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    Differential Diagnosis of Asthma and COPD Based on Multivariate Pulmonary Sounds Analysis
    (IEEE-Inst Electrical Electronics Engineers Inc, 2021) Sen, Ipek; Saraclar, Murat; Kahya, Yasemin P.
    Objective: Asthma and chronic obstructive pulmonary disease (COPD) can be confused in clinical diagnosis due to overlapping symptoms. The purpose of this study is to develop a method based on multivariate pulmonary sounds analysis for differential diagnosis of the two diseases. Methods: The recorded 14-channel pulmonary sound data are mathematically modeled using multivariate (or, vector) autoregressive (VAR) model, and the model parameters are fed to the classifier. Separate classifiers are assumed for each of the six sub-phases of flow cycle, namely, early/mid/late inspiration and expiration, and the six decisions are combined to reach the final decision. Parameter classification is performed in the Bayesian framework with the assumption of Gaussian mixture model (GMM) for the likelihoods, and the six sub-phase decisions are combined by voting, where the weights are learned by a linear support vector machine (SVM) classifier. Fifty subjects are incorporated in the study, 30 being diagnosed with asthma and 20 with COPD. Results: The highest accuracy of the classifier is 98 percent, corresponding to correct classification rates of 100 and 95 percent for asthma and COPD, respectively. The prominent sub-phase to differentiate between the two diseases is found to be mid-inspiration. Conclusion: The methodology proves to be promising in terms of asthma-COPD differentiation based on acoustic information. The results also reveal that the six sub-phases are not equally pertinent in the differentiation. Significance: Pulmonary sounds analysis may be a complementary tool in clinical practice for differential diagnosis of asthma and COPD, especially in the absence of reliable spirometric testing.
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    Effect of Smoking on Pulmonary Acoustic Parameters in Terms of Displacement Away From Non-smokers Towards COPD: A Preliminary Study
    (IEEE, 2022) Sen, Ipek; Wali, Amrou; Hassan, Salma Ahmed
    In this study, the aim is to understand what happens to the pulmonary acoustic features due to smoking, with a particular interest on the distances to the non-smoker and chronic obstructive pulmonary disease (COPD) groups in the feature space. Three new measures are defined, the first two of which are to quantify the differences between the three groups and to sort the features with respect to their discriminative abilities. The third measure is defined to quantify the linear displacement of smokers' group away from the non-smokers towards COPD in the multivariate feature space formed by the selected features in the previous step. Among the features adopted, percentile frequencies f(50) and f(75), and the eighth mel-frequency cepstral coefficient (MFCC8) are selected by the first two measures in common. Using this set of features, it is found that early-inspiration, early-expiration, and the seventh microphone location are more useful to detect early signs of COPD. Recommending a reliable methodology for early detection of COPD in this manner requires increasing the data size, devising other measures, running additional tests, and interpreting the results clinically.
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    Feature Selection for Differential Diagnosis of Asthma and COPD: A Preliminary Study
    (IEEE, 2023) Sargin, Serhat Ismet; Sen, Ipek
    Asthma and chronic obstructive pulmonary disease (COPD) are two obstructive pulmonary diseases whose differential diagnosis is difficult due to overlapping symptoms and inadequacy of classical methods. The main aim of this study is to understand which combination of acoustic properties is more discriminative, and to develop a new method that can be used in clinical practice. Accordingly, a total of 26 features under eight different feature types have been calculated using pulmonary sounds acquired from 50 volunteers diagnosed with asthma (30 subjects) and 20 copd (20 subjects), and forward sequential feature selection has been performed using k-nearest neighbor (k-NN) classifier as it was observed to have better performance than Bayesian classifier on the singular features extracted. Consequently, the feature set composed of F-max, BIN5, AR(3), P-max, and F-95 has been selected to be the best feature set by the k-NN classifier with 96% accuracy in asthma and COPD discrimination. To develop a more reliable method for the differential diagnosis of asthma and COPD, the feature set should be augmented and different types of classifiers should also be used.