Welcome to the InfoSci Platform
Reference Hub8
Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

Osman Salem, Alexey Guerassimov, Ahmed Mehaoua, Anthony Marcus, Borko Furht
Copyright: © 2016 |Pages: 21
ISBN13: 9781466687561|ISBN10: 1466687568|EISBN13: 9781466687578
DOI: 10.4018/978-1-4666-8756-1.ch024
Cite Chapter Cite Chapter

MLA

Salem, Osman, et al. "Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models." E-Health and Telemedicine: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 466-486. https://doi.org/10.4018/978-1-4666-8756-1.ch024

APA

Salem, O., Guerassimov, A., Mehaoua, A., Marcus, A., & Furht, B. (2016). Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models. In I. Management Association (Ed.), E-Health and Telemedicine: Concepts, Methodologies, Tools, and Applications (pp. 466-486). IGI Global. https://doi.org/10.4018/978-1-4666-8756-1.ch024

Chicago

Salem, Osman, et al. "Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models." In E-Health and Telemedicine: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 466-486. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8756-1.ch024

Export Reference

Mendeley
Favorite

Abstract

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.