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Artificial Apnea Classification with Quantitative Sleep EEG Synchronization

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Abstract

In the present study, both linear and nonlinear EEG synchronization methods so called Coherence Function (CF) and Mutual Information (MI) are performed to obtain high quality signal features in discriminating the Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA) from controls. For this purpose, sleep EEG series recorded from patients and healthy volunteers are classified by using several Feed Forward Neural Network (FFNN) architectures with respect to synchronic activities between C3 and C4 recordings. Among the sleep stages, stage2 is considered in tests. The NN approaches are trained with several numbers of neurons and hidden layers. The results show that the degree of central EEG synchronization during night sleep is closely related to sleep disorders like CSA and OSA. The MI and CF give us cooperatively meaningful information to support clinical findings. Those three groups determined with an expert physician can be classified by addressing two hidden layers with very low absolute error where the average area of CF curves ranged form 0 to 10 Hz and the average MI values are assigned as two features. In a future work, these two features can be combined to create an integrated single feature for error free apnea classification.

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References

  1. Fogel, R. B., Malhotra, A., Dalagiorgou, G., Robinson, M. K., et al., Anatomic and physiologic predictors of apnea severity in morbidly obese subjects. Sleep 26:150–155, 2003.

    Google Scholar 

  2. Várady, P., Micsik, T., Benedek, S., Benyó, Z., A Novel method for the detection of apnea and hypopnea events in respiration signals. IEEE Trans. BME 49(9):936–942, 2002.

    Article  Google Scholar 

  3. McNames, J. N., Fraser, A. M., Obstructive sleep apnea classification based on spectrogram patterns in the electrocardiogram. Comput. Cardiol. 27:749–752, 2000.

    Google Scholar 

  4. Shinar, Z., Baharav, A., Obstructive sleep apnea detection based on electrocardiogram analysis. Comput. Cardiol. 27:757–760, 2000.

    Google Scholar 

  5. Suhas, S. R., Behbehani, K., Vijendra, S., et al., Time domain analysis of R-wave attenuation envelope for sleep apnea detection. Eng. Med. Biol. Soc. 2:3885–3888, 2004.

    Google Scholar 

  6. Álvarez, D., Hornero, R., et al., Improving diagnostic ability of blood oxygen saturation from overnight pulse oximetry in obstructive sleep apnea detection by means of central tendency measure. Artif. Intell. Med. 41(1):13–24, 2007.

    Article  Google Scholar 

  7. Romero, O. F., Berdin, G., Betanzos, A. A., Bonillo, V. M., A new method for sleep apnea classification using wavelets and feed forward neural networks. Artif. Intell. Med. 34:65–76, 2005.

    Article  Google Scholar 

  8. Tagluk, M. E., Akin, M., Sezgin, N., Classification of sleep apnea by using wavelet transform and artificial neural networks. Expert Syst. Appl. 37(2):1600–1607, 2010.

    Article  Google Scholar 

  9. Tagluk, M. E., Sezgin, N., Classification of sleep apnea through sub-band energy of abdominal effort signal using wavelets + neural networks. J. Med. Syst., 2009, doi:10.1007/s10916-009-9330-5.

    Google Scholar 

  10. Bronzino, J. D., The biomedical engineering handbook. 2nd ed. CRC: Boca Raton, pp. 15, 2000.

    Google Scholar 

  11. Aydın, S., Comparison of power spectrum predictors in computing coherence functions for intracortical EEG signals. Ann. Biomed. Eng. 37(1):192–200, 2009.

    Article  Google Scholar 

  12. Koenig, T., Prichep, L., et al., Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment. Neurobiol. Aging 26:165–171, 2005.

    Article  Google Scholar 

  13. Aydın, S., Computer based synchronization analysis on sleep EEG in insomnia. J. Med. Syst., 2009. doi:10.1007/s10916-009-9387-1, (published online on October 21, 2009).

    Google Scholar 

  14. Duman, F., Erdamar, A., Eroǧul, O., Telatar, Z., Yetkin, S., Efficient sleep spindle detection algorithm with decision tree. Expert Syst. Appl. 36(6):9980–9985, 2009.

    Article  Google Scholar 

  15. Culebras, A., Clinical handbook of sleep disorders. Butterworth-Heinemann: Boston, 1996.

    Google Scholar 

  16. Susmakova, K., Human sleep and sleep EEG. Meas. Sci. Rev. 4(2):59, 2004.

    Google Scholar 

  17. Boccaletti, S. et al., The synchronization of chaotic systems. Phys. Rep. 366:1–101, 2002.

    Article  MathSciNet  MATH  Google Scholar 

  18. Pereda, E., Quiroga, R. Q., Nonlinear multivariate analysis of neurophysiologic signals. Prog. Neurobiol. 77:1–37, 2005.

