Time-frequency BSS of biosignals

Seda Senay

Time-frequency BSS of biosignals

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Author(s): Seda Senay¹
- Affiliations: 1: Electrical Engineering Department , New Mexico Institute of Mining and Technology , Socorro, NM 87801 , USA
Source: Volume 5, Issue 6, December 2018, p. 242 – 246
DOI: 10.1049/htl.2018.5029 , Online ISSN 2053-3713

This is an open access article published by the IET under the Creative Commons Attribution-NoDerivs License (http://creativecommons.org/licenses/by-nd/3.0/)

Received 14/02/2018, Accepted 31/08/2018, Revised 24/05/2018, Published 15/11/2018

Time–frequency (TF) representations are very important tools to understand and explain circumstances, where the frequency content of non-stationary signals varies in time. A variety of biosignals such as speech, electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) show some form of non-stationarity. Considering Priestley's evolutionary (time-dependent) spectral theory for analysis of non-stationary signals, the authors defined a TF representation called evolutionary Slepian transform (EST). The evolutionary spectral theory generalises the definition of spectra while avoiding some of the shortcomings of bilinear TF methods. The performance of the EST in the representation of biosignals for the blind source separation (BSS) problem to extract information from a mixture of sources is studied. For example, in the case of EEG recordings, as electrodes are placed along the scalp, what is actually observed from EEG data at each electrode is a mixture of all the active sources. Separation of these sources from a mixture of observations is crucial for the analysis of recordings. In this study, they show that the EST can be used efficiently in the TF-based BSS problem of biosignals.

