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.
Original language | English |
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Title of host publication | 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 |
Number of pages | 2 |
ISBN (Electronic) | 9798350383386 |
DOIs | |
State | Published - 2023 |
Event | 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 - Malta, Malta Duration: 7 Dec 2023 → 9 Dec 2023 |
Publication series
Name | 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 |
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Conference
Conference | 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 |
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Country/Territory | Malta |
City | Malta |
Period | 7/12/23 → 9/12/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Funding
* This study was supported in part by an Israel Science Foundation (ISF) grant (297/18). The authors thank Dr. Katya Belelovsky, Dr. Noa Benaroya-Milshtein and Yocheved Loewenstern for collecting, preprocessing and providing data.
Funders | Funder number |
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Israel Science Foundation | 297/18 |