@inproceedings{efdfafaf7c164077917e4466c43cd66a,
title = "Temporally Aligned Segmentation and Clustering (TASC) of Behavioral Time Series",
abstract = "Behavior consists of a series of repeating yet variable discrete motifs across various timescales. We introduce a framework for temporally aligned segmentation and clustering (TASC) of behavioral time series. TASC is designed to extract such motif recurrences in high temporal resolution. This framework operates iteratively in two steps: (1) embedding of time series segments and calculation of linearly aligned distances within the clustered space, and (2) recalculating of the clustered space after alignment. We evaluated TASC on a semi-synthetic experimental and a clinical dataset, and it demonstrated enhanced segmentation performance. TASC may be applied to other domains where analysis of recurring time series patterns with high temporal precision is needed.Clinical Relevance: This framework enables identifying the temporal structure of discrete pathological behaviors, such as tics properties in Tourette's syndrome.",
author = "Ekaterina Zinkovskaia and Orel Tahary and Izhar Bar-Gad",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 ; Conference date: 07-12-2023 Through 09-12-2023",
year = "2023",
doi = "10.1109/ieeeconf58974.2023.10405072",
language = "الإنجليزيّة",
series = "2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "173--174",
booktitle = "2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023",
address = "الولايات المتّحدة",
}
