On-line adaptive trend extraction of multiple physiological signals for alarm filtering in intensive care units
Abstract
Abstract This paper presents an alarm validation system dedicated to patient monitoring in intensive care units (ICU). Several physiological signals are continuously acquired and an on‐line trend extraction method is implemented for each one. A trend is a succession of contiguous semi‐quantitative episodes, expressing the time evolution of a signal with several symbols. The difference between the trend and the signal is considered as a residual. In this paper, trend extraction is based on several thresholds that are adapted on‐line, following the signal variations. Multivariate change indices are further deduced from the trends and the residuals. They provide an indication of changes to patient hemodynamic and respiratory state. An alarm validation system based on these indices is then proposed, which uses fuzzy decision making. Whenever a monitoring system sets off an alarm, the system proposed carries out a backward analysis of the physiological variables monitored. The system enables various policies to be implemented: filtering of false alarms due to artifacts, confirmation of true alarms due to a patient state change. The system was tested on more than 50 h of data recorded on adult patients in an ICU unit, when 105SpO2alarms were set off by a fixed threshold alarm system. The comparison between the decision made on‐line by the validation system and the decision made by a medical expert for each of these alarms showed that the system is able to recognize 100% of true alarms and filter 50–80% of false alarms. Copyright © 2009 John Wiley & Sons, Ltd.
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Some papers find irregular patterns in vital signs time series such as abnormal episodes in ECG pulses, SpO2 signal, and blood glucose level, mostly discovering unusual temporal patterns in continuous data.
“Some of the papers considered finding irregular patterns in vital signs time series such as abnormal episodes in ECG pulses [31,32], SpO2 signal [33] and blood glucose level [28], which mostly discover unusual temporal patterns in continuous data.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [33]
In online health monitoring systems, raising an alarm as soon as an anomaly in vital signs is detected triggers instant reaction, and such alarm systems are usually designed for monitoring patients in clinical units.
“In online health monitoring systems, raising alarm as soon as detecting any anomaly in vital signs will be triggered to have instant reaction and such alarm system are usually designed for monitoring patients in clinical units [33].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [33]
In physiological data, time-domain features are common because traditional decision-making frameworks on vital signs are based on observable trends in the signal.
“In physiological data, the time-domain features are common, because the traditional decision making frameworks on vital signs are based on the observable trends in the signal [33].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [33]
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- Authors
- Charbonnier, S., & Gentil, S.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Charbonnier, S.
- ORCID
- Charbonnier, S.
- Venue
- International Journal of Adaptive Control and Signal Processing
- Publisher
- Wiley
- Published
- 2009-06-03
- Volume
- 24
- Issue
- 5
- Pages
- 382-408
- ISSN
- 0890-6327 (ISSN-L)
- 1099-1115
- OpenAlex
- W2012658957
- Cited by
- 26
- References
- 44
- Citations by year
- 2026: 1 · 2023: 1 · 2022: 2 · 2020: 3 · 2019: 2 · 2018: 1 · 2016: 5 · 2015: 3 · 2013: 4 · 2012: 1
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Adaptive Control and Signal Processing (publishedVersion)
- HAL (submittedVersion)
- Topics
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- false alarm filtering, trend estimation, physiological signals, patient monitoring, intensive care unit, fuzzy decision making, respiratory state, backward analysis
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