References

1. 1)
  - 26. Belouchrani, A., Amin, A.: ‘Blind source separation based on time–frequency signal representation’, IEEE Trans. Signal. Process., 1998, 46, pp. 2888–2897 (doi: 10.1109/78.726803).
2. 2)
  - 35. Moghtaderi, A., Takahara, G., Thomson, D.J.: ‘Evolutionary spectrum estimation for uniformly modulated processes with improved frequency resolution’. IEEE Workshop on Statistical Signal Processing, Cardiff, UK, 2009.
3. 3)
  - 17. Ma, L., Blu, T., Wang, W.: ‘An EEG blind source separation algorithm based on a weak exclusion principle’. 38th Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20 August 2016, pp. 859–862.
4. 4)
  - 27. Hajipour, S., Bagher, S., Albera, L., et al: ‘Denoising of ictal EEG data using semi-blind source separation methods based on time–frequency priors’, IEEE J. Biomed. Health Inf., 2015, 19, pp. 839–847 (doi: 10.1109/JBHI.2014.2336797).
5. 5)
  - 23. Zhang, X., Wang, W., Shen, C., et al: ‘Extraction of EEG components based on time–frequency blind source separation’, in Pan, J.S., Tsai, P.W., Watada, J., et al (Eds.): ‘Advances in intelligent information hiding and multimedia signal processing, smart innovation, systems and technologies’ (Springer, Shimane, Japan, 2018), vol. 82.
6. 6)
  - 19. Alvarez, L., Sarmiento, O., Gonzalez, A., et al: ‘Hybrid BSS techniques for fetal ECG extraction using a semi-synthetic database’. 20th IEEE Signal Processing, Images and Computer Vision (STSIVA) Symp., Bogota, Colombia, 2015.
7. 7)
  - 30. Kayhan, A.S., Moeness, G.A.: ‘Spatial evolutionary spectrum for DOA estimation and blind signal estimation’, IEEE Trans. Signal Process., 2000, 48, pp. 791–797 (doi: 10.1109/78.824673).
8. 8)
  - 32. Senay, S.: ‘Nonstationary blind source separation using Slepian spectral estimator’, IARAS Int. J. Signal Process., 2017, 2, pp. 107–114.
9. 9)
  - 37. Walter, G.G., Shen, X.: ‘Sampling with prolate spheroidal functions’, Am. Math. Soc. Abstr., 2003, 6, pp. 41–227.
10. 10)
  - 43. Liebeherr, J., Markus, F., Shahrokh, V.: ‘A min-plus system interpretation of bandwidth estimation’. INFOCOM 26th IEEE Int. Conf. Computer Communications, Anchorage, AL, USA, 2007.
11. 11)
  - 44. Fevotte, C., Doncarli, C.: ‘Two contributions to blind source separation using time–frequency distributions’, IEEE Signal. Process. Lett., 2004, 11, pp. 386–389 (doi: 10.1109/LSP.2003.819343).
12. 12)
  - 7. Priestley, M.B.: ‘Spectral analysis and time series’ (Academic Press, San Diego, CA, 1981).
13. 13)
  - 20. Kam, A., Cohen, A.: ‘Separation of twins fetal ECG by means of blind source separation (BSS)’. 21st IEEE Convention, 0-7803-5842-2, Tel-Aviv, Israel, 2000, pp. 342–345.
14. 14)
  - 14. Johnson, D.H., Dudgeon, D.E.: ‘Array signal processing: concepts and techniques’ (Prentice-Hall, Englewood Cliffs, NJ, 1993).
15. 15)
  - 40. Wang, X., Yong, G.: ‘Stochastic resonance for estimation of a signals bandwidth under low SNR’, Analog Integr. Circuits Signal Process., 2016, 89, (1), pp. 263–269 (doi: 10.1007/s10470-016-0809-y).
16. 16)
  - 20. Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: ‘A blind source separation technique using second-order statistics’, IEEE Trans. Signal Process., 1997, 45, (2), pp. 434–444 (doi: 10.1109/78.554307).
17. 17)
  - 15. Makeig, S., Bell, A.J., Jun, T.P., et al: ‘Independent component analysis of EEG data’, J. Adv. Neural Inf. Process. Syst., 1996, 21, pp. 145–151.
18. 18)
  - 21. Lin, W., Xiaofeng, M.: ‘An adaptive generalized S-transform for instantaneous frequency estimation’, Signal Process., 2011, 91, (8), pp. 1876–1886 (doi: 10.1016/j.sigpro.2011.02.010).
19. 19)
  - 29. Bohme, J.F.: ‘Array processing in semi-homogeneous random fields’. Proc. Septieme Colloque Sur le Traitment di Signal est Ses Applications, Nice, France, 1979, pp. 104/1–104/4.
20. 20)
  - 24. Naoya, O., Keiichi, K., Hasegawaa, N., et al: ‘A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal’, Neural Netw., 2017, 93, pp. 1–6 (doi: 10.1016/j.neunet.2017.01.005).
21. 21)
  - 11. Guo, J., Zeng, X., She, Z.: ‘Blind source separation based on high-resolution time–frequency distributions’, Comput. Electr. Eng., 2012, 38, (1), pp. 175–184 (doi: 10.1016/j.compeleceng.2011.12.002).
22. 22)
  - 22. Thuy-Duong, N.T., Linh-Trung, N., Tran-Duc, T., et al: ‘Separation of nonstationary EEG epileptic seizures using time–frequency-based blind signal processing techniques’. Fourth Int. Conf. Biomedical Engineering in Vietnam IFMBE Proc., Vietnam, 2013, vol. 49.
23. 23)
  - 8. Melard, G., Schutter, A.H.: ‘Contributions to evolutionary spectral theory’, J. Time Ser. Anal., 1989, 10, pp. 41–63 (doi: 10.1111/j.1467-9892.1989.tb00014.x).
24. 24)
