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DC Field | Value | Language |
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dc.contributor.author | Mondal, A. | |
dc.contributor.author | Tang, H. | |
dc.date.accessioned | 2020-03-30T10:23:05Z | - |
dc.date.available | 2020-03-30T10:23:05Z | - |
dc.date.issued | 2017 | |
dc.identifier.citation | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, Vol., , pp.2952-2955 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/8944 | - |
dc.description.abstract | In 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.title | Respiratory sounds classification using statistical biomarker | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | 2. Conference Papers |
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