Tweedell Andrew J, Haynes Courtney A, Tenan Matthew S
U.S. Army Research Laboratory, Human Research & Engineering Directorate, Aberdeen Proving Ground, Maryland, USA.
U.S. Army Research Laboratory, Human Research & Engineering Directorate, Aberdeen Proving Ground, Maryland, USA.
Ultrasound Med Biol. 2017 May;43(5):1070-1075. doi: 10.1016/j.ultrasmedbio.2016.12.019. Epub 2017 Feb 22.
The study purpose was to evaluate the use of computer-automated algorithms as a replacement for subjective, visual determination of muscle contraction onset using M-mode ultrasonography. Biceps and quadriceps contraction images were analyzed visually and with three different classes of algorithms: pixel standard deviation (SD), high-pass filter and Teager Kaiser energy operator transformation. Algorithmic parameters and muscle onset threshold criteria were systematically varied within each class of algorithm. Linear relationships and agreements between computed and visual muscle onset were calculated. The top algorithms were high-pass filtered with a 30 Hz cutoff frequency and 20 SD above baseline, Teager Kaiser energy operator transformation with a 1200 absolute SD above baseline and SD at 10% pixel deviation with intra-class correlation coefficients (mean difference) of 0.74 (37.7 ms), 0.80 (61.8 ms) and 0.72 (109.8 ms), respectively. The results suggest that computer automated determination using high-pass filtering is a potential objective alternative to visual determination in human movement science.
本研究的目的是评估使用计算机自动算法替代通过M型超声心动图主观视觉判定肌肉收缩起始。对肱二头肌和股四头肌的收缩图像进行了视觉分析，并使用了三类不同的算法：像素标准差（SD）、高通滤波器和蒂杰 - 凯泽能量算子变换。在每类算法中，系统地改变算法参数和肌肉起始阈值标准。计算了算法得出的肌肉起始与视觉判定之间的线性关系和一致性。最佳算法分别是截止频率为30Hz且高于基线20标准差的高通滤波、高于基线绝对标准差为1200的蒂杰 - 凯泽能量算子变换以及像素偏差为10%时的标准差，其组内相关系数（平均差异）分别为0.74（37.7毫秒）、0.80（61.8毫秒）和0.72（109.8毫秒）。结果表明，在人体运动科学中，使用高通滤波的计算机自动判定是视觉判定的一种潜在客观替代方法。
