Sleep disorder detection and identification

Dennis E. B. Tan, Wai Yie Leong

Universiti Tunku Abdul Rahman (UTAR), Kuala Lumpur, Malaysia.

DOI: 10.4236/jbise.2012.56043                

Abstract

Electroencephalogram (EEG) is one of the medical devices that used for sleep disorder detection. Sleep disorder such as Obstructive Sleep Apnea Syndrome (OSAS) often appears during sleep event. Since the OSAS patients have the difficulties to allow the airflow into the lung while inspiration, the EEG is applied to capture and record the brainwave of the patient. In this work, the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) are used to process and analyze the accuracy and efficiency of the results. Both of these methods will decompose the EEG signal into a collection of Intrinsic Mode Function (IMF). In this paper, index orthogonality has been calculated to indicate the completeness of the decomposed signal with the original signal. The instantaneous frequency and Hilbert Spectrum based on both methods also employed by IMF to analyze and present the results in frequency-time distribution to determine the characteristic of the inherent properties of signal. Besides, Hilbert marginal spectrum has been applied to measure the total amplitude contribution from each frequency value. Finally, the results shown that the EEMD is better in solving mode mixing problem and better improvement over EMD method.

Keywords

Empirical Mode Decomposition (EMD); Intrinsic Mode Function (IMF); Ensemble Empirical Mode Decomposition (EEMD); Hilbert Spectrum

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Lee, E.-K. and Jerri, R.H. (2001) Sleep Disorder. Sacramento, Greenville.
[2]	Yi, L., Fan, Y.-L., Li, G. and Tong, Q.-Y. (2009) Sleep stage classification based on eeg hilbert-huang transform. 4th IEEE Conference on Industrial Electronics and Applications, Xi’an, 25-27 May 2009, 3676-3681.
[3]	Mandic, D.P., Souretis, G., Leong, W.-Y., Looney, D., Van Hulle, M.M. and Tanaka, T. (2008) Complex empirical mode decomposition for multichannel information fusion. In: Mandic, D., Golz, M., Kuh, A., Obradovic, D. and Tanaka, T., Eds., Signal Processing Techniques for Knowledge Extraction and Information Fusion, Springer, New York, 243-249. doi:10.1007/978-0-387-74367-7_13
[4]	Bao, Y.-K., Zhang, R., Xiong, T. and Hu, Z.-Y. (2001) Forecasting non-normal demand by support vector machines with ensemble empirical mode decomposition. Advances in Information Sciences and Service Sciences, 3, 81-91.
[5]	Zhu, X.-J., Lv, S.-Q., Fan, L.-J. and Yu, X.-L. (2011) The EEG signal process based on EEMD. IEEE International Symposium on Intelligent Processing and Trusted Computing, Hubei, 22-23 October 2011, 222-225. doi:10.1109/IPTC.2011.67
[6]	Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C. and Liu, H.H. (1998) The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceeding Royal Society London, 454, 903-995.
[7]	Wu, Z.-H. and Huang, N.E. (2004) A study of the characteristics of white noise using the empirical mode decomposition. Proceeding Royal Society London, 460, 1597- 1611.
[8]	Demir, B. and Erturk, S. (2008) Empirical mode decomposition preprocess for higher accuracy hyperspectral image classification. IEEE International Geoscience and Remote Sensing Symposium, Boston, 7-11 July 2008, 939-941.
[9]	Wu, Z.-H. and Huang, N.E. (2005) Ensemble empirical mode decomposition: A noise assisted data analysis method. Center for Ocean-Land-Atmosphere Studies, Calverton.
[10]	Huang, N.E., Wu, M.-L., Long, S.R., Shen, S.S.P., Qu, W.-D., Gloersen, P. and Fan, K.L. (2003) A confidence limit for the empirical mode decomposition and hilbert spectral analysis. Proceeding Royal Society London, 459, 2317-2345.
[11]	Huang, T.-L., Ren, W.-X. and Lou, M.-L. (2008) The orthogonal hilbert huang transform and its application in earthquake motion recordings analysis. 14th World Conference on Earthquake Engineering, Beijing, 12-17 October 2008. http://www.iitk.ac.in/nicee/wcee/article/14_02-0059.pdf
[12]	Li, H., Wang, Y.-C. and Ma, Y.-F. (2010) Ensemble em-pirical mode decomposition and hilbert-huang transform applied to bearing fault diagnosis. IEEE 3rd International Congress on Image and Signal Processing, Yantai, 16-18 October 2010, 3413-3417.
[13]	Golz, M., Sommer, D., Chen, M., Mandic, D. and Trust-schel, U. (2007) Feature fusion for the detection of microsleep events. Journal of VLSI Signal Processing, 49, 329-342. doi:10.1007/s11265-007-0083-4
[14]	Rutkowski, T.M., Cichocki, A. and Mandic, D.P. (2008) Information fusion for perceptual feedback: A brain activity sonification approach. In: Mandic, D., Golz, M., Kuh, A., Obradovic, D. and Tanaka, T., Eds., Signal Processing Techniques for Knowledge Extraction and Information Fusion, Springer, New York, 2008, 261-273. doi:10.1007/978-0-387-74367-7_14
[15]	Huang, N.E., Shen, Z. and Long, S.R. (1999) A new view of nonlinear water waves: The hilbert spectrum. Annual Review of Fluid Mechanics, 31, 417-457. doi:10.1146/annurev.fluid.31.1.417
[16]	Huang, N.E., Wu, Z.-H., Long, S.R., Arnold, K.C., Chen, X.-Y. and Blank, K. (2009) On instantaneous frequency. Advances in Adaptive Data Analysis, 1, 177-229. doi:10.1142/S1793536909000096
[17]	Rehman, N. and Mandic, D.P. (2009) Multivariate empirical mode decomposition. Proceeding of the Royal Society, 466, 1291-1302.
[18]	Ur Rehman, N. and Mandic, D.P. (2010) Empirical mode decomposition for trivatiate signals. IEEE Transactions on Signal Processing, 58, 1059-1068. doi:10.1109/TSP.2009.2033730
[19]	Altaf, M.U.B., Gautama, T., Tanaka, T. and Mandic, D.P. (2007) Rotation invariant complex empirical mode decomposition. http://www.commsp.ee.ic.ac.uk/~mandic/research/RI_CEMD_ICASSP_2007.pdf