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access icon free Hardware design of multiclass SVM classification for epilepsy and epileptic seizure detection

An automatic detection system for distinguishing healthy, ictal, and inter-ictal EEG signals plays an important role in medical practice. This paper presents a very large scale integration (VLSI) architecture of three-class classification for epilepsy and seizure detection. In order to find out the most efficient three-class classification scheme for hardware implementation, several multiclass non-linear support vector machine (NLSVM) classifiers are compared and validated using software implementation. Finally, the one-against-one (OAO) multiclass NLSVM is selected due to its highest accuracy. The designed system consists of a discrete wavelet transform (DWT)-based feature extraction module, a modified sequential minimal optimization (MSMO) training module, and an OAO multiclass classification module. A lifting structure of Daubechies order 4 wavelet is introduced in three-level DWT to save circuit area and speed up the computational time. The MSMO is used for on-chip training. The circuit of the largest absolute value decision is designed to avoid the unclassifiable problem in the OAO multiclass classification. The designed system is implemented on a field-programmable gate array (FPGA) platform and evaluated using the publicly available epilepsy dataset. The experimental results demonstrate that the designed system achieves high accuracy with low-dimensional feature vectors.

References

    1. 1)
      • 23. Joachims, T.: ‘Making large-scale support vector machine learning practical’, in Scholkopf, B., Burges, C.J.C., Smola, A.J. (Eds.): ‘Advances in kernel methods’ (MIT Press, Cambridge, MA, 1998), pp. 169184.
    2. 2)
      • 39. Adeli, H., Zhou, Z., Dadmehr, N.: ‘Analysis of EEG records in an epileptic patient using wavelet transform’, J. Neurosci. Methods, 2003, 123, pp. 6987.
    3. 3)
      • 25. Platt, J. C.: ‘Fast training of support vector machines using sequential minimal optimization’, in Scholkopf, B., Burges, C. J. C., Smola, A. J. (Eds.): ‘Advances in kernel methods – support vector learning’ (MIT Press, Cambridge, MA, USA, 1998), pp. 185208.
    4. 4)
      • 40. Hsia, C.H., Chiang, J.S., Guo, J.M.: ‘Memory-efficient hardware architecture of 2-D dual-mode lifting-based discrete wavelet transform’, IEEE Trans. Circuits Syst. Video Technol., 2013, 23, pp. 671683.
    5. 5)
      • 53. Güler, İ, Übeyli, E.D.: ‘Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients’, J. Neurosci. Methods, 2005, 12, pp. 918.
    6. 6)
      • 37. Islam, M.K., Rastegarnia, A., Yang, Z.: ‘A wavelet-based artifact reduction from scalp EEG for epileptic seizure detection’, IEEE J. Biomed. Health Inf., 2016, 20, (5), pp. 13211332.
    7. 7)
      • 47. Kayhan, S., Ercelebi, E.: ‘ECG denoising on bivariate shrinkage function exploiting interscale dependency of wavelet coefficients’, Turk. J. Elec. Eng. Comp. Sci., 2011, 19, (3), pp. 495511.
    8. 8)
      • 29. Mathur, A., Foody, G.M.: ‘Multiclass and binary SVM classification: implications for training and classification users’, IEEE Geosci. Remote Sens. Lett., 2008, 5, (2), pp. 241245.
    9. 9)
      • 32. Kolekar, M.H., Dash, D.P.: ‘A nonlinear feature based epileptic seizure detection using least square support vector machine classifier’. TENCON 2015 IEEE Region 10 Conf., Macao, China, November 2015, pp. 16.
    10. 10)
      • 12. Sharma, M., Pachori, R.B., Acharya, U.R.: ‘A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension’, Pattern Recognit. Lett., 2017, 94, pp. 172179.
    11. 11)
      • 3. Altaf, M., Yoo, J.: ‘A 1.83 J/classification, 8-channel, patient-specific epileptic seizure classification SoC using a non-linear support vector machine’, IEEE Trans. Biomed. Circuits Syst., 2016, 10, (1), pp. 4960.
    12. 12)
      • 11. Tiwari, A.K., Pachori, R.B., Kanhangad, V., et al: ‘Automated diagnosis of epilepsy using key-point based local binary pattern of EEG signals’, IEEE J. Biomed. Health Inf., 2017, 21, (4), pp. 888896.
    13. 13)
      • 43. Song, J., Park, I.-C.: ‘Pipelined discrete wavelet transform architecture scanning dual lines’, IEEE Trans. Circuits Syst. II, Exp. Briefs, 2009, 56, (12), pp. 916920.
    14. 14)
      • 28. Guler, I., Ubeyli, E.D.: ‘Multiclass support vector machines for EEG-signals classification’, IEEE Trans. Inf. Technol. Biomed., 2007, 11, (2), pp. 117126.
    15. 15)
      • 51. Riaz, F., Hassan, A., Rehman, S., et al: ‘EMD-based temporal and spectral features for the classification of EEG signals using supervised learning’, IEEE Trans. Neural Syst. Rehabil. Eng., 2016, 24, (1), pp. 2835.
