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International Journal of Telemedicine and Applications
Volume 2014 (2014), Article ID 816369, 8 pages
Research Article

Effect of Wireless Channels on Detection and Classification of Asthma Attacks in Wireless Remote Health Monitoring Systems

Yarmouk University, Irbid 21163, Jordan

Received 9 September 2013; Revised 22 December 2013; Accepted 26 December 2013; Published 10 February 2014

Academic Editor: Velio Macellari

Copyright © 2014 Orobah Al-Momani and Khaled M. Gharaibeh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper aims to study the performance of support vector machine (SVM) classification in detecting asthma attacks in a wireless remote monitoring scenario. The effect of wireless channels on decision making of the SVM classifier is studied in order to determine the channel conditions under which transmission is not recommended from a clinical point of view. The simulation results show that the performance of the SVM classification algorithm in detecting asthma attacks is highly influenced by the mobility of the user where Doppler effects are manifested. The results also show that SVM classifiers outperform other methods used for classification of cough signals such as the hidden markov model (HMM) based classifier specially when wireless channel impairments are considered.