http://iet.metastore.ingenta.com
1887

Sleep stages classification from EEG signal based on Stockwell transform

Sleep stages classification from EEG signal based on Stockwell transform

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Sleep has great effect on physical health and quality of life. Electroencephalogram (EEG) signal is used in studying sleep process and recently, time–frequency transforms are increasingly utilised in EEG signal analysis. This study proposes an efficient method for sleep stages classification based on a time–frequency transform, namely Stockwell transform. In the introduced method, at first, the Stockwell transform is used to map each 30 s epoch of EEG signal into the time–frequency domains, which results in a complex-valued matrix. Then, the frequency domain is divided into different non-overlapping segments, leading to several matrices. After that, entropy features are extracted from the obtained matrices. In order to determine the sleep stage of each epoch, the computed features are applied to classifier. Support vector machine, weighted K-nearest neighbour, and ensemble bagged tree classifiers are considered. The Pz–Oz and Fpz–Cz channels of EEG signal from Sleep-EDF data set and C3–A2 channel from ISRUC-Sleep data set are used in this research. The results indicate that the proposed method outperforms the recently introduced methods.

References

    1. 1)
      • 1. Chen, X., Peng, H., Yu, F., et al: ‘Independent vector analysis applied to remove muscle artifacts in EEG data’, IEEE Trans. Instrum. Meas., 2017, 66, (11), pp. 17701779.
    2. 2)
      • 2. Young, C.-P., Liang, S.-F., Chang, D.-W., et al: ‘A portable wireless online closed-loop seizure controller in freely moving rats’, IEEE Trans. Instrum. Meas., 2011, 60, (2), pp. 513521.
    3. 3)
      • 3. Liang, S.-F., Kuo, C.-E., Hu, Y.-H., et al: ‘Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models’, IEEE Trans. Instrum. Meas., 2012, 61, (6), pp. 16491657.
    4. 4)
      • 4. Liang, S.-F., Kuo, C.-E., Lee, Y.-C., et al: ‘Development of an EOG-based automatic sleep-monitoring eye mask’, IEEE Trans. Instrum. Meas., 2015, 64, (11), pp. 29772985.
    5. 5)
      • 5. Li, Y.: ‘Multichannel EEG signal classification – a geometric approach’, 2010.
    6. 6)
      • 6. Adikarapatti, V.K.: ‘Optimal EEG channels and rhythm selection for task classification’, 2007.
    7. 7)
      • 7. Homan, R.W., Herman, J., Purdy, P.: ‘Cerebral location of international 10–20 system electrode placement’, Electroencephalogr. Clin. Neurophysiol., 1987, 66, (4), pp. 376382.
    8. 8)
      • 8. Kelly, J.M., Strecker, R.E., Bianchi, M.T.: ‘Recent developments in home sleep-monitoring devices’, ISRN Neurol., 2012, 2012, pp. 110.
    9. 9)
      • 9. Rechtschaffen, A., Kales, A.: ‘A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects’, 1968.
    10. 10)
      • 10. A. A. o. S. Medicine: ‘The international classification of sleep disorders: diagnostic and coding manual’. American Academy of Sleep Medicine, 2005.
    11. 11)
      • 11. Zhu, G., Li, Y., Wen, P.P.: ‘Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal’, IEEE. J. Biomed. Health. Inform., 2014, 18, (6), pp. 18131821.
    12. 12)
      • 12. Kellaway, P.: ‘Sleep and epilepsy’, Epilepsia, 1985, 26, pp. S15S30.
    13. 13)
      • 13. Baraniuk, R.G.: ‘Warped perspectives in time-frequency analysis’. Proc. IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, Philadelphia, 1994, pp. 528531.
    14. 14)
      • 14. Feltane, A.: ‘Time-frequency based methods for non-stationary signal analysis with application to EEG signals’, Open Access Dissertations, 2016, Paper 445. Available at: https://digitalcommons.uri.edu/oa_diss/445.
    15. 15)
      • 15. Miwakeichi, F., Martınez-Montes, E., Valdés-Sosa, P.A., et al: ‘Decomposing EEG data into space–time–frequency components using parallel factor analysis’, NeuroImage, 2004, 22, (3), pp. 10351045.
    16. 16)
      • 16. Roach, B.J., Mathalon, D.H.: ‘Event-related EEG time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia’, Schizophr. Bull., 2008, 34, (5), pp. 907926.
    17. 17)
      • 17. Supratak, A., Dong, H., Wu, C., et al: ‘Deepsleepnet: a model for automatic sleep stage scoring based on raw single-channel EEG’, arXiv preprint arXiv:1703.04046, 2017.
