Sparse representation-based classification for breath sample identification

It has been discovered that some compounds in human breath can be used to detect some diseases and monitor the development of the conditions. A sensor system in tandem with certain data evaluation algorithm offers an approach to analyze the compositions of breath. Currently, most algorithms rely on the generally designed pattern recognition techniques rather than considering the specific characteristics of data. They may not be suitable for odor signal identification. This paper proposes a Sparse Representation-based Classification (SRC) method for breath sample identification. The sparse representation expresses an input signal as the linear combination of a small number of the training signals, which are from the same category as the input signal. The selection of a proper set of training signals in representation, therefore, gives us useful cues for classification. Two experiments were conducted to evaluate the proposed method. The first one was to distinguish diabetes samples from healthy ones. The second one aimed to classify these diseased samples into different groups, each standing for one blood glucose level. To illustrate the robustness of this method, two different feature sets, namely, geometry features and principle components were employed. Experimental results show that the proposed SRC outperforms other common methods, such as K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), irrespective of the features selected.