Sato Hiroki, Tanaka Naoki, Uchida Mariko, Hirabayashi Yukiko, Kanai Makoto, Ashida Takashi, Konishi Ikuo, Maki Atsushi
Advanced Research Laboratory, 2520 Akanuma, Hatoyama, Saitama 350-0395, Japan.
Neuroimage. 2006 Nov 1;33(2):580-7. doi: 10.1016/j.neuroimage.2006.06.028. Epub 2006 Aug 28.
We have developed a wavelet-based method of detecting body-movement artifacts in optical topography (OT) signals. Although OT, which is a noninvasive imaging technique for measuring hemodynamic response related to brain activation, is particularly useful for studying infants, the signals occasionally contain undesirable artifacts caused by body movements, so data corrupted by body-movement artifacts must be eliminated to obtain reliable results. For this purpose, we applied a wavelet transform to automatically detect body-movement artifacts in OT signals. We measured OT signals from nine healthy infants in response to speech stimuli. After the continuous signals had been divided into blocks (a block is a time series of OT signal in a 30-s period including a 10-s stimulation period), they were classified into two groups (movement blocks and non-movement blocks) according to whether the participants moved or not by video judgment. Using those data, we developed a wavelet-based algorithm for detecting body-movement artifacts at a high discrimination rate being consistent with the actual body-movement state. The wavelet method has two parameters (scale and threshold), and a Monte Carlo analysis gave the mean optimal parameters as 9+/-1.9 (mean+/-standard deviation) for the scale and as 42.7+/-1.9 for the threshold. Our wavelet method with the mean optimal parameters (scale=9, threshold=43) achieved a higher discrimination rate (mean+/-standard deviation: 86.3+/-8.8%) for actual body movement than a previous method (mean+/-standard deviation: 80.6+/-8.7%) among different participants (paired t test: t(8)=2.92, p<0.05). These results demonstrate that our wavelet method is useful in practice for eliminating blocks containing body-movement artifacts in OT signals. It will contribute to obtaining reliable results from OT studies of infants.
我们开发了一种基于小波的方法来检测光学地形图（OT）信号中的身体运动伪影。虽然OT是一种用于测量与大脑激活相关的血液动力学反应的非侵入性成像技术，对研究婴儿特别有用，但信号偶尔会包含由身体运动引起的不良伪影，因此必须消除因身体运动伪影而损坏的数据才能获得可靠的结果。为此，我们应用小波变换来自动检测OT信号中的身体运动伪影。我们测量了九名健康婴儿对语音刺激的OT信号。在将连续信号分成块（一个块是30秒时间段内的OT信号时间序列，包括10秒的刺激期）后，根据视频判断参与者是否移动，将它们分为两组（运动块和非运动块）。利用这些数据，我们开发了一种基于小波的算法，以高辨别率检测身体运动伪影，该辨别率与实际身体运动状态一致。小波方法有两个参数（尺度和阈值），蒙特卡罗分析给出尺度的平均最佳参数为9±1.9（平均值±标准差），阈值为42.7±1.9。在不同参与者中，我们的具有平均最佳参数（尺度 = 9，阈值 = 43）的小波方法对于实际身体运动的辨别率（平均值±标准差：86.3±8.8%）高于先前的方法（平均值±标准差：80.6±8.7%）（配对t检验：t(8)=2.92，p<0.05）。这些结果表明，我们的小波方法在实际中可用于消除OT信号中包含身体运动伪影的块。它将有助于从婴儿的OT研究中获得可靠的结果。
