Noise attenuation analysis on auditory evoked potential based on maximum length sequence_Journal of Biomedical Engineering

Authors：

CHEN Yun’er ¹ , ZHAN Chang’an ¹ , PENG Xian ² , FU Qiuyang ³ ,  WANG Tao ¹

1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, P.R.China;
2. Institute of Rehabilitation Engineering, Guangdong Provincial Work Injury Rehabilitation Center, Guangzhou 510440, P.R.China;
3. Department of Otolaryngology-Head and Neck Surgery, Guangdong No.2 Provincial People’s Hospital, Guangzhou 510317, P.R.China;

Corresponding author：

WANG Tao, Email: taowang@smu.edu.cn

Keywords：

auditory evoked potential; maximum length sequence; noise attenuation

DOI：

10.7507/1001-5515.201703065

Video：

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The maximum length sequence (m-sequence) has been successfully used to study the linear/nonlinear components of auditory evoked potential (AEP) with rapid stimulation. However, more study is needed to evaluate the effect of the m-sequence order in terms of the noise attenuation performance. This study aimed to address this issue using response-free electroencephalogram (EEG) and EEGs with nonlinear AEPs. We examined the noise attenuation ratios to evaluate the noise variation for the calculations of superimposed averaging and cross-correlation, respectively, which constitutes the main process in the deconvolution method using the dataset of spontaneous EEGs to simulate the cases of different orders (order 5 to 12) of m-sequences. And an experiment using m-sequences of order 7 and 9 was performed in true cases with substantial linear and nonlinear AEPs. The results demonstrate that the noise attenuation ratio is well agreed with the theoretical value derived from the properties of m-sequences on the random noise condition. The comparison of waveforms for AEP components from two m-sequences showed high similarity suggesting the insensitivity of AEP to the m-sequence order. This study provides a more comprehensive solution to the selection of m-sequences which will facilitate the feasible application on the nonlinear AEP with m-sequence method.

Citation： CHEN Yun’er, ZHAN Chang’an, PENG Xian, FU Qiuyang, WANG Tao. Noise attenuation analysis on auditory evoked potential based on maximum length sequence. Journal of Biomedical Engineering, 2018, 35(2): 266-272. doi: 10.7507/1001-5515.201703065 Copy

1.	Eysholdt U, Schreiner C. Maximum length sequence—A fast method for measuring brain-stem evoked responses. Audiology, 1982, 21(3): 242-250.
2.	Burkard R, Jomes S, Jones T. Conventional and cross-correlation brain-stem auditory evoked responses in the white leghorn click: Rate manipulations. Journal of the Acoustical Society of America, 1994, 95(4): 2136-2144.
3.	Shi Y, Hecox K E. Nonlinear system identification by m-pulse sequence: Application to brainstem auditory evoked response. IEEE Trans Biomed Eng, 1991, 38(9): 834-845.
4.	Lasky R E, Veen B D V, Marier M M. Nonlinear functional modeling of scalp recorded auditory evoked responses to maximum length sequences. Hearing Research, 1998, 120(1/2): 133-142.
5.	March R R. Signal to noise constraints on maximum length sequence auditory brain stem response. Ear and Hearing, 1992, 13(6): 396-400.
6.	Thornton A R D. High rate otoacoustic emissions. Journal of the Acoustical Society of America, 1993, 94(1): 132-136.
7.	Thornton A R D. Click-evoked otoacoustic emissions: New techniques and applications. British Journal of Audiology, 1993, 27(2): 109-115.
8.	Bohorquez J, Ozdamar O. Signal to noise ratio analysis of maximum length sequence deconvolution of overlapping evoked potentials. Journal of the Acoustical society of America, 2006, 119( 1): 2881-2888.
9.	Korenberg M J, Hunter I W. The identification of nonlinear biological systems: Wiener kernel approaches. Annals of Biomedical Engineering, 1990, 18(6): 629-654.
10.	Lee Y W, Schetzen M. Measurement of Wiener kernels of a non-linear system by cross-correlation. International Journal of Control, 1965, 2(3): 237-254.
11.	Sutter E E. A practical non-stochastic approach to nonlinear time-domain analysis. Advanced Methods of Physiological System Modeling, 1987, 1: 303-315.
12.	吕辉, 何晶, 王刚. 伪随机序列中本原多项式生成算法. 计算机工程, 2004, 30(16): 108-109, 165.
13.	Barker H A, Pradisthayon T. High-order autocorrelation functions of pseudorandom signals based on m sequences. Proceedings of the Institution of Electrical Engineers, 1970, 117(9): 1857-1863.
14.	Peng Xian, Chen Yuner, Wang Tao et al. Noise attenuation estimation for maximum length sequences in deconvolution process of auditory evoked potentials. Computational and Mathematical Methods in Medicine, 2017, 2017: 3927486-3927495.
15.	Schimmel H. The (±) reference: Accuracy of estimated mean components in average response studies. Science, 1967, 157(3784): 92-94.
16.	黄召辉, 林霖, 王涛. 最大长序列诱发听性脑干反应的线性与非线性成分引出率和稳定性分析. 生物医学工程学报, 2016, 35(2): 148-154.
17.	Hecox K E, Cone B, Blaw M E. Brainstem auditory evoked response in the diagnosis of pediatric neurologic diseases. Neurology, 1981, 31(7): 832-840.
18.	Hu Jinyan, Yan Gang, Wang Tao. Identifying odd/even-order binary kernel slices for a nonlinear system using inverse repeat m-sequences Computational and Mathematical Methods in Medicine, 2015, 2015(3): 454638-454647.

