Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/8944
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dc.contributor.authorMondal, A.
dc.contributor.authorTang, H.
dc.date.accessioned2020-03-30T10:23:05Z-
dc.date.available2020-03-30T10:23:05Z-
dc.date.issued2017
dc.identifier.citationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, Vol., , pp.2952-2955en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8944-
dc.description.abstractIn 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.en_US
dc.titleRespiratory sounds classification using statistical biomarkeren_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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