technol. Assess. Health Care 6(2), 194–202 (1990)

    Article  Google Scholar 

  9. M.E. Hossain, A. Khan, M.A. Moni, S. Uddin, Use of electronic health data for disease prediction: a comprehensive literature review. IEEE/ACM Trans. Comput. Biol. Bioinform. (2019)

    Google Scholar 

  10. C. Zhang, L. Zhu, C. Xu, R. Lu, PPDP: an efficient and privacy-preserving disease prediction scheme in cloud-based e-Healthcare system. Future Gener. Comput. Syst. 79, 16–25 (2018)

    Article  Google Scholar 

  11. H. Yin, N.K. Jha, A health decision support system for disease diagnosis based on wearable medical sensors and machine learning ensembles. IEEE Trans. Multi-Scale Comput. Syst. 3(4), 228–241 (2017)

    Article  Google Scholar 

  12. G. Bieber, M. Haescher, M. Vahl, Sensor requirements for activity recognition on smart watches (2013), pp. 1–6

    Google Scholar 

  13. D. Malathi, R. Logesh, V. Subramaniyaswamy, V. Vijayakumar, A.K. Sangaiah, Hybrid reasoning-based privacy-aware disease prediction support system. Comput. Electr. Eng. 73, 114–127 (2019)

    Article  Google Scholar 

  14. A. Petrosian, D. Prokhorov, R. Homan, R. Dasheiff, D. Wunsch II, Recurrent neural network based prediction of epileptic seizures in intra-and extracranial EEG. Neurocomputing 30(1–4), 201–218 (2000)

    Article  Google Scholar 

  15. Κ.Μ. Tsiouris, V.C. Pezoulas, M. Zervakis, S. Konitsiotis, D.D. Koutsouris, D.I. Fotiadis, A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput. Biol. Med. 99, 24–37 (2018)

    Article  Google Scholar 

  16. U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, H. Adeli, 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 

  17. H. Khan, L. Marcuse, M. Fields, K. Swann, B. Yener, Focal onset seizure prediction using convolutional networks. IEEE Trans. Biomed. Eng. 65(9), 2109–2118 (2017)

    Article  Google Scholar 

  18. R. San-Segundo, M. Gil-Martín, L.F. D’Haro-Enríquez, J.M. Pardo, Classification of epileptic EEG recordings using signal transforms and convolutional neural networks. Comput. Biol. Med. 109, 148–158 (2019)

    Article  Google Scholar 

  19. R. Rosas-Romero et al., Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals. Comput. Biol. Med. 111, 103355 (2019)

    Article  Google Scholar 

  20. D. Jain, V. Singh, Feature selection and classification systems for chronic disease prediction: a review. Egypt. Inform. J. 19(3), 179–189 (2018)

    Article  Google Scholar 

  21. S. Huda, J. Yearwood, H.F. Jelinek, M.M. Hassan, G. Fortino, M. Buckland, A hybrid feature selection with ensemble classification for imbalanced healthcare data: A case study for brain tumor diagnosis. IEEE Access 4, 9145–9154 (2016)

    Article  Google Scholar 

  22. M. Chen, J. Yang, X. Zhu, X. Wang, M. Liu, J. Song, Smart home 2.0: Innovative smart home system powered by botanical IoT and emotion detection. Mob. Netw. Appl. 22(6), 1159–1169 (2017)

    Article  Google Scholar 

  23. M. Chen, Y. Zhang, M. Qiu, N. Guizani, Y. Hao, SPHA: smart personal health advisor based on deep analytics. IEEE Commun. Mag. 56(3), 164–169 (2018)

    Article  Google Scholar 

  24. K. He, J. Chen, R. Du, Q. Wu, G. Xue, X. Zhang, Deypos: deduplicatable dynamic proof of storage for multi-user environments. IEEE Trans. Comput. 65(12), 3631–3645 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  25. M. Usama, B. Ahmad, W. Xiao, M.S. Hossain, G. Muhammad, Self-attention based recurrent convolutional neural network for disease prediction using healthcare data. Comput. Methods Programs Biomed. 190, 105191 (2020)

    Article  Google Scholar 

  26. A.K. Verma, S. Pal, S. Kumar, Comparison of skin disease prediction by feature selection using ensemble data mining techniques. Inform. Med. Unlocked 16, 100202 (2019)

    Article  Google Scholar 

  27. E. Alpaydin, Introduction to Machine Learning (MIT press, 2014)

    Google Scholar 

  28. N. Barakat, A.P. Bradley, M.N.H. Barakat, Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans. Inf. Technol. Biomed. 14(4), 1114–1120 (2010)

