A novel feature extraction technique for pulmonary sound analysis based on EMD. (June 2018)

• Record Type: Journal Article
• Authors: Mondal, Ashok; Banerjee, Poulami; Tang, Hong
• Is Part Of: Computer methods and programs in biomedicine. Volume 159(2018)
• Page Start: 199
• Page End: 209
• Publication Date: 2018-06
• Subjects: Empirical mode decomposition -- Lung sound -- Multilayer perceptron -- Pulmonary diseases; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.03.016
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A new feature extraction technique is proposed based on EMD framework and statistical parameters. One complete breathing cycle is calculated using Hilbert Transform approach. The effectiveness of the proposed features are compared with others features including WT, MFCC and SSA. The proposed method gives a superior performance than the baseline methods. Abstract: Background and objective : The stethoscope based auscultation technique is a primary diagnostic tool for chest sound analysis. However, the performance of this method is limited due to its dependency on physicians experience, knowledge and also clarity of the signal. To overcome this problem we need an automated computer-aided diagnostic system that will be competent in noisy environment. In this paper, a novel feature extraction technique is introduced for discriminating various pulmonary dysfunctions in an automated way based on pattern recognition algorithms. Method : In this work, the disease correlated relevant characteristics of lung sounds signals are identified in terms of statistical distribution parameters: mean, variance, skewness, and kurtosis. These features are extracted from selective morphological components of the mapped signal in the empirical mode decomposition domain. The feature set is fed to the classifier model to differentiate their corresponding classes. Results : The significance of features developed are validated by conducting several experiments using supervised and unsupervised classifiers. Furthermore, the discriminating power of the proposed features is compared with three types of baseline features. The experimental result is evaluated by statistical analysis and also validated with physicians inference. Conclusions : It is found that the proposed features extraction technique is superior to the baseline methods in terms of classification accuracy, sensitivity and specificity. The developed method gives better results compared to baseline methods in any circumstance. The proposed method gives a higher accuracy of 94.16, sensitivity of 100 and specificity of 93.75 for an artificial neural network classifier.