Time-warping analysis for biological signals: methodology and application
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-95108-5 |
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| _version_ | 1850153605868290048 |
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| author | Aleksei Krotov Reza Sharif Razavian Mohsen Sadeghi Dagmar Sternad |
| author_facet | Aleksei Krotov Reza Sharif Razavian Mohsen Sadeghi Dagmar Sternad |
| author_sort | Aleksei Krotov |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-d031a564bc6541dfbb496488d71762db |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d031a564bc6541dfbb496488d71762db2025-08-20T02:25:40ZengNature PortfolioScientific Reports2045-23222025-04-0115111610.1038/s41598-025-95108-5Time-warping analysis for biological signals: methodology and applicationAleksei Krotov0Reza Sharif Razavian1Mohsen Sadeghi2Dagmar Sternad3Department of Bioengineering, Northeastern UniversityDepartment of Mechanical Engineering, Northern Arizona UniversityDepartment of Biology, Northeastern UniversityDepartment of Biology, Northeastern UniversityAbstract 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.https://doi.org/10.1038/s41598-025-95108-5 |
| spellingShingle | Aleksei Krotov Reza Sharif Razavian Mohsen Sadeghi Dagmar Sternad Time-warping analysis for biological signals: methodology and application Scientific Reports |
| title | Time-warping analysis for biological signals: methodology and application |
| title_full | Time-warping analysis for biological signals: methodology and application |
| title_fullStr | Time-warping analysis for biological signals: methodology and application |
| title_full_unstemmed | Time-warping analysis for biological signals: methodology and application |
| title_short | Time-warping analysis for biological signals: methodology and application |
| title_sort | time warping analysis for biological signals methodology and application |
| url | https://doi.org/10.1038/s41598-025-95108-5 |
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