  - 39. Tsiakoulis, P., Alexandros, P., Dimitrios, D.: ‘Instantaneous frequency and bandwidth estimation using filterbank arrays’. Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE Int. Conf., Vancouver, Canada, 2013.
25. 25)
  - 46. Tong, L., Liu, R., Soon, V., et al: ‘Indeterminacy and identifiability of blind identification’, IEEE Trans. Circuits Syst., 2011, 38, pp. 499–509 (doi: 10.1109/31.76486).
26. 26)
  - 41. Marques, M., Neves, A., Marques, J.S., et al: ‘The Papoulis–Gerchberg algorithm with unknown signal bandwidth’. Int. Conf. Image Analysis and Recognition, Berlin, Heidelberg, 2006.
27. 27)
  - 12. Guo, Q., Ruan, G., Liao, Y.: ‘A time–frequency domain underdetermined blind source separation algorithm for MIMO radar signals’, Symmetry, 2017, 9, pp. 1–15.
28. 28)
  - 9. Kayhan, A.S., El-Jaroudi, A., Chaparro, L.F.: ‘Evolutionary periodogram for nonstationary signals’, IEEE Trans. Signal Process., 1994, 42, pp. 1527–1536 (doi: 10.1109/78.286968).
29. 29)
  - 18. Kokkinakis, K., Philipos, C.: ‘Advances in modern blind signal separation algorithms: theory and applications’, Synthesis lectures on algorithms and software in engineering2010, 2, (1), pp. 1–100 (doi: 10.2200/S00258ED1V01Y201003ASE006).
30. 30)
  - 33. Slepian, D.: ‘Prolate spheroidal wave functions, Fourier analysis and uncertainty IV: extensions to many dimensions; generalized prolate spheroidal functions’, Bell Syst. Tech. J., 1962, 43, pp. 3009–3057 (doi: 10.1002/j.1538-7305.1964.tb01037.x).
31. 31)
  - 2. Boashash, B.: ‘Time–frequency signal analysis and processing: a comprehensive reference’ (Elsevier, Oxford, 2003).
32. 32)
  - 28. Sekihara, K, Nagarajan, S., Poeppel, D., et al: ‘Time–frequency MEG-MUSIC algorithm’, IEEE Trans. Med. Imaging, 1999, 18, pp. 92–97 (doi: 10.1109/42.750262).
33. 33)
  - 45. Cardoso, J.F., Souloumiac, A.: ‘Blind beamforming for non-Gaussian signals’, Proc. Inst. Electr. Eng., 1993, 140, pp. 362–370.
34. 34)
  - 25. Boashash, B., Khana, N.A., Ben-Jabeura, T.: ‘Time–frequency features for pattern recognition using high-resolution TFDs: a tutorial review’, Digit. Signal Process., 2015, 40, pp. 1–30 (doi: 10.1016/j.dsp.2014.12.015).
35. 35)
  - 38. Woyczynski, W.A.: ‘Spectral representation of discrete-time stationary signals and their computer simulations’, inWoyczynski, W.A. (Ed.): ‘A first course in statistics for signal analysis’ (Birkhauser, Boston, 2010).
36. 36)
  - 42. Liebeherr, J., Markus, F., Shahrokh, V.: ‘A system-theoretic approach to bandwidth estimation’, IEEE/ACM Trans. Netw., 2016, 18, (4), pp. 1040–1053 (doi: 10.1109/TNET.2009.2035115).
37. 37)
  - 47. Cichocki, A., Amari, S., Siwek, K., et al: ‘ICALAB toolboxes’. Available at http://www.bsp.brain.riken.jp/ICALAB, 2007, accessed 2018.
38. 38)
  - 34. Slepian, D., Pollack, H.O.: ‘Prolate spheroidal wave functions, Fourier analysis and uncertainty’, Bell Syst. Tech. J., 1961, 40, pp. 43–64 (doi: 10.1002/j.1538-7305.1961.tb03976.x).
39. 39)
  - 16. Delorme, A., Sejnowski, T., Makeig, S.: ‘Enhanced detection of artifacts in EEG data using higher order statistics and independent analysis’, J. Neuroimage, 2007, 34, pp. 1443–1449 (doi: 10.1016/j.neuroimage.2006.11.004).
40. 40)
  - 28. Stanković, L.: ‘A method for time–frequency analysis’, IEEE Trans. Signal Process., 1994, 42, (1), pp. 225–229 (doi: 10.1109/78.258146).
41. 41)
  - 6. Assous, S., Boashash, B.: ‘Evaluation of the modified S-transform for time–frequency synchrony analysis and source localization’, EURASIP J. Adv. Signal Process., 2012, 49.
42. 42)
  - 13. Pal, M., Roy, R., Basu, J., et al: ‘Blind source separation: a review and analysis’. Asian Spoken Language Research and Evaluation Conf., Gurgaon, India, 2013, pp. 1–5.
43. 43)
  - 1. Cohen, L.: ‘Time–frequency analysis’ (Prentice-Hall, Englewood Cliffs, NJ, 1995).
44. 44)
  - 36. Moore, I.C., Cada, M.: ‘Prolate spheroidal wave functions: an introduction to the slepian series and its properties’, Appl. Comput. Harmon. Anal., 2004, 16, pp. 208–230 (doi: 10.1016/j.acha.2004.03.004).
45. 45)
  - 21. Delorme, A., Palme, J., Onton, J., et al: ‘Independent EEG sources are dipolar’, J. PLOS, 2012, 7, pp. 1–14.
46. 46)
  - 31. Oh, J., Senay, S., Chaparro, L.F.: ‘Signal reconstruction from nonuniformly spaced samples using evolutionary Slepian transform-based POCS’, EURASIP Journal on Advances in Signal Processing, 2010, 2010, (9), pp. 1–12.
47. 47)
  - 10. Suleesathira, R., Chaparro, L.F., Akan, A.: ‘Discrete evolutionary transform for time–frequency analysis’. Proc. Asilomar Conf. Signals, Systems and Computers, Pacific Grove, CA, USA, 1998, pp. 812–816.

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