    18. 18)
      • 18. Zhang, J., Wu, Y.: ‘A new method for automatic sleep stage classification’, IEEE Trans. Biomed. Circuits Syst., 2017, 11, (5), pp. 10971110.
    19. 19)
      • 19. Kang, D.Y., DeYoung, P.N., Malhotra, A., et al: ‘A state space and density estimation framework for sleep staging in obstructive sleep apnea’, IEEE Trans. Biomed. Eng., 2017, 65, (6), pp. 12011212.
    20. 20)
      • 20. Hassan, A.R., Bhuiyan, M.I.H.: ‘Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating’, Biomed. Signal Proc. Control, 2016, 24, pp. 110.
    21. 21)
      • 21. Hassan, A. R., Bhuiyan, M.I.H.: ‘Automatic sleep scoring using statistical features in the EMD domain and ensemble methods’, Biocybernetics Biomed. Eng., 2016, 36, (1), pp. 248255.
    22. 22)
      • 22. Hassan, A.R., Bhuiyan, M.I.H.: ‘Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting’, Comput. Methods Programs Biomed., 2017, 140, pp. 201210.
    23. 23)
      • 23. Güneş, S., Polat, K., Yosunkaya, Ş.: ‘Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting’, Expert Syst. Appl., 2010, 37, (12), pp. 79227928.
    24. 24)
      • 24. Aboalayon, K.A., Ocbagabir, H.T., Faezipour, M.: ‘Efficient sleep stage classification based on EEG signals’. Proc. of IEEE Conference on Systems, Applications and Technology, Farmingdale, 2014, pp. 16.
    25. 25)
      • 25. Wu, H.-T., Talmon, R., Lo, Y.-L.: ‘Assess sleep stage by modern signal processing techniques’, IEEE Trans. Biomed. Eng., 2015, 62, (4), pp. 11591168.
    26. 26)
      • 26. Peker, M.: ‘A new approach for automatic sleep scoring: combining Taguchi based complex-valued neural network and complex wavelet transform’, Comput. Methods Programs Biomed., 2016, 129, pp. 203216.
    27. 27)
      • 27. Hassan, A.R., Bhuiyan, M.I.H.: ‘A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features’, J. Neurosci. Methods, 2016, 271, pp. 107118.
    28. 28)
      • 28. Hassan, A.R., Bhuiyan, M.I.H.: ‘An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting’, Neurocomputing, 2017, 219, pp. 7687.
    29. 29)
      • 29. Hassan, A.R., Subasi, A.: ‘A decision support system for automated identification of sleep stages from single-channel EEG signals’, Knowl.-Based Syst., 2017, 128, pp. 115124.
    30. 30)
      • 30. Bajaj, V., Pachori, R.B.: ‘Automatic classification of sleep stages based on the time-frequency image of EEG signals’, Comput. Methods Programs Biomed., 2013, 112, (3), pp. 320328.
    31. 31)
      • 31. Stockwell, R.G.: ‘A basis for efficient representation of the S-transform’, Digit. Signal Process., 2007, 17, (1), pp. 371393.
    32. 32)
      • 32. Kalbkhani, H., Shayesteh, M.G.: ‘Stockwell transform for epileptic seizure detection from EEG signals’, Biomed. Signal Proc. Control, 2017, 38, pp. 108118.
    33. 33)
      • 33. Kemp, B.: ‘The sleep-edf database online’. Available from http://www.physionet.org/physiobank/database/sleep-edf, 2013.
    34. 34)
      • 34. Khalighi, S., Sousa, T., Santos, J. M., et al: ‘ISRUC-Sleep: a comprehensive public dataset for sleep researchers’, Comput. Methods Programs Biomed., 2016, 124, pp. 180192.
    35. 35)
      • 35. Rai, K., Bajaj, V., Kumar, A.: ‘Hilbert–Huang transform based classification of sleep and wake EEG signals using fuzzy c-means algorithm’. Proc. of IEEE Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, 2015, pp. 460464.
    36. 36)
      • 36. Stockwell, R.G., Mansinha, L., Lowe, R.: ‘Localization of the complex spectrum: the S transform’, IEEE Trans. Signal Process., 1996, 44, (4), pp. 9981001.
    37. 37)
      • 37. Xia, Y., Zhou, W., Li, C., et al: ‘Seizure detection approach using S-transform and singular value decomposition’, Epilepsy. Behav., 2015, 52, pp. 187193.
    38. 38)
      • 38. Lokhande, A.A.: ‘A survey on S-transform’. National Conf. on Emerging Trends in Engineering & Technology, Chennai, 2017.
    39. 39)
      • 39. Rutkowski, G., Patan, K., Leśniak, P.: ‘Computer-aided on-line seizure detection using Stockwell transform’, in Korbicz, J., Kowal, M. (Eds.): ‘Intelligent systems in technical and medical diagnostics’ (Springer, Heidelberg2014), pp. 279289.
    40. 40)
