CINNAMON: A hybrid approach to change point detection and parameter estimation in single-particle tracking data
Change point detection has become an important part of the analysis of the single-particle tracking (SPT) data, as it allows one to identify moments, in which the motion patterns of observed particles undergo significant changes. The segmentation of diffusive trajectories based on those moments may...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | JPhys Photonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2515-7647/add826 |
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| Summary: | Change point detection has become an important part of the analysis of the single-particle tracking (SPT) data, as it allows one to identify moments, in which the motion patterns of observed particles undergo significant changes. The segmentation of diffusive trajectories based on those moments may provide insight into various phenomena in soft condensed matter and biological physics. In this paper, we propose Change poInt detectioN aNd pArameter estiMation fOr aNomalous diffusion, a hybrid approach to classifying SPT trajectories, detecting change points within them, and estimating diffusion parameters in the segments between the change points. Our method is based on a combination of neural networks, feature-based machine learning, and statistical techniques. It has been benchmarked in the second anomalous diffusion challenge. The method offers a high level of interpretability due to its analytical and feature-based components. A potential use of features from topological data analysis is also discussed. |
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| ISSN: | 2515-7647 |