    16. 16)
      • 21. Iacoviello, D., Petracca, A., Spezialetti, M., et al: ‘A classification algorithm for electroencephalography signals by self-induced emotional stimuli’, IEEE Trans. Cybern., 2016, 46, (12), pp. 317131803.
    17. 17)
      • 36. Pachori, R.B.: ‘Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition’, Res. Lett. Signal Process., 2008, 14.
    18. 18)
      • 18. Siuly, S., Li, Y.: ‘Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification’, Comput. Methods Programs Biomed., 2015, 119, pp. 2942.
    19. 19)
      • 49. Andrzejak, R.G., Lehnertz, K., Mormann, F., et al: ‘Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state’, Phys. Rev. E, 2001, 64, p. 061907.
    20. 20)
      • 48. Altaf, M., Tillak, J., Kifle, Y., et al: ‘A 1.83uJ/classification nonlinear support-vector machine-based patient-specific seizure classification SoC’. IEEE Int. Solid-State Circuits Conf. (ISSCC) Digest of Technical Papers, 2013, pp. 100101.
    21. 21)
      • 58. Meher, P.K., Park, S.Y.: ‘CORDIC designs for fixed angle of rotation’, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., 2013, 21, (2), pp. 217228.
    22. 22)
      • 10. Bhattacharyya, A., Pachori, R.B., Upadhyay, A., et al: ‘Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals’, Appl. Sci., 2017, 7, (385), pp. 118.
    23. 23)
      • 14. Supriya, S., Siuly, S., Zhang, Y.: ‘Automatic epilepsy detection from EEG introducing a new edge weight method in the complex network’, Electron. Lett., 2016, 52, (17), pp. 14301432.
    24. 24)
      • 16. Tang, Y., Durand, D.M.: ‘A tunable support vector machine assembly classifier for epileptic seizure detection’, Expert Syst. Appl., 2012, 39, pp. 39253938.
    25. 25)
      • 7. Shafiul Alam, S.M., Bhuiyan, M.I.H.: ‘Detection of seizure and epilepsy using higher order statistics in the EMD domain’, IEEE J. Biomed. Health Inf., 2013, 17, (2), pp. 312318.
    26. 26)
      • 54. Siuly, S., Li, Y.: ‘Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface’, IEEE Trans. Neural Syst. Rehabil. Eng., 2012, 20, (4), pp. 526538.
    27. 27)
      • 26. Cao, L.J., Keerthi, S.S., Ong, C.J., et al: ‘Parallel sequential minimal optimization for the training of support vector machines’, IEEE Trans. Neural Netw., 2007, 17, (4), pp. 10391049.
    28. 28)
      • 41. José, C., Thomas, L.: ‘Hardware implementation of 1D wavelet transform on an FPGA for infrasound signal classification’, IEEE Trans. Nucl. Sci., 2008, 55, (1), pp. 913.
    29. 29)
      • 31. Lee, F., Scherer, R., Leeb, R., et al: ‘A comparative analysis of multi-class EEG classification for brain computer interface’. Proc. 10th Computer Vision Winter Workshop (CVWW), Graz, Austria, January 2005, pp. 110.
    30. 30)
      • 4. Bhati, D., Pachori, R.B., Gadre, V.M.: ‘A novel approach for time-frequency localization of scaling functions and design of three-band biorthogonal linear phase wavelet filter banks’, Digit. Signal Process., 2017, 69, pp. 309322.
    31. 31)
      • 55. Shen, C.P., Chen, C.C., Hsieh, S.L., et al: ‘High-performance seizure detection system using a wavelet-approximate entropy-fSVM cascade with clinical validation’, Clin. EEG Neurosci., 2013, 44, (4), pp. 247256.
    32. 32)
      • 35. Bajaj, V., Pachori, R.B.: ‘Classification of seizure and nonseizure EEG signals using empirical mode decomposition’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, (6), pp. 11351142.
    33. 33)
      • 22. Boser, B., Guyon, I., Vapnik, V.: ‘A training algorithm for optimal margin classifiers’. Proc. 5th Annual Workshop on Computational Learning Theory, Pittsburgh, 1992, pp. 144152.
    34. 34)
      • 8. Joshi, V., Pachori, R.B., Vijesh, A.: ‘Classification of ictal and seizure-free EEG signals using fractional linear prediction’, Biomed. Signal Proc. Control, 2014, 9, pp. 15.
    35. 35)
      • 1. Yoo, J., Yan, L., El-Damak, D., et al: ‘An 8-channel scalable EEG acquisition SoC with patient-specific seizure classification and recording processor’, IEEE J. Solid-State Circuits, 2013, 48, (1), pp. 214228.
    36. 36)
      • 44. Yonga, G.C., Maan, N., Ahmad, T.: ‘EEG signal of epiliptic patient by fast Fourier and wavelet transforms’, Jurnal Teknologi (Sci. Eng.), 2013, 61, (1), pp. 1320.
