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 | |
| 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 |
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All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Computer Networks and Communications
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
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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
TY - GEN
T1 - Poster Abstract
T2 - Constructing a bio-signal repository from an intensive care unit for effective big-data analysis
AU - Lee, Sukhoon
AU - Park, Jaeyeon
AU - Kim, Doyeop
AU - Kim, Tae Young
AU - Park, Rae Woong
AU - Yoon, Dukyong
AU - Ko, Jeonggil
PY - 2016/11/14
Y1 - 2016/11/14
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85007105521&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007105521&partnerID=8YFLogxK
U2 - 10.1145/2994551.2996712
DO - 10.1145/2994551.2996712
M3 - Conference contribution
AN - SCOPUS:85007105521
T3 - Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
SP - 372
EP - 373
BT - Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
PB - Association for Computing Machinery, Inc
ER -