Bradley A P, Wilson W J
Cooperative Research Centre for Sensor Signal and Information Processing (CSSIP), School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia.
Clin Neurophysiol. 2004 May;115(5):1114-28. doi: 10.1016/j.clinph.2003.11.016.
To determine a preferred wavelet transform (WT) procedure for multi-resolution analysis (MRA) of auditory evoked potentials (AEP).
A number of WT algorithms, mother wavelets, and pre-processing techniques were examined by way of critical theoretical discussion followed by experimental testing of key points using real and simulated auditory brain-stem response (ABR) waveforms. Conclusions from these examinations were then tested on a normative ABR dataset.
The results of the various experiments are reported in detail.
Optimal AEP WT MRA is most likely to occur when an over-sampled discrete wavelet transformation (DWT) is used, utilising a smooth (regularity >or=3) and symmetrical (linear phase) mother wavelet, and a reflection boundary extension policy.
This study demonstrates the practical importance of, and explains how to minimize potential artefacts due to, 4 inter-related issues relevant to AEP WT MRA, namely shift variance, phase distortion, reconstruction smoothness, and boundary artefacts.
确定一种用于听觉诱发电位（AEP）多分辨率分析（MRA）的首选小波变换（WT）方法。
通过批判性理论讨论，研究了多种WT算法、母小波和预处理技术，随后使用真实和模拟的听觉脑干反应（ABR）波形对关键点进行实验测试。然后在一个标准化ABR数据集上对这些研究得出的结论进行测试。
详细报告了各项实验的结果。
当使用过采样离散小波变换（DWT），采用平滑（正则性≥3）且对称（线性相位）的母小波以及反射边界扩展策略时，最有可能实现最佳的AEP WT MRA。
本研究证明了与AEP WT MRA相关的4个相互关联问题（即移位方差、相位失真、重建平滑度和边界伪影）的实际重要性，并解释了如何将这些问题导致的潜在伪影降至最低。