    37. 37)
      • 9. Siuly Li, Y., Wen, P.: ‘Clustering technique-based least square support vector machine for EEG signal classification’, Comput. Methods Programs Biomed., 2011, 104, pp. 358372.
    38. 38)
      • 2. Chen, W.-M., Chiueh, H., Chen, T.-J., et al: ‘A fully integrated 8-channel closed-loop neural-prosthetic CMOS SoC for real-time epileptic seizure control’, IEEE J. Solid-State Circuits, 2014, 49, (1), pp. 232247.
    39. 39)
      • 45. Acharya, U.R., Sree, S.V., Swapna, G., et al: ‘Automated EEG analysis of epilepsy: a review’, Knowl.-Based Syst., 2013, 45, pp. 147165.
    40. 40)
      • 17. Liu, Y., Zhou, W., Yuan, Q., et al: ‘Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG’, IEEE Trans. Neural Syst. Rehabil. Eng., 2012, 20, pp. 749755.
    41. 41)
      • 33. Sharma, R., Pachori, R.B.: ‘Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions’, Expert Syst. Appl., 2015, 42, (3), pp. 11061117.
    42. 42)
      • 6. Ghosh-Dastidar, S., Adeli, H., Dadmehr, N.: ‘Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection’, IEEE Trans. Biomed. Eng., 2008, 55, (2), pp. 512518.
    43. 43)
      • 50. Liang, S.F., Wang, H.C., Chang, W.L.: ‘Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection’, EURASIP J. Adv. Signal Process., 2010, 2010, pp. 853434-1853434-15.
    44. 44)
      • 38. 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, 2007, pp. 80510-180510-13.
    45. 45)
      • 19. Kalyani, S., Shanti Swarup, K.: ‘Classification and assessment of power system security using multiclass SVM’, IEEE Trans. Syst., Man Cybern., 2010, 41, (5), pp. 753758.
    46. 46)
      • 13. Kumar, T.S., Kanhangad, V., Pachori, R.B.: ‘Classification of seizure and seizure-free EEG signals using local binary patterns’, Biomed. Signal Process. Control, 2015, 15, pp. 3340.
    47. 47)
      • 20. Alzoubi, O., Koprinska, I., Calvo, R.A.: ‘Classification of brain computer interface data’, Proc. 7th Australasian Data Mining Conf., Glenelg, Australia, November, 2008, pp. 123131.
    48. 48)
      • 30. Fei, B., Liu, J.: ‘Binary tree of SVM: a new fast multiclass training and classification algorithm IEEE trans’, Neural Netw., 2006, 17, (3), pp. 696704.
    49. 49)
      • 46. Srinivasan, V., Eswaran, C., Sriraam, N.: ‘Approximate entropy-based epileptic EEG detection using artificial neural networks’, IEEE Trans. Inf. Technol. Biomed., 2007, 11, (3), pp. 288295.
    50. 50)
      • 27. Hsu, C.W., Lin, C.J.: ‘A comparison of methods for multiclass support vector machines’, IEEE Trans. Neural Netw., 2002, 13, (2), pp. 415425.
    51. 51)
      • 57. Kuan, T.-W., Wang, J.-F., Wang, J.-C., et al: ‘VLSI design of an SVM learning core on sequential minimal optimization algorithm’, IEEE Trans. Very Large Scale Integr. (VLSI) Syst., 2012, 20, (4), pp. 673683.
    52. 52)
      • 56. Lee, K., Verma, N.: ‘A low-power processor with configurable embedded machine-learning accelerators for high-order and adaptive analysis of medical-sensor signals’, IEEE J. Solid-State Circuits, 2013, 48, (7), pp. 16251636.
    53. 53)
      • 5. Bhati, D., Sharma, M., Pachori, R.B., et al: ‘Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification’, Digit. Signal Process., 2017, 62, pp. 259273.
    54. 54)
      • 15. Muthanantha Murugavel, A.S., Ramakrishnan, S.: ‘Hierarchical multi class SVM with ELM kernel for epileptic EEG signal classification’, Med. Biol. Eng. Comput., 2016, 54, pp. 149161.
    55. 55)
      • 52. Gülera, N.F., Übeylib, E.D., Güler, İ: ‘Recurrent neural networks employing Lyapunov exponents for EEG signals classification’, Expert Syst. Appl., 2005, 29, pp. 506514.
    56. 56)
      • 24. DeCoste, D., Schölkopf, B.: ‘Training invariant support vector machines’, Mach. Learn., 2002, 46, (1), pp. 161190.
    57. 57)
      • 42. Martina, M., Masera, G.: ‘Low-complexity, efficient 9/7 wavelet filters VLSI implementation’, IEEE Trans. Circuits Syst. II, Exp. Briefs, 2006, 53, (11), pp. 12891293.
    58. 58)
      • 34. Pachori, R.B., Patidar, S.: ‘Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions’, Comput. Methods Programs Biomed., 2014, 113, (2), pp. 494502.
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