Tracing Patterns in Electrophysiological Time Series Data
Jan David Hüwel, Anne Gresch, Fabian Berns, Ruben Koch, Martina Düfer, Christian Beecks · 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) · 2022
When multiple sensors record spatially proximate areas of activity, spreading activity patterns appear as temporally shifted signals in multiple time series. This is particularly prominent in the domains of medical and health analysis, where multi-sensory data is the object of time-elastic investigation. Tracing the spread of these patterns still remains a challenge in time series analysis. In this paper, we propose Motif Tracking for Spatially Ordered Time Series (MoTrack), an algorithm to efficiently track the propagation of individual patterns of activity throughout spatially ordered time series. Additionally, we present the concept of propagation trees to represent this propagation for a given point of origin. We investigate our proposal by applying MoTrack to high-frequency recordings of the electrical activity of β-cells located inside the pancreatic islet. The results confirm MoTrack’s capability to trace dynamically evolving signals in such recordings and indicate that future work using this approach can address current challenges in diabetes research.