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<p>Any set of biological signals has variability, both in the temporal and spatial domains. To extract characteristic features of the ensemble, these spatiotemporal profiles are typically summarized by their mean and variance, often requiring prior padding or resampling of the data to equalize signal length. Such compression can conceal essential information in the signal. This work presents the method of time-warping, reformulated as elastic functional data analysis (EFDA), in an accessible way. This powerful approach rescales the temporal evolution of signals, aligns them accurately, decouples their spatial and temporal variability, and faithfully extracts their characteristics. This technique was compared to conventional methods of normalizing or padding data followed by averaging, using synthetized signals with controlled variability and real human data from a complex manipulation task. Comparative analysis demonstrates that EFDA successfully reveals otherwise concealed features and teases apart temporal and spatial variability. Critical advances to the more common method of dynamic time-warping (DTW) are discussed. Application of EFDA and potential new insights are illustrated in the context of human motor neuroscience. Annotated code to facilitate the use of this technique is provided.</p>

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Time-warping analysis for biological signals: methodology and application

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月5日

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  • Aleksei Krotov,
  • Reza Sharif Razavian,
  • Mohsen Sadeghi,
  • Dagmar Sternad

摘要

Any set of biological signals has variability, both in the temporal and spatial domains. To extract characteristic features of the ensemble, these spatiotemporal profiles are typically summarized by their mean and variance, often requiring prior padding or resampling of the data to equalize signal length. Such compression can conceal essential information in the signal. This work presents the method of time-warping, reformulated as elastic functional data analysis (EFDA), in an accessible way. This powerful approach rescales the temporal evolution of signals, aligns them accurately, decouples their spatial and temporal variability, and faithfully extracts their characteristics. This technique was compared to conventional methods of normalizing or padding data followed by averaging, using synthetized signals with controlled variability and real human data from a complex manipulation task. Comparative analysis demonstrates that EFDA successfully reveals otherwise concealed features and teases apart temporal and spatial variability. Critical advances to the more common method of dynamic time-warping (DTW) are discussed. Application of EFDA and potential new insights are illustrated in the context of human motor neuroscience. Annotated code to facilitate the use of this technique is provided.