TY - GEN
T1 - Poster Abstract
T2 - 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
AU - Lee, Sukhoon
AU - Park, Jaeyeon
AU - Kim, Doyeop
AU - Kim, Tae Young
AU - Park, Rae Woong
AU - Yoon, Dukyong
AU - Ko, Jeonggil
N1 - Publisher Copyright:
© 2016 Copyright held by the owner/author(s).
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
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
Y2 - 14 November 2016 through 16 November 2016
ER -