pp. 57-73
S&M869 Research Paper https://doi.org/10.18494/SAM.2012.734 Published: February 16, 2012 Feature Extraction from Sensor Data for Detection of Wound Pathogen Based on Electronic Nose [PDF] Jia Yan, Fengchun Tian, Qinghua He, Yue Shen, Shan Xu, Jingwei Feng and Kadri Chaibou (Received November 2, 2010; Accepted March 23, 2011) Keywords: electronic nose, wound infection, feature extraction, curve fitting
Three feature extraction methods, extraction from original response curves of sensors, curve fitting parameters and transform domains, for pathogen detection based on an electronic nose have been discussed. By using the integrals, coefficients of exponential fitting with two parameters and hyperbolic tangent function fitting, Fourier coefficients, and wavelet coefficients as features, 100% identification accuracy with the radical basis function neural network (RBFNN) classifier was reached for the seven pathogens. Theoretical analysis and experimental results indicate that the methods based on dynamic response are better than those based on steady response and can provide accurate identification of common pathogens present in wound infection.
Corresponding author: Jia YanCite this article Jia Yan, Fengchun Tian, Qinghua He, Yue Shen, Shan Xu, Jingwei Feng and Kadri Chaibou, Feature Extraction from Sensor Data for Detection of Wound Pathogen Based on Electronic Nose, Sens. Mater., Vol. 24, No. 2, 2012, p. 57-73. |