1. Eysholdt U, Schreiner C. Maximum length sequence—A fast method for measuring brain-stem evoked responses. Audiology, 1982, 21(3): 242-250.
2. Burkard R, Jomes S, Jones T. Conventional and cross-correlation brain-stem auditory evoked responses in the white leghorn click: Rate manipulations. Journal of the Acoustical Society of America, 1994, 95(4): 2136-2144.
3. Shi Y, Hecox K E. Nonlinear system identification by m-pulse sequence: Application to brainstem auditory evoked response. IEEE Trans Biomed Eng, 1991, 38(9): 834-845.
4. Lasky R E, Veen B D V, Marier M M. Nonlinear functional modeling of scalp recorded auditory evoked responses to maximum length sequences. Hearing Research, 1998, 120(1/2): 133-142.
5. March R R. Signal to noise constraints on maximum length sequence auditory brain stem response. Ear and Hearing, 1992, 13(6): 396-400.
6. Thornton A R D. High rate otoacoustic emissions. Journal of the Acoustical Society of America, 1993, 94(1): 132-136.
7. Thornton A R D. Click-evoked otoacoustic emissions: New techniques and applications. British Journal of Audiology, 1993, 27(2): 109-115.
8. Bohorquez J, Ozdamar O. Signal to noise ratio analysis of maximum length sequence deconvolution of overlapping evoked potentials. Journal of the Acoustical society of America, 2006, 119( 1): 2881-2888.
9. Korenberg M J, Hunter I W. The identification of nonlinear biological systems: Wiener kernel approaches. Annals of Biomedical Engineering, 1990, 18(6): 629-654.
10. Lee Y W, Schetzen M. Measurement of Wiener kernels of a non-linear system by cross-correlation. International Journal of Control, 1965, 2(3): 237-254.
11. Sutter E E. A practical non-stochastic approach to nonlinear time-domain analysis. Advanced Methods of Physiological System Modeling, 1987, 1: 303-315.
12. 吕辉, 何晶, 王刚. 伪随机序列中本原多项式生成算法. 计算机工程, 2004, 30(16): 108-109, 165.
13. Barker H A, Pradisthayon T. High-order autocorrelation functions of pseudorandom signals based on m sequences. Proceedings of the Institution of Electrical Engineers, 1970, 117(9): 1857-1863.
14. Peng Xian, Chen Yuner, Wang Tao et al. Noise attenuation estimation for maximum length sequences in deconvolution process of auditory evoked potentials. Computational and Mathematical Methods in Medicine, 2017, 2017: 3927486-3927495.
15. Schimmel H. The (±) reference: Accuracy of estimated mean components in average response studies. Science, 1967, 157(3784): 92-94.
16. 黄召辉, 林霖, 王涛. 最大长序列诱发听性脑干反应的线性与非线性成分引出率和稳定性分析. 生物医学工程学报, 2016, 35(2): 148-154.
17. Hecox K E, Cone B, Blaw M E. Brainstem auditory evoked response in the diagnosis of pediatric neurologic diseases. Neurology, 1981, 31(7): 832-840.
18. Hu Jinyan, Yan Gang, Wang Tao. Identifying odd/even-order binary kernel slices for a nonlinear system using inverse repeat m-sequences Computational and Mathematical Methods in Medicine, 2015, 2015(3): 454638-454647.

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