CINNAMON: A hybrid approach to change point detection and parameter estimation in single-particle tracking data
🤖 AI Summary
This work addresses the problem of detecting abrupt changes in motion regimes and estimating piecewise diffusion parameters from single-particle tracking (SPT) data. Methodologically, we propose a novel framework integrating interpretable feature engineering, topological data analysis (TDA), and statistical inference: it jointly leverages hand-crafted dynamical features (e.g., displacement distributions, velocity autocorrelation), topological features extracted via persistent homology, Bayesian changepoint detection, and maximum-likelihood parameter estimation—augmented by a custom-designed neural network to enhance discriminative capability. Our key contribution is the first incorporation of topological features into SPT changepoint analysis, achieving both high accuracy and strong interpretability. Evaluated on the benchmark dataset of the Second Anomalous Diffusion Challenge, our method significantly improves changepoint localization accuracy and robustness of diffusion coefficient estimation. This provides a new analytical tool for characterizing dynamic processes in soft matter and living cells.
Application Category
📝 Abstract
Change point detection has become an important part of the analysis of the single-particle tracking 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 CINNAMON, a hybrid approach to classifying single-particle tracking 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.
Problem
Research questions and friction points this paper is trying to address.
Detects change points in single-particle tracking data.
Estimates diffusion parameters between detected change points.
Combines neural networks, machine learning, and statistical techniques.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Combines neural networks and machine learning
Detects change points in particle trajectories
Estimates diffusion parameters between change points
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