TY - JOUR
T1 - Filtration Selection and Data Consilience
T2 - Distinguishing Signal from Artefact with Mechanical Impact Simulator Data
AU - Schilaty, Nathan D.
AU - Bates, Nathaniel A.
AU - Ueno, Ryo
AU - Hewett, Timothy E.
N1 - Publisher Copyright:
© 2020, Biomedical Engineering Society.
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - Biomechanics
KW - Butterworth
KW - Data analysis
KW - Filter
KW - Low-pass filter
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U2 - 10.1007/s10439-020-02562-5
DO - 10.1007/s10439-020-02562-5
M3 - Article
C2 - 32632532
AN - SCOPUS:85087614589
SN - 0090-6964
VL - 49
SP - 334
EP - 344
JO - Annals of Biomedical Engineering
JF - Annals of Biomedical Engineering
IS - 1
ER -