Sun Limin, Ahlfors Seppo P, Hinrichs Hermann
Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.
Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Brain Topogr. 2016 Nov;29(6):783-790. doi: 10.1007/s10548-016-0513-3. Epub 2016 Aug 8.
Magnetoencephalography (MEG) signals are commonly contaminated by cardiac artefacts (CAs). Principle component analysis and independent component analysis have been widely used for removing CAs, but they typically require a complex procedure for the identification of CA-related components. We propose a simple and efficient method, resampled moving average subtraction (RMAS), to remove CAs from MEG data. Based on an electrocardiogram (ECG) channel, a template for each cardiac cycle was estimated by a weighted average of epochs of MEG data over consecutive cardiac cycles, combined with a resampling technique for accurate alignment of the time waveforms. The template was subtracted from the corresponding epoch of the MEG data. The resampling reduced distortions due to asynchrony between the cardiac cycle and the MEG sampling times. The RMAS method successfully suppressed CAs while preserving both event-related responses and high-frequency (>45 Hz) components in the MEG data.
脑磁图（MEG）信号通常会受到心脏伪迹（CA）的干扰。主成分分析和独立成分分析已被广泛用于去除心脏伪迹，但它们通常需要一个复杂的程序来识别与心脏伪迹相关的成分。我们提出了一种简单有效的方法，即重采样移动平均减法（RMAS），用于从MEG数据中去除心脏伪迹。基于心电图（ECG）通道，通过对连续心动周期的MEG数据片段进行加权平均，并结合重采样技术以精确对齐时间波形，来估计每个心动周期的模板。然后从MEG数据的相应片段中减去该模板。重采样减少了由于心动周期与MEG采样时间不同步而导致的失真。RMAS方法成功抑制了心脏伪迹，同时保留了MEG数据中的事件相关响应和高频（>45Hz）成分。
