Mondal, A.Tang, H.2026-02-062017Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, Vol., , p. 2952-295505891019; 1557170Xhttps://doi.org/10.1109/EMBC.2017.8037476https://idr.nitk.ac.in/handle/123456789/31778In this paper, we have proposed a new feature extraction technique based on statistical morphology of lung sound signal (LS). This work attempts to (i) generate certain intrinsic mode functions (IMFs), (ii) select a set of informative IMFs and (iii) extract relevant features from the selected IMFs and residue. Feature vector is formed by using the higher order moments: mean, standard deviation, skewness and kurtosis and employed as input to the classifier models for classification of three types of LS signals: crackle, wheeze and normal. The efficiency of these features is examined with an artificial neural network (ANN) classifier and compared the results with three baseline methods. The proposed method gives a superior performance in term of classification accuracy, sensitivity and specificity. © 2017 IEEE.