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Filtration Selection and Data Consilience: Distinguishing Signal from Artefact with Mechanical Impact Simulator Data.
Annals of Biomedical Engineering ( IF 3.0 ) Pub Date : 2020-07-06 , DOI: 10.1007/s10439-020-02562-5
Nathan D Schilaty 1, 2, 3, 4, 5 , Nathaniel A Bates 1, 2, 3, 5 , Ryo Ueno 1, 2 , Timothy E Hewett 6
Affiliation  

A large variety of data filtration techniques exist in biomechanics literature. Data filtration is both an ‘art’ and a ‘science’ to eliminate noise and retain true signal to draw conclusions that will direct future hypotheses, experimentation, and technology development. Thus, data consilience is paramount, but is dependent on filtration methodologies. In this study, we utilized ligament strain, vertical ground reaction force, and kinetic data from cadaveric impact simulations to assess data from four different filters (12 vs. 50 Hz low-pass; forward vs. zero lag). We hypothesized that 50 Hz filtered data would demonstrate larger peak magnitudes, but exhibit consilience of waveforms and statistical significance as compared to 12 Hz filtered data. Results demonstrated high data consilience for matched pair t test correlations of peak ACL strain (≥ 0.97), MCL strain (≥ 0.93) and vertical ground reaction force (≥ 0.98). Kinetics had a larger range of correlation (0.06–0.96) that was dependent on both external load application and direction of motion monitored. Coefficients of multiple correlation demonstrated high data consilience for zero lag filtered data. With respect to in vitro mechanical data, selection of low-pass filter cutoff frequency will influence both the magnitudes of discrete and waveform data. Dependent on the data type (i.e., strain and ground reaction forces), this will not likely significantly alter conclusions of statistical significance previously reported in the literature with high consilience of matched pair t-test correlations and coefficients of multiple correlation demonstrated. However, rotational kinetics are more sensitive to filtration selection and could be suspect to errors, especially at lower magnitudes.



中文翻译:

过滤选择和数据一致性:用机械冲击模拟器数据区分信号和人工制品。

生物力学文献中存在多种数据过滤技术。数据过滤既是一门“艺术”,也是一门“科学”,可以消除噪音并保留真实信号,从而得出指导未来假设、实验和技术发展的结论。因此,数据一致性至关重要,但取决于过滤方法。在这项研究中,我们利用来自尸体撞击模拟的韧带应变、垂直地面反作用力和动力学数据来评估来自四种不同滤波器的数据(12 与 50 Hz 低通;前向与零滞后)。我们假设 50 Hz 滤波数据将显示更大的峰值幅度,但与 12 Hz 滤波数据相比,显示出波形的一致性和统计显着性。结果证明了匹配对t 的高数据一致性测试峰值 ACL 应变 (≥ 0.97)、MCL 应变 (≥ 0.93) 和垂直地面反作用力 (≥ 0.98) 的相关性。动力学具有更大范围的相关性 (0.06-0.96),这取决于外部负载应用和所监测的运动方向。多重相关系数证明了零滞后过滤数据的高数据一致性。对于体外机械数据,低通滤波器截止频率的选择将影响离散数据和波形数据的幅度。取决于数据类型(即应变和地面反作用力),这不太可能显着改变先前在文献中报告的具有高度匹配对t 的一致性的统计显着性结论- 测试相关性和多重相关系数证明。然而,旋转动力学对过滤选择更敏感,可能会出现错误,尤其是在较低的量级时。

更新日期:2020-07-07
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