论文标题
WQN算法在EEG信号中适应性纠正伪像
The WQN algorithm to adaptively correct artifacts in the EEG signal
论文作者
论文摘要
小波分位数归一化（WQN）是一种非参数算法，旨在在实时临床监测中有效地从单渠道脑电图中从单渠道脑电图中删除瞬时伪像。如今，当检测到信号中的伪影时，脑电图监测机将暂停其输出。因此，消除不可预测的脑电图将允许改善监视的连续性。我们分析了WQN算法，该算法包括将伪影时期的小波系数分布转运为参考，未污染的信号分布。我们表明该算法将信号规范化。为了确认该算法非常适合，我们研究了脑电图和伪像小波系数的经验分布。我们将WQN算法与经典小波阈值方法进行比较，并研究它们对小波系数分布的影响。我们表明，WQN算法保留了分布，而阈值方法可能会导致变化。最后，我们展示了如何使用WQN算法从EEG信号中计算出的频谱图。
Wavelet quantile normalization (WQN) is a nonparametric algorithm designed to efficiently remove transient artifacts from single-channel EEG in real-time clinical monitoring. Today, EEG monitoring machines suspend their output when artifacts in the signal are detected. Removing unpredictable EEG artifacts would thus allow to improve the continuity of the monitoring. We analyze the WQN algorithm which consists in transporting wavelet coefficient distributions of an artifacted epoch into a reference, uncontaminated signal distribution. We show that the algorithm regularizes the signal. To confirm that the algorithm is well suited, we study the empirical distributions of the EEG and the artifacts wavelet coefficients. We compare the WQN algorithm to the classical wavelet thresholding methods and study their effect on the distribution of the wavelet coefficients. We show that the WQN algorithm preserves the distribution while the thresholding methods can cause alterations. Finally, we show how the spectrogram computed from an EEG signal can be cleaned using the WQN algorithm.
