基于小波分解利用脑电图信号检测帕金森病患者的步态冻结
The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition.
作者信息
Handojoseno A M Ardi, Shine James M, Nguyen Tuan N, Tran Yvonne, Lewis Simon J G, Nguyen Hung T
机构信息
Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway, NSW 2007, Australia.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:69-72. doi: 10.1109/EMBC.2012.6345873.
Freezing of Gait (FOG) is one of the most disabling gait disturbances of Parkinson's disease (PD). The experience has often been described as "feeling like their feet have been glued to the floor while trying to walk" and as such it is a common cause of falling in PD patients. In this paper, EEG subbands Wavelet Energy and Total Wavelet Entropy were extracted using the multiresolution decomposition of EEG signal based on the Discrete Wavelet Transform and were used to analyze the dynamics in the EEG during freezing. The Back Propagation Neural Network classifier has the ability to identify the onset of freezing of PD patients during walking using these features with average values of accuracy, sensitivity and specificity are around 75 %. This results have proved the feasibility of utilized EEG in future treatment of FOG.
冻结步态（FOG）是帕金森病（PD）中最致残的步态障碍之一。这种体验常被描述为“试图行走时感觉双脚像被粘在地板上”，因此它是PD患者跌倒的常见原因。在本文中，基于离散小波变换的脑电信号多分辨率分解提取了脑电子带小波能量和总小波熵，并用于分析冻结期间脑电的动态变化。反向传播神经网络分类器能够利用这些特征识别PD患者行走过程中冻结的发作，其准确率、灵敏度和特异性的平均值约为75%。这一结果证明了利用脑电在未来治疗FOG中的可行性。
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