    Article  Google Scholar 

  29. A. Ahlemeyer-Stubbe, S. Coleman, A Practical Guide to Data Mining for Business and Industry (Wiley, 2014)

    Google Scholar 

  30. Y. Bengio, Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)

    Google Scholar 

  31. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Google Scholar 

  32. A. Subasi, Practical guide for biomedical signals analysis using machine learning techniques, a MATLAB based approach (Elsevier, First, 2019)

    Google Scholar 

  33. N. Fayyaz Khan, M. Kamil, A. Hussain, M. Sajjad, Detection and classification of vehicle-type by using convolution neural network. Presented at the The 4th International Conference on Next Generation Computing 2018 (2018)

    Google Scholar 

  34. S.N. Rakhade, F.E. Jensen, Epileptogenesis in the immature brain: emerging mechanisms. Nat. Rev. Neurol. 5(7), 380 (2009)

    Article  Google Scholar 

  35. M.J. England, C.T. Liverman, A.M. Schultz, L.M. Strawbridge, Epilepsy across the spectrum: promoting health and understanding.: a summary of the Institute of Medicine report. Epilepsy Behav. 25(2), 266–276 (2012)

    Article  Google Scholar 

  36. K. Rasheed et al., Machine learning for predicting epileptic seizures using EEG signals: a review. IEEE Rev. Biomed. Eng (2020), pp. 1–1. https://doi.org/10.1109/rbme.2020.3008792

  37. L. Sörnmo, P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications, vol. 8 (Academic Press, San Diego, California, 2005)

    Google Scholar 

  38. A. Subasi, Biomedical signal analysis and its usage in healthcare, in Biomedical Engineering and its Applications in Healthcare (Springer, 2019), pp. 423–452

    Google Scholar 

  39. M.M.N. Mannan, M.A. Kamran, M.Y. Jeong, Identification and removal of physiological artifacts from electroencephalogram signals: A review. IEEE Access 6, 30630–30652 (2018)

    Article  Google Scholar 

  40. H. Chu, C.K. Chung, W. Jeong, K.-H. Cho, Predicting epileptic seizures from scalp EEG based on attractor state analysis. Comput. Methods Programs Biomed. 143, 75–87 (2017)

    Article  Google Scholar 

  41. N.D. Truong et al., Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw. 105, 104–111 (2018)

    Article  Google Scholar 

  42. Y. Gao, B. Gao, Q. Chen, J. Liu, Y. Zhang, Deep convolutional neural network-based epileptic electroencephalogram (EEG) signal classification. Front. Neurol. 11, 375 (2020). https://doi.org/10.3389/fneur.2020.00375

    Article  Google Scholar 

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

    Article  Google Scholar 

  44. X. Wei, L. Zhou, Z. Zhang, Z. Chen, Y. Zhou, Early prediction of epileptic seizures using a long-term recurrent convolutional network. J. Neurosci. Methods 327, 108395 (2019)

    Article  Google Scholar 

  45. C.-L. Liu, B. Xiao, W.-H. Hsaio, V.S. Tseng, Epileptic Seizure prediction with multi-view convolutional neural networks. IEEE Access 7, 170352–170361 (2019)

    Article  Google Scholar 

  46. S. Rukhsar, Y. Khan, O. Farooq, M. Sarfraz, A. Khan, Patient-specific epileptic seizure prediction in long-term scalp eeg signal using multivariate statistical process control. IRBM 40(6), 320–331 (2019)

    Article  Google Scholar 

  47. S. Sanei, Adaptive Processing of Brain Signals. The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, (John Wiley & Sons, UK, 2013)

    Google Scholar 

  48. B.R. Bakshi, Multiscale PCA with application to multivariate statistical process monitoring. AIChE J. 44(7), 1596–1610 (1998)

    Article  Google Scholar 

  49. B. Graimann, B. Allison, G. Pfurtscheller, Brain–computer interfaces: a gentle introduction, in Brain-Computer Interfaces (Springer, 2009), pp. 1–27

    Google Scholar 

  50. S. Siuly, Y. Li, Y. Zhang, EEG Signal Analysis and Classification (Springer, 2016)

    Google Scholar 

  51. A. Phinyomark, F. Quaine, S. Charbonnier, C. Serviere, F. Tarpin-Bernard, Y. Laurillau, EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Syst. Appl. 40(12), 4832–4840 (2013)

