TSSummarize: A Visual Strategy to Summarize Biosignals

João Rodrigues; Phillip Probst; Hugo Gamboa

doi:10.1109/ICBSII51839.2021.9445154

TSSummarize: A Visual Strategy to Summarize Biosignals

João Rodrigues, Phillip Probst, Hugo Gamboa

LIBPhys-UNL

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Visual tools enhance the human ability to detect structures found on time series. Medical doctors and data-scientists rely on their visual abilities to perform time series analysis. A visual tool that would summarize several sources of information of time series would be of great value and is not yet provided in the literature. This work proposes a novel unsupervised visual strategy to summarize a time series and compact several layers of information. The strategy extracts information from the Self-Similarity Matrix (SSM). This data source is able to segment the time series, detect events and show relationships between subsequences. The visual strategy has been tested on several use-cases from the medical domain, proving to be type agnostic, intuitive and compact.

Original language	English
Title of host publication	Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9781665441261
DOIs	https://doi.org/10.1109/ICBSII51839.2021.9445154
Publication status	Published - 25 Mar 2021
Event	7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021 - Chennai, India Duration: 25 Mar 2021 → 27 Mar 2021

Conference

Conference	7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021
Country/Territory	India
City	Chennai
Period	25/03/21 → 27/03/21

Keywords

biosignals
events
segmentation
summarize
time series
unsupervised
visualization

Access to Document

10.1109/ICBSII51839.2021.9445154

Cite this

@inproceedings{fa1c7f9cd54340d4997e8d279eafabff,

title = "TSSummarize: A Visual Strategy to Summarize Biosignals",

abstract = "Visual tools enhance the human ability to detect structures found on time series. Medical doctors and data-scientists rely on their visual abilities to perform time series analysis. A visual tool that would summarize several sources of information of time series would be of great value and is not yet provided in the literature. This work proposes a novel unsupervised visual strategy to summarize a time series and compact several layers of information. The strategy extracts information from the Self-Similarity Matrix (SSM). This data source is able to segment the time series, detect events and show relationships between subsequences. The visual strategy has been tested on several use-cases from the medical domain, proving to be type agnostic, intuitive and compact. ",

keywords = "biosignals, events, segmentation, summarize, time series, unsupervised, visualization",

author = "Jo{\~a}o Rodrigues and Phillip Probst and Hugo Gamboa",

note = "Funding Information: info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FAI%2F0105%2F2019/PT# info:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F142816%2F2018/PT# ACKNOWLEDGMENT This work was partly supported by Fundac¸{\~a}o para a Ci{\^e}ncia e Tecnologia, under projects OPERATOR (ref. 04/SI/2019). Publisher Copyright: {\textcopyright} 2021 IEEE. ; 7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021 ; Conference date: 25-03-2021 Through 27-03-2021",

year = "2021",

month = mar,

day = "25",

doi = "10.1109/ICBSII51839.2021.9445154",

language = "English",

booktitle = "Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

}

Rodrigues, J , Probst, P & Gamboa, H 2021, TSSummarize: A Visual Strategy to Summarize Biosignals. in Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021., 9445154, Institute of Electrical and Electronics Engineers (IEEE), 7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021, Chennai, India, 25/03/21. https://doi.org/10.1109/ICBSII51839.2021.9445154

TSSummarize: A Visual Strategy to Summarize Biosignals. / Rodrigues, João ; Probst, Phillip ; Gamboa, Hugo.
Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021. Institute of Electrical and Electronics Engineers (IEEE), 2021. 9445154.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - TSSummarize

T2 - 7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

AU - Rodrigues, João

AU - Probst, Phillip

AU - Gamboa, Hugo

N1 - Funding Information: info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FAI%2F0105%2F2019/PT# info:eu-repo/grantAgreement/FCT/OE/PD%2FBDE%2F142816%2F2018/PT# ACKNOWLEDGMENT This work was partly supported by Fundac¸ão para a Ciência e Tecnologia, under projects OPERATOR (ref. 04/SI/2019). Publisher Copyright: © 2021 IEEE.

PY - 2021/3/25

Y1 - 2021/3/25

N2 - Visual tools enhance the human ability to detect structures found on time series. Medical doctors and data-scientists rely on their visual abilities to perform time series analysis. A visual tool that would summarize several sources of information of time series would be of great value and is not yet provided in the literature. This work proposes a novel unsupervised visual strategy to summarize a time series and compact several layers of information. The strategy extracts information from the Self-Similarity Matrix (SSM). This data source is able to segment the time series, detect events and show relationships between subsequences. The visual strategy has been tested on several use-cases from the medical domain, proving to be type agnostic, intuitive and compact.

AB - Visual tools enhance the human ability to detect structures found on time series. Medical doctors and data-scientists rely on their visual abilities to perform time series analysis. A visual tool that would summarize several sources of information of time series would be of great value and is not yet provided in the literature. This work proposes a novel unsupervised visual strategy to summarize a time series and compact several layers of information. The strategy extracts information from the Self-Similarity Matrix (SSM). This data source is able to segment the time series, detect events and show relationships between subsequences. The visual strategy has been tested on several use-cases from the medical domain, proving to be type agnostic, intuitive and compact.

KW - biosignals

KW - events

KW - segmentation

KW - summarize

KW - time series

KW - unsupervised

KW - visualization

UR - http://www.scopus.com/inward/record.url?scp=85107992275&partnerID=8YFLogxK

U2 - 10.1109/ICBSII51839.2021.9445154

DO - 10.1109/ICBSII51839.2021.9445154

M3 - Conference contribution

AN - SCOPUS:85107992275

BT - Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

PB - Institute of Electrical and Electronics Engineers (IEEE)

Y2 - 25 March 2021 through 27 March 2021

ER -

TSSummarize: A Visual Strategy to Summarize Biosignals

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this