Event Detection in Wireless Body Area Networks Using Kalman Filter and Power Divergence

• Published in: IEEE eTransactions on network and service management Vol. 15; no. 3; pp. 1018 - 1034
• Main Authors: Salem, Osman, Serhrouchni, Ahmed, Mehaoua, Ahmed, Boutaba, Raouf
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alarm systems; Alarms; anomaly detection; Biomedical data; Biomedical monitoring; Biosensors; Body area networks; Change detection; Computer Science; Correlation; Correlation analysis; Divergence; False alarms; fault detection; Kalman filter; Kalman filters; Medical services; Monitoring; Physiology; power divergence; reliability; Sensors; Wireless body area networks; Wireless sensor networks; Wireless body area networks; anomaly detection; power divergence; Kalman filter; reliability; fault detection
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2018.2842195

The collected data by biomedical sensors must be analyzed for automatic detection of physiological changes. The early identification of an event in collected data is required to trigger an alarm upon detection of patient health degradation. Such alarms inform healthcare professionals and allow them to quickly react by taking appropriate actions. However, events result from physiological change or faulty measurements, and lead to false alarms and unnecessary medical intervention. In this paper, we propose a framework for automatic detection of events from collected data by biomedical sensors. The proposed approach is based on the Kalman filter to forecast the current measurement and to derive the baseline of the time series. The power divergence is used to measure the distance between the forecasted and measured values. When a change occurs, this metric significantly deviates from past values. To distinguish emergency events from faulty measurements, we exploit the spatial correlation between the monitored attributes. We conduct experiments on real physiological data set and our results show that our proposed framework achieves a good detection accuracy with a low false alarm rate. Its simplicity and processing speed make our proposed framework efficient and effective for real-world deployment.