    Article  Google Scholar 

  52. A. Wołczowski, R. Zdunek, Electromyography and mechanomyography signal recognition: Experimental analysis using multi-way array decomposition methods. Biocybern. Biomed. Eng. 37(1), 103–113 (2017). https://doi.org/10.1016/j.bbe.2016.09.004

    Article  Google Scholar 

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

    Google Scholar 

  54. J.-G. Lee et al., Deep learning in medical imaging: general overview. Korean J. Radiol. 18(4), 570–584 (2017)

    Article  Google Scholar 

  55. C. Angermueller, T. Pärnamaa, L. Parts, O. Stegle, Deep learning for computational biology. Mol. Syst. Biol. 12(7), 878 (2016)

    Article  Google Scholar 

  56. E. Asgari, M.R. Mofrad, Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS ONE 10(11), e0141287 (2015)

    Article  Google Scholar 

  57. G. Li, C.H. Lee, J.J. Jung, Y.C. Youn, D. Camacho, Deep learning for EEG data analytics: a survey. Concurr. Comput. Pract. Exp. e5199 (2019)

    Google Scholar 

  58. D. Hassabis, D. Kumaran, C. Summerfield, M. Botvinick, Neuroscience-inspired artificial intelligence. Neuron 95(2), 245–258 (2017)

    Article  Google Scholar 

  59. V. Jain, H.S. Seung, S.C. Turaga, Machines that learn to segment images: a crucial technology for connectomics. Curr. Opin. Neurobiol. 20(5), 653–666 (2010)

    Article  Google Scholar 

  60. I. Kiral-Kornek et al., Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine 27, 103–111 (2018)

    Article  Google Scholar 

  61. A. Antoniades, L. Spyrou, C.C. Took, S. Sanei, Deep Learning for Epileptic Intracranial EEG Data (2016), pp. 1–6

    Google Scholar 

  62. N.D. Truong, A.D. Nguyen, L. Kuhlmann, M.R. Bonyadi, J. Yang, O. Kavehei, A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis. ArXiv170701976 (2017)

    Google Scholar 

  63. R. Begg, D.T. Lai, M. Palaniswami, Computational Intelligence in Biomedical Engineering (CRC Press, 2008)

    Google Scholar 

  64. M. Winterhalder, T. Maiwald, H. Voss, R. Aschenbrenner-Scheibe, J. Timmer, A. Schulze-Bonhage, The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav. 4(3), 318–325 (2003)

    Article  MATH  Google Scholar 

  65. A.H. Shoeb, Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment (2009)

    Google Scholar 

  66. A.L. Goldberger et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), Art. no. 23 (2000)

    Google Scholar 

  67. M. Hall, I. Witten, E. Frank, Data mining: Practical machine learning tools and techniques. Kaufmann Burlingt. (2011)

    Google Scholar 

  68. M. Sokolova, N. Japkowicz, S. Szpakowicz, Beyond Accuracy, F-score and ROC: A Family of Discriminant Measures for Performance Evaluation (2006), pp. 1015–1021

    Google Scholar 

  69. J. Cohen, A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37–46 (1960)

    Article  Google Scholar 

  70. Z. Yang, M. Zhou, Kappa statistic for clustered physician–patients polytomous data. Comput. Stat. Data Anal. 87, 1–17 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  71. N. LaPierre, C.J.-T. Ju, G. Zhou, W. Wang, MetaPheno: a critical evaluation of deep learning and machine learning in metagenome-based disease prediction. Methods 166, 74–82 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, 20520, Finland

    Abdulhamit Subasi

  2. Department of Computer Science, College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia

    Abdulhamit Subasi

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  1. Abdulhamit Subasi
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Correspondence to Abdulhamit Subasi .

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Editors and Affiliations

  1. Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, Oliveira do Hospital, Coimbra, Portugal

    Gonçalo Marques

  2. Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Majhitar, Sikkim, India

    Akash Kumar Bhoi

  3. University of Valladolid, Valladolid, Spain

    Isabel de la Torre Díez

  4. Universidad de Deusto, Bilbao, Vizcaya, Spain

    Begonya Garcia-Zapirain

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Subasi, A. (2021). Disease Prediction Using Artificial Intelligence: A Case Study on Epileptic Seizure Prediction. In: Marques, G., Kumar Bhoi, A., de la Torre Díez, I., Garcia-Zapirain, B. (eds) Enhanced Telemedicine and e-Health. Studies in Fuzziness and Soft Computing, vol 410. Springer, Cham. https://doi.org/10.1007/978-3-030-70111-6_14

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