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 |
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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 |
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Conference
Conference | 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 |
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Country/Territory | 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