Parekh Ankit, Selesnick Ivan W, Osorio Ricardo S, Varga Andrew W, Rapoport David M, Ayappa Indu
Dept. of Electrical and Computer Engineering, College of Engineering, University of Iowa, United States.
Dept. of Electrical and Computer Engineering, Tandon School of Engineering, New York University, United States.
J Neurosci Methods. 2017 Aug 15;288:1-16. doi: 10.1016/j.jneumeth.2017.06.004. Epub 2017 Jun 26.
Automated single-channel spindle detectors, for human sleep EEG, are blind to the presence of spindles in other recorded channels unlike visual annotation by a human expert.
We propose a multichannel spindle detection method that aims to detect global and local spindle activity in human sleep EEG. Using a non-linear signal model, which assumes the input EEG to be the sum of a transient and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles.
The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4min to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters.
The proposed method attempts to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen.
用于人类睡眠脑电图的自动单通道纺锤波检测器，与人类专家的视觉注释不同，它对其他记录通道中纺锤波的存在视而不见。
我们提出了一种多通道纺锤波检测方法，旨在检测人类睡眠脑电图中的全局和局部纺锤波活动。使用一种非线性信号模型，该模型假设输入的脑电图是一个瞬态分量和一个振荡分量的总和，我们提出了一种多通道瞬态分离算法。假设多通道振荡分量的连续重叠块是低秩的，而瞬态分量假设为具有零基线的分段常数。估计的振荡分量与带通滤波器和Teager算子结合使用，用于检测睡眠纺锤波。
将所提出的方法应用于两个公开可用的数据库，并与7种现有的单通道自动检测器进行比较。所提出的纺锤波检测方法在两个数据库中的F分数分别平均为0.66（0.02）和0.62（0.06）。对于一个整夜的6通道脑电图信号，所提出的算法在单个相应算法参数设置下，大约需要4分钟来同时检测所有通道的睡眠纺锤波。
所提出的方法试图模仿并利用一种特定的人类专家行为，以便更好地检测纺锤波，即标记纺锤波事件的决定可能会下意识地受到数字屏幕上可见的中央通道以外的脑电图通道中纺锤波存在的影响。