    Article  Google Scholar 

  19. Koenig, T., Lehmann, D., et al., Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-native patients with schizophrenia: Preliminary results. Schizophr. Res. 50:55–60, 2001.

    Article  Google Scholar 

  20. Ferria, R., Rundo, F., Bruni, O., Dynamics of the EEG slow-wave synchronization during sleep. Clin. Neurophysiol. 116:2783–2795, 2005.

    Article  Google Scholar 

  21. Pizzagalli, D., Lehmann, D., Gianotti, L., et al., Brain electric correlates of strong belief in paranormal phenomena: Intracerebral EEG source and regional Omega complexity analyses. Neuroimaging 100:139–154, 2000.

    Google Scholar 

  22. Rappelsberger, P., Petsche, H., Probability mapping: Power and coherence analysis of cognitive processes. Brain Topogr. 1:46–54, 1988.

    Article  Google Scholar 

  23. Proakis, J. G., Manolakis, D. G., Digital signal processing. 3rd ed., sec. 12. Prentice Hall: Upper Saddle River, pp. 925–956, 1996.

    Google Scholar 

  24. Aydın, S., Determination of autoregressive model orders for seizure detection. Turk. J. Elec. Eng. Comp. Sci. 18(1):1–22, 2010. doi:10.3906/elk-0906-83.

    Google Scholar 

  25. Neumaier, A., Shneider, T., Estimation of parameters and Eigen modes of multivariate autoregressive models. ACM Trans. Math. Soft. 27(1):27–57, 2001.

    Article  MATH  Google Scholar 

  26. Wang, Q., Shen, Y., Zhang, J. Q., A nonlinear correlation measure for multivariate data set. Physica D 200:287–295, 2005.

    Article  MathSciNet  MATH  Google Scholar 

  27. Gray, R., Entropy and information theory. Springer: New York, 1990.

    MATH  Google Scholar 

  28. Hagan, M. T., Demuth, H. B., Beale, M. H., Neural network design. PWS: Boston, 1996.

    Google Scholar 

  29. Ali, A. N. (ed.), Advanced biosignal processing. 8, C, Springer: Berlin, 2009. doi:10.1007/978-3-540-89506-0.

    Google Scholar 

  30. Aydın, S., Saraoǧlu, H. M., Kara, S., Singular spectrum analysis of insomnia. J. Med. Syst., 2009. doi:10.1007/s10916-009-9381-7.

    Google Scholar 

  31. Richard, P. B., Fast training algorithms for multi layer neural nets. IEEE Trans Neural Netw. 2:346–354, 1991.

    Article  Google Scholar 

  32. Gulbag, A., Temurtas, F., A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro fuzzy inference systems. Sens. Actuators 115:252–262, 2006.

    Article  Google Scholar 

  33. Temurtas, F., Tasaltın, C., Temurtas, H., et al., Fuzzy logic and neural network applications on the gas sensor data: Concentration estimation. Lect. Notes Comput. Sci., pp. 179–186, 2003.

  34. Saraoǧlu, H. M., Edin, B., E-Nose system for anesthetic dose level detection using artificial neural network. J. Med. Syst. 6:475–482, 2007.

    Article  Google Scholar 

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Authors and Affiliations

  1. Biomedical Engineering Department, Baṣkent University, Ankara, Turkey

    Mehmet Akṣahin

  2. Electrical and Electronics Engineering Department, Engineering Faculty, Ondokuz Mayıs University, Samsun, Turkey

    Serap Aydın

  3. Dıṣkapı Yıldırım Beyazıt Instructional, and Exploratory Hospital, Sleep Laboratory, Ankara, Turkey

    Hikmet Fırat

  4. Biomedical and Clinical Engineering Center, Gülhane Military Medical Academia, Ankara, Turkey

    Osman Eroǧul

Authors
  1. Mehmet Akṣahin
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  2. Serap Aydın
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  3. Hikmet Fırat
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  4. Osman Eroǧul
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Correspondence to Serap Aydın.

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Akṣahin, M., Aydın, S., Fırat, H. et al. Artificial Apnea Classification with Quantitative Sleep EEG Synchronization. J Med Syst 36, 139–144 (2012). https://doi.org/10.1007/s10916-010-9453-8

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  • DOI: https://doi.org/10.1007/s10916-010-9453-8

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