Handojoseno A M Ardi, Shine James M, Nguyen Tuan N, Tran Yvonne, Lewis Simon J G, Nguyen Hung T
IEEE Trans Neural Syst Rehabil Eng. 2015 Sep;23(5):887-96. doi: 10.1109/TNSRE.2014.2381254. Epub 2014 Dec 18.
Freezing of Gait (FOG) is a common symptom in the advanced stages of Parkinson's disease (PD), which significantly affects patients' quality of life. Treatment options offer limited benefit and there are currently no mechanisms able to effectively detect FOG before it occurs, allowing time for a sufferer to avert a freezing episode. Electroencephalography (EEG) offers a novel technique that may be able to address this problem. In this paper, we investigated the univariate and multivariate EEG features determined by both Fourier and wavelet analysis in the confirmation and prediction of FOG. The EEG power measures and network properties from 16 patients with PD and FOG were extracted and analyzed. It was found that both power spectral density and wavelet energy could potentially act as biomarkers during FOG. Information in the frequency domain of the EEG was found to provide better discrimination of EEG signals during transition to freezing than information coded in the time domain. The performance of the FOG prediction systems improved when the information from both domains was used. This combination resulted in a sensitivity of 86.0%, specificity of 74.4%, and accuracy of 80.2% when predicting episodes of freezing, outperforming current accelerometry-based tools for the prediction of FOG.
冻结步态（FOG）是帕金森病（PD）晚期的常见症状，严重影响患者的生活质量。治疗方法效果有限，目前尚无机制能够在冻结步态发生前有效检测到它，从而让患者有时间避免冻结发作。脑电图（EEG）提供了一种可能解决这一问题的新技术。在本文中，我们研究了通过傅里叶分析和小波分析确定的单变量和多变量脑电图特征在冻结步态的确认和预测中的作用。提取并分析了16例患有冻结步态的帕金森病患者的脑电图功率测量值和网络特性。研究发现，功率谱密度和小波能量在冻结步态期间都有可能作为生物标志物。研究发现，脑电图频域中的信息在向冻结状态转变期间比时域中编码的信息能更好地区分脑电信号。当使用来自两个域的信息时，冻结步态预测系统的性能得到了改善。在预测冻结发作时，这种组合的灵敏度为86.0%，特异性为74.4%，准确率为80.2%，优于目前基于加速度计的冻结步态预测工具。
