Feature Extraction of Brainstem Auditory Evoked Potential Based on Wavelet Multi-resolution Analysis_Journal of Biomedical Engineering

Authors：

 TIANFuying , SUNYing

School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;

Corresponding author：

TIANFuying, Email: fuying1020@sina.com

Keywords：

brainstem auditory evoked potential; wavelet transformation; correlation analysis

DOI：

10.7507/1001-5515.20150094

Video：

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We proposed a multi-resolution-wavelet-transform based method to extract brainstem auditory evoked potential (BAEP) from the background noise and then to identify its characteristics correctly. Firstly we discussed the mother wavelet and wavelet transform algorithm and proved that bi-orthogonal wavelet bior5.5 and stationary discrete wavelet transform (SWT) were more suitable for BAEP signals. The correlation analysis of D6 scale wavelet coefficients between single trails and the ensemble average of all trails showed that the trails with good correlation (> 0.4) had higher signal-to-noise ratio, so that we could get a clear BAEP from a few trails by an average and wavelet filter method. Finally, we used this method to select desirable trails, extracted BAEP from every 10 trails and calculated theⅠ-Ⅴinter-waves' latency. The results showed that this strategy of trail selection was efficient. This method can not only achieve better de-noising effect, but also greatly reduce the stimulation time needed as well.

Citation： TIANFuying, SUNYing. Feature Extraction of Brainstem Auditory Evoked Potential Based on Wavelet Multi-resolution Analysis. Journal of Biomedical Engineering, 2015, 32(3): 514-519. doi: 10.7507/1001-5515.20150094 Copy

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9.	SCHELLER B, ZWISSLER B, DAUNDERER M, et al. The influence of wavelets on multiscale analysis and parametrization of midlatency auditory evoked potentials[J]. Biol Cybern, 2006, 95(3): 193-203.
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1. REGEN D. Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine[M]. New York: Elsevier, 1989.
2. 潘映辐.临床诱发电位学[M].北京:人民卫生出版社, 1988.
3. GEORGIADIS S D. RANTA-AHO P O, TARVAINEN M P, et al. Single-trial dynamical estimation of event-related potentials: a Kalman filter-based approach[J]. IEEE Trans Biomed Eng, 2005, 52(8): 1397-1406.
4. 侯宏花, 桂志国.基于小波熵的心电信号去噪处理[J].中国生物医学工程学报, 2010, 29(1): 22-28, 34.
5. LEE Y H, KIM S S, PARK S I, et al. Extraction of enhanced evoked potentials using wavelet filtering[J]. Multimed Tools Appl, 2013, 63(1): 45-61.
6. 闫蓓, 刘莎, 李建华, 等.单通道视觉诱发脑电的单次提取方法研究[J].仪器仪表学报, 2012, 33(4):905-910.
7. 孙迎, 叶英.听觉诱发电位提取方法的研究与分析[J].中国生物医学工程学报, 2006, 25(1):117-120.
8. BRADLEY A P, WILSON W J. On wavelet analysis of auditory evoked potentials[J]. Clin Neurophysiol, 2004, 115(5): 1114-1128.
9. SCHELLER B, ZWISSLER B, DAUNDERER M, et al. The influence of wavelets on multiscale analysis and parametrization of midlatency auditory evoked potentials[J]. Biol Cybern, 2006, 95(3): 193-203.
10. WILSON W J. The relationship between the auditory brain-stem response and its reconstructed waveforms following discrete wavelet transformation[J]. Clin Neurophysiol, 2004, 115(5): 1129-1139.
11. ACIR N, ERKAN Y, BAHTIYAR Y A. Auditory brainstem response classification for threshold detection using estimated evoked potential data: comparison with ensemble averaged data[J]. Neural Comput Appl, 2013, 22(5): 859-867.
12. NASON G P, SILVERMAN B W. The stationary wavelet transform and some statistical applications[M]//ANTONIADIS A, OPPENHEIM G. Wavelets and statistics. New York: Springer New York, 1995, 103: 281-299.

Journal of Biomedical Engineering

Feature Extraction of Brainstem Auditory Evoked Potential Based on Wavelet Multi-resolution Analysis

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