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Extraction of low-dimensional features for single-channel common lung sound classification.

In this study, feature extraction methods used in the classification of single-channel lung sounds obtained by automatic identification of respiratory cycles were examined in detail in order to extract distinctive features at the lowest size. In this way, it will be possible to design a system for t...

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Published in:Medical & Biological Engineering & Computing Vol. 60; no. 6; pp. 1555 - 1569
Main Authors: Engin, M. Alptekin, Aras, Selim, Gangal, Ali
Format: Journal Article
Published: Springer Nature Jun2022
Online Access:View this record in EBSCOhost
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      jtl: Medical & Biological Engineering & Computing
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      dt: Jun2022
      vid: 60
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      pub: Springer Nature
      place: New York, New York
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        atl: Extraction of low-dimensional features for single-channel common lung sound classification.
      aug:
        au:
          Engin, M. Alptekin
          Aras, Selim
          Gangal, Ali
        affil: Department of Electrical and Electronics Engineering, Bayburt University, 69000, Bayburt, Turkey
      sug:
        subj:
          Respiratory Sounds
          Discriminant Analysis
          Algorithms
          Probability
          Scales
      ab: In this study, feature extraction methods used in the classification of single-channel lung sounds obtained by automatic identification of respiratory cycles were examined in detail in order to extract distinctive features at the lowest size. In this way, it will be possible to design a system for the detection of lung diseases, completely autonomously. In the study, automatic separation and classification of 400 respiratory cycles were performed from the single-channel common lung sounds obtained from 94 people. Leave one out cross validation (LOOCV) was used for the calibration and validation of the classification model. The Mel frequency cepstrum coefficients (MFCC), time domain features, frequency domain features, and linear predictive coding (LPC) were used for classification. The performance of the features was tested using linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), and naive Bayes (NB) classification algorithms. The success of combinations of features was explored and enhanced using the sequential forward selection (SFS). As a result, the best accuracy (90.14% in the training set and 90.63% in the test set) was acquired using the k-NN for the triple combination, which included the standard deviation of LPC and the standard deviation and the mean of MFCC.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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