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Empirical mode decomposition analysis of alcohol withdrawal tremor signals

机译:戒酒震颤信号的经验模式分解分析

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In this paper, we have introduced a novel method to extract involuntary tremor movement activity in patients with Alcohol Withdrawal Syndrome (AWS). Using the Empirical Mode Decomposition (EMD), we show that the variations of energy of the tremor and voluntary activity can be distinguished in different Intrinsic Mode Functions (IMF) of the recorded tremor signals. To measure the performance of our method in extracting the tremor activity and eventually developing a shortened, more objective AWS assessment tool, we compared the electronic tremor assessment employing our new technique with the consensus rating from 3 expert physicians on a 7-point scale. Based on a 3-fold cross-validation on 104 recordings from 64 patients with AW, we found that our proposed method achieves an average Root Mean Squared Error (RMSE) of 0.71 with respect to the consensus rating, a significant improvement over prior work.
机译:在本文中,我们介绍了一种提取酒精戒断综合征(AWS)患者非自愿性震颤运动活动的新方法。使用经验模态分解(EMD),我们表明震颤能量的变化和自愿活动可以在记录的震颤信号的不同本征模式函数(IMF)中进行区分。为了衡量我们提取震颤活动并最终开发出一种缩短的,更客观的AWS评估工具的方法的性能,我们将采用我们的新技术的电子震颤评估与3位专家医师的共识评分进行了7分制比较。基于对64位AW患者的104条记录进行的3倍交叉验证,我们发现,我们提出的方法相对于共识等级,平均均方根误差(RMSE)为0.71,比以前的工作有显着提高。

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