Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD)

Haider, Nishi Shahnaj; Behera, Ajoy Kumar

doi:10.1016/j.bbe.2021.12.004

Cited by 20 publications

(7 citation statements)

References 58 publications

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“…The study aimed to achieve a high level of accuracy and successfully reached an accuracy of 95.28%. Haider and Behera [12] conducted a study focused on denoising LSs using a combination of EMD, hurst analysis, spectral subtraction denoising, and wavelet packet decomposition to extract wavelet-based features. A decision tree (DT) classifier with wavelet features is employed to classify normal, COPD, and asthma conditions.…”

Section: Literature Surveymentioning

confidence: 99%

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Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

Sangeetha Balasubramanian,

Periyasamy Rajadurai

2023

Int. j. eng. technol. innov.

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The study presents a computer-based automated system that employs machine learning to classify pulmonary diseases using lung sound data collected from hospitals. Denoising techniques, such as discrete wavelet transform and variational mode decomposition, are applied to enhance classifier performance. The system combines cepstral features, such as Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, for classification. Four machine learning classifiers, namely the decision tree, k-nearest neighbor, linear discriminant analysis, and random forest, are compared. Evaluation metrics such as accuracy, recall, specificity, and f1 score are employed. This study includes patients affected by chronic obstructive pulmonary disease, asthma, bronchiectasis, and healthy individuals. The results demonstrate that the random forest classifier outperforms the others, achieving an accuracy of 99.72% along with 100% recall, specificity, and f1 scores. The study suggests that the computer-based system serves as a decision-making tool for classifying pulmonary diseases, especially in resource-limited settings.

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Section: Literature Surveymentioning

confidence: 99%

“…A supervised learning approach called LDA is used in ML for classification applications [12]. It is a method for determining the optimum linear combination of features for classifying a dataset into different classes.…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

Sangeetha Balasubramanian,

Periyasamy Rajadurai

2023

Int. j. eng. technol. innov.

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“…Various machine learning models have been proposed to detect asthma automatically using recorded respiratory sounds. Haider and Behera [17] developed an automated method for detecting asthma and chronic obstructive pulmonary disease based on Hurst analysis, empirical mode decomposition, and spectral subtraction methods. Trained and tested on a dataset of lung sounds acquired from 80 normal, 80 asthmatic, and 80 chronic obstructive pulmonary disease subjects, the model attained 99.30% accuracy using a decision tree classifier.…”

Section: Introductionmentioning

confidence: 99%

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

Barua,

Keles,

Kuluozturk

et al. 2024

Neural Comput & Applic

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Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening.

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“…In the literature, certain studies concentrate on identifying wheeze and crackle sounds in pulmonary records, often associated with unhealthy cases [11][12][13][14][15][16]. Other studies explore the classification of prevalent lung diseases such as pneumonia, asthma, and COPD [16][17][18][19][20][21][22][23]. Below, we provide an overview of both types of studies.…”

Section: Introductionmentioning

confidence: 99%

A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases

Kababulut,

Gürkan Kuntalp,

Düzyel

et al. 2023

Diagnostics

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The aim of this study is to propose a new feature selection method based on the class-based contribution of Shapley values. For this purpose, a clinical decision support system was developed to assist doctors in their diagnosis of lung diseases from lung sounds. The developed systems, which are based on the Decision Tree Algorithm (DTA), create a classification for five different cases: healthy and disease (URTI, COPD, Pneumonia, and Bronchiolitis) states. The most important reason for using a Decision Tree Classifier instead of other high-performance classifiers such as CNN and RNN is that the class contributions of Shapley values can be seen with this classifier. The systems developed consist of either a single DTA classifier or five parallel DTA classifiers each of which is optimized to make a binary classification such as healthy vs. others, COPD vs. Others, etc. Feature sets based on Power Spectral Density (PSD), Mel Frequency Cepstral Coefficients (MFCC), and statistical characteristics extracted from lung sound recordings were used in these classifications. The results indicate that employing features selected based on the class-based contribution of Shapley values, along with utilizing an ensemble (parallel) system, leads to improved classification performance compared to performances using either raw features alone or traditional use of Shapley values.

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Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD)

Cited by 20 publications

References 58 publications

Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases

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