Title :
Ambient context in breathing signal fusion
Author :
Holtzman, M. ; Knoefel, Frank
Author_Institution :
Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, ON, Canada
Abstract :
Fusion of ambient breathing signals can be hampered by the current environmental conditions. Fusion methods that are resistant to most adverse ambient conditions may still be susceptible to some. The diversity of multiple fusion methods can be leveraged by employing a variety of fusion methods to fuse each breathing epoch. By using a trained linear classifier to select the best candidate fusion method according to current condition estimates, a more robust system is proposed. Regularization by feature selection and method selection were addressed. The final system used two fusion methods and ten ambient features to outperform the single best fusion method. The system increased the percentage of epochs with good signal quality by 5%. Analysis of optimal fusion method selection indicates that further improvement is also possible.
Keywords :
feature selection; medical signal processing; pneumodynamics; signal classification; ambient context; breathing signal fusion; feature selection; regularization; signal quality; trained linear classifier; Accuracy; Arrays; Diversity reception; Feature extraction; Monitoring; Noise; Training; data fusion; linear diversity combining; pressure sensor array; respiratory signal; unobtrusive monitoring;
Conference_Titel :
Medical Measurements and Applications (MeMeA), 2014 IEEE International Symposium on
Conference_Location :
Lisboa
Print_ISBN :
978-1-4799-2920-7
DOI :
10.1109/MeMeA.2014.6860111