Poster Abstract: Constructing a bio-signal repository from an intensive care unit for effective big-data analysis

Sukhoon Lee; Jaeyeon Park; Doyeop Kim; Tae Young Kim; Rae Woong Park; Dukyong Yoon; Jeonggil Ko

doi:10.1145/2994551.2996712

Poster Abstract: Constructing a bio-signal repository from an intensive care unit for effective big-data analysis

Sukhoon Lee, Jaeyeon Park, Doyeop Kim, Tae Young Kim, Rae Woong Park, Dukyong Yoon, Jeonggil Ko

School of Integrated Technology

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

2 Citations (Scopus)

Abstract

Analyzing large quantities of bio-signal data can lead to new findings in patient status diagnosis and medical emer-gency event prediction. Specifically, improvements in ma-chine learning schemes suggest that by inputting clinical waveforms, designing mechanisms to predict medical emer-gencies, such as ventricular arrhythmia or sepsis, can soon be possible. However, we are still lacking the data-vaults that provide such clinically useful bio-signal data. With the goal of providing such an environment, this work focuses on developing a data repository for bio-signals collected from a hospital's intensive care init (ICU). Specifically, we design our data collection system to effectively store data from at-bed patient monitors and also integrate sensing information from bed-embedded sensing platforms, which allow filtering of noisy bio-signal samples caused by motion artifacts.

Original language	English
Title of host publication	Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
Publisher	Association for Computing Machinery, Inc
Pages	372-373
Number of pages	2
ISBN (Electronic)	9781450342636
DOIs	https://doi.org/10.1145/2994551.2996712
Publication status	Published - 2016 Nov 14
Event	14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 - Stanford, United States Duration: 2016 Nov 14 → 2016 Nov 16

Publication series

Name	Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016

Conference

Conference	14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
Country	United States
City	Stanford
Period	16/11/14 → 16/11/16

Bibliographical note

Publisher Copyright:
© 2016 Copyright held by the owner/author(s).

All Science Journal Classification (ASJC) codes

Hardware and Architecture
Computer Networks and Communications
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1145/2994551.2996712

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Lee, S., Park, J., Kim, D., Kim, T. Y., Park, R. W., Yoon, D., & Ko, J. (2016). Poster Abstract: Constructing a bio-signal repository from an intensive care unit for effective big-data analysis. In Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 (pp. 372-373). (Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016). Association for Computing Machinery, Inc. https://doi.org/10.1145/2994551.2996712

Lee, Sukhoon ; Park, Jaeyeon ; Kim, Doyeop ; Kim, Tae Young ; Park, Rae Woong ; Yoon, Dukyong ; Ko, Jeonggil. / Poster Abstract : Constructing a bio-signal repository from an intensive care unit for effective big-data analysis. Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016. Association for Computing Machinery, Inc, 2016. pp. 372-373 (Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016).

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abstract = "Analyzing large quantities of bio-signal data can lead to new findings in patient status diagnosis and medical emer-gency event prediction. Specifically, improvements in ma-chine learning schemes suggest that by inputting clinical waveforms, designing mechanisms to predict medical emer-gencies, such as ventricular arrhythmia or sepsis, can soon be possible. However, we are still lacking the data-vaults that provide such clinically useful bio-signal data. With the goal of providing such an environment, this work focuses on developing a data repository for bio-signals collected from a hospital's intensive care init (ICU). Specifically, we design our data collection system to effectively store data from at-bed patient monitors and also integrate sensing information from bed-embedded sensing platforms, which allow filtering of noisy bio-signal samples caused by motion artifacts.",

author = "Sukhoon Lee and Jaeyeon Park and Doyeop Kim and Kim, {Tae Young} and Park, {Rae Woong} and Dukyong Yoon and Jeonggil Ko",

note = "Publisher Copyright: {\textcopyright} 2016 Copyright held by the owner/author(s).; 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 ; Conference date: 14-11-2016 Through 16-11-2016",

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Lee, S, Park, J, Kim, D, Kim, TY, Park, RW, Yoon, D & Ko, J 2016, Poster Abstract: Constructing a bio-signal repository from an intensive care unit for effective big-data analysis. in Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016. Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016, Association for Computing Machinery, Inc, pp. 372-373, 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016, Stanford, United States, 16/11/14. https://doi.org/10.1145/2994551.2996712

Poster Abstract : Constructing a bio-signal repository from an intensive care unit for effective big-data analysis. / Lee, Sukhoon; Park, Jaeyeon; Kim, Doyeop; Kim, Tae Young; Park, Rae Woong; Yoon, Dukyong; Ko, Jeonggil.

Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016. Association for Computing Machinery, Inc, 2016. p. 372-373 (Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016).

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

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AU - Lee, Sukhoon

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AU - Ko, Jeonggil

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AB - Analyzing large quantities of bio-signal data can lead to new findings in patient status diagnosis and medical emer-gency event prediction. Specifically, improvements in ma-chine learning schemes suggest that by inputting clinical waveforms, designing mechanisms to predict medical emer-gencies, such as ventricular arrhythmia or sepsis, can soon be possible. However, we are still lacking the data-vaults that provide such clinically useful bio-signal data. With the goal of providing such an environment, this work focuses on developing a data repository for bio-signals collected from a hospital's intensive care init (ICU). Specifically, we design our data collection system to effectively store data from at-bed patient monitors and also integrate sensing information from bed-embedded sensing platforms, which allow filtering of noisy bio-signal samples caused by motion artifacts.

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Lee S, Park J, Kim D, Kim TY, Park RW, Yoon D et al. Poster Abstract: Constructing a bio-signal repository from an intensive care unit for effective big-data analysis. In Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016. Association for Computing Machinery, Inc. 2016. p. 372-373. (Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016). https://doi.org/10.1145/2994551.2996712

Poster Abstract: Constructing a bio-signal repository from an intensive care unit for effective big-data analysis

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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