      • 40. Brankačk, J., Kukushka, V.I., Vyssotski, A.L., et al: ‘EEG gamma frequency and sleep–wake scoring in mice: comparing two types of supervised classifiers’, Brain Res., 2010, 1322, pp. 5971.
    41. 41)
      • 41. Haenschel, C., Baldeweg, T., Croft, R.J., et al: ‘Gamma and beta frequency oscillations in response to novel auditory stimuli: a comparison of human electroencephalogram (EEG) data with in vitro models’, Proc. Natl Acad. Sci. USA, 2000, 97, (13), pp. 76457650.
    42. 42)
      • 42. Porjesz, B., Almasy, L., Edenberg, H.J., et al: ‘Linkage disequilibrium between the beta frequency of the human EEG and a GABAA receptor gene locus’, Proc. Natl Acad. Sci. USA, 2002, 99, (6), pp. 37293733.
    43. 43)
      • 43. Rangaswamy, M., Porjesz, B., Chorlian, D.B., et al: ‘Beta power in the EEG of alcoholics’, Biol. Psychiatry, 2002, 52, (8), pp. 831842.
    44. 44)
      • 44. Soroko, S., Shemyakina, N., Nagornova, Z.V., et al: ‘Longitudinal study of EEG frequency maturation and power changes in children on the Russian north’, Int. J. Dev. Neurosci., 2014, 38, pp. 127137.
    45. 45)
      • 45. Lay-Ekuakille, A., Vergallo, P., Griffo, G., et al: ‘Entropy index in quantitative EEG measurement for diagnosis accuracy’, IEEE Trans. Instrum. Meas., 2014, 63, (6), pp. 14401450.
    46. 46)
      • 46. Skilling, J., Bryan, R.: ‘Maximum entropy image reconstruction: general algorithm’, Mon. Not. R. Astron. Soc., 1984, 211, (1), pp. 111124.
    47. 47)
      • 47. Li, T., Zhou, M.: ‘ECG classification using wavelet packet entropy and random forests’, Entropy, 2016, 18, (8), p. 285.
    48. 48)
      • 48. Zhang, J.-S., Chen, C.-J.: ‘Local variance projection log energy entropy features for illumination robust face recognition’. Proc. IEEE International Symposium on Biometrics and Security Technologies, Islamabad, 2008, pp. 15.
    49. 49)
      • 49. Wang, D., Miao, D., Xie, C.: ‘Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection’, Expert Syst. Appl., 2011, 38, (11), pp. 1431414320.
    50. 50)
      • 50. Kecman, V.: ‘Learning and soft computing: support vector machines, neural networks, and fuzzy logic models’ (MIT Press, USA, 2001).
    51. 51)
      • 51. Fehrmann, E.: ‘Automated sleep classification using the new sleep stage standards’, 2013.
    52. 52)
      • 52. Vapnik, V.: ‘The nature of statistical learning theory’ (Springer Science & Business Media, USA, 2013).
    53. 53)
      • 53. Yigit, H.: ‘A weighting approach for KNN classifier’. Proc. of IEEE International Conference on Electronics, Computer and Computation, Ankara, 2013, pp. 228231.
    54. 54)
      • 54. Dietterich, T.G.: ‘Ensemble methods in machine learning’, pp. 115.
    55. 55)
      • 55. Kalbkhani, H., Shayesteh, M.G., Zali-Vargahan, B.: ‘Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series’, Biomed. Signal Proc. Control, 2013, 8, (6), pp. 909919.
    56. 56)
      • 56. Diykh, M., Li, Y., Wen, P.: ‘EEG sleep stages classification based on time domain features and structural graph similarity’, IEEE Trans. Neural Syst. Rehabil. Eng., 2016, 24, (11), pp. 11591168.
    57. 57)
      • 57. Hsu, Y.-L., Yang, Y.-T., Wang, J.-S., et al: ‘Automatic sleep stage recurrent neural classifier using energy features of EEG signals’, Neurocomputing, 2013, 104, pp. 105114.
    58. 58)
      • 58. Doroshenkov, L., Konyshev, V., Selishchev, S.: ‘Classification of human sleep stages based on EEG processing using hidden Markov models’, Biomed. Eng., 2007, 41, (1), pp. 2528.
    59. 59)
      • 59. Najdi, S., Gharbali, A.A., Fonseca, J.M.: ‘Feature transformation based on stacked sparse autoencoders for sleep stage classification’. Springer Doctoral Conference on Computing, Electrical and Industrial Systems, Costa de Caparica, 2017, pp. 191200.
    60. 60)
      • 60. Bhandari, A., Marziliano, P., Barrutia, A.M.: ‘Need for speed: fast Stockwell transform (FST) with O (N) complexity’. pp. 15.
    61. 61)
      • 61. Claesen, M., De Smet, F., Suykens, J.A., et al: ‘Fast prediction with SVM models containing RBF kernels’. Proc. IEEE International Conference on Information, Communications and Signal Processing, Macau, 2009, pp. 15.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2018.5032
Loading

Related content

content/journals/10.1049/iet-spr.2018.5032
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address