A lightweight anomaly detection framework for medical wireless sensor networks
Abstract
In this paper, we focus on online detection and isolation of erroneous values reported by medical wireless sensors. We propose a lightweight approach for online anomaly detection in collected data, able to raise alarms only when patients enter in emergency situation and to discard faulty measurements. The proposed approach is based on Haar wavelet decomposition and Hampel filter for spatial analysis, and on boxplot for temporal analysis. Our objective is to reduce false alarms resulted from unreliable measurements. We apply our proposed approach on real physiological data set. Our experimental results prove the effectiveness of our approach to achieve good detection accuracy with low false alarm rate.
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Several studies in this area have used stored clinical data sets.
“several studies in this area have used the stored clinical data sets [31,89].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [89]
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
- Authors
- Salem, O., Liu, Y., & Mehaoua, A.
- Affiliations
- Université Paris Cité (FR)
- Pohang University of Science and Technology (KR)
- Venue
- 2013 IEEE Wireless Communications and Networking Conference (WCNC)
- Publisher
- IEEE
- Published
- 2013-04
- Pages
- 4358-4363
- ISSN
- 1525-3511 (ISSN-L)
- OpenAlex
- W1970384124
- Cited by
- 26
- References
- 28
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 2 · 2022: 1 · 2020: 2 · 2019: 3 · 2018: 2 · 2017: 1 · 2016: 2 · 2015: 4 · 2014: 3 · 2013: 2
- FWCI
- 2.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Wireless Communications and Networking Conference (publishedVersion)
- Funders
- Korea Science and Engineering Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- anomaly detection, online detection, Haar wavelet decomposition, Hampel filter, box plots, false alarm reduction, physiological signals, emergency alarm
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