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Signal quality and data fusion for false alarm reduction in the intensive care unit

Abstract:

Due to a lack of integration between different sensors, false alarms (FA) in the intensive care unit (ICU) are frequent and can lead to reduced standard of care. We present a novel framework for FA reduction using a machine learning approach to combine up to 114 signal quality and physiological features extracted from the electrocardiogram, photoplethysmograph, and optionally the arterial blood pressure waveform. A machine learning algorithm was trained and evaluated on a database of 4107 exp...

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Journal:
Journal of Electrocardiology More from this journal
Volume:
45
Issue:
6
Pages:
596-603
Publication date:
2012-11-01
DOI:
EISSN:
1532-8430
ISSN:
0022-0736
Language:
English
Keywords:
Pubs id:
pubs:359735
UUID:
uuid:6aa7d169-9d8b-4eac-91d0-d24c3af16fd4
Local pid:
pubs:359735
Source identifiers:
359735
Deposit date:
2013-11-17

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