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Author Notes:

Address for reprint requests and other correspondence: L. H. Ting, The Wallace H. Coulter Dept. of Biomedical Engineering, Emory Univ. and the Georgia Institute of Technology, 313 Ferst Dr., Atlanta, GA 30332-0535 (e-mail: lting@emory.edu).


Research Funding:

National Institute of Neurological Disorders and Stroke Grant NS-058322 supported this work.


  • electromyogram
  • time series analysis
  • repeated measurements
  • balance

Statistically significant contrasts between EMG waveforms revealed using wavelet-based functional ANOVA


Journal Title:

Journal of Neurophysiology


Volume 109, Number 2


, Pages 591-602

Type of Work:

Article | Post-print: After Peer Review


We developed wavelet-based functional ANOVA (wfANOVA) as a novel approach for comparing neurophysiological signals that are functions of time. Temporal resolution is often sacrificed by analyzing such data in large time bins, increasing statistical power by reducing the number of comparisons. We performed ANOVA in the wavelet domain because differences between curves tend to be represented by a few temporally localized wavelets, which we transformed back to the time domain for visualization. We compared wfANOVA and ANOVA performed in the time domain (tANOVA) on both experimental electromyographic (EMG) signals from responses to perturbation during standing balance across changes in peak perturbation acceleration (3 levels) and velocity (4 levels) and on simulated data with known contrasts. In experimental EMG data, wfANOVA revealed the continuous shape and magnitude of significant differences over time without a priori selection of time bins. However, tANOVA revealed only the largest differences at discontinuous time points, resulting in features with later onsets and shorter durations than those identified using wfANOVA (P < 0.02). Furthermore, wfANOVA required significantly fewer (∼¼×; P < 0.015) significant F tests than tANOVA, resulting in post hoc tests with increased power. In simulated EMG data, wfANOVA identified known contrast curves with a high level of precision (r2 = 0.94 ± 0.08) and performed better than tANOVA across noise levels (P < <0.01). Therefore, wfANOVA may be useful for revealing differences in the shape and magnitude of neurophysiological signals (e.g., EMG, firing rates) across multiple conditions with both high temporal resolution and high statistical power.

Copyright information:

© 2013 the American Physiological Society

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