Identification of asthma severity levels through wheeze sound characterization and classification using integrated power features

Nabi, Fizza Ghulam; Sundaraj, Kenneth; Lam, Chee Kiang

doi:10.1016/j.bspc.2019.04.018

Cited by 24 publications

(8 citation statements)

References 51 publications

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“…In special cases, wheeze may even absent in asthma patients. The data collection was made entirely from asthmatic subjects, however, the same adventitious sound is associated with COPD subjects which can affect the system accuracy in practice [ 23 , 24 ]. In another research article, a COPD diagnosis technique was based on the transfer learning (TL) approach referred to as a balanced probability distribution (BPD).…”

Section: Literature Reviewmentioning

confidence: 99%

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Naqvi

Choudhry

2020

Sensors

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Chronic obstructive pulmonary disease (COPD) and pneumonia are two of the few fatal lung diseases which share common adventitious lung sounds. Diagnosing the disease from lung sound analysis to design a noninvasive technique for telemedicine is a challenging task. A novel framework is presented to perform a diagnosis of COPD and Pneumonia via application of the signal processing and machine learning approach. This model will help the pulmonologist to accurately detect disease A and B. COPD, normal and pneumonia lung sound (LS) data from the ICBHI respiratory database is used in this research. The performance analysis is evidence of the improved performance of the quadratic discriminate classifier with an accuracy of 99.70% on selected fused features after experimentation. The fusion of time domain, cepstral, and spectral features are employed. Feature selection for fusion is performed through the back-elimination method whereas empirical mode decomposition (EMD) and discrete wavelet transform (DWT)-based techniques are used to denoise and segment the pulmonic signal. Class imbalance is catered with the implementation of the adaptive synthetic (ADASYN) sampling technique.

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Section: Literature Reviewmentioning

confidence: 99%

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Naqvi

Choudhry

2020

Sensors

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“…Power spectral density, Eigen values, power spectral density of univariate and multivariate auto regressive (AR) models [13] are applied to the supervised neural networks to do the binary classification of lung diseases against healthy subjects. Severity of the respiratory disease asthma is assessed based on Integrated power and spectral features with KNN, SVM and ensemble classifiers [14,15] by using wheezing sounds. Pulmonary disease [16] is assessed based on lung acoustic signals.…”

Section: Introductionmentioning

confidence: 99%

Real Time Implementation of Robust Sound based Respiratory Disease Classification using Spectrogram and Deep Convolutional Neural Networks

Revathi¹,

et al. 2023

International Journal of Biology and Biomedical Engineering

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Respiratory diseases become burden to affect health of the people and five lung related diseases namely COPD, Asthma, Tuberculosis, Lower respiratory tract infection and Lung cancer are leading causes of death worldwide. X-ray or CT scan images of lungs of patients are analysed for prediction of any lung related respiratory diseases clinically. Respiratory sounds also can be analysed to diagnose the respiratory illness prevailing among humans. Sound based respiratory disease classification against healthy subjects is done by extracting spectrogram from the respiratory sound signal and Convolutional neural network (CNN) templates are created by applying the extracted features on the layered CNN architecture. Test sound is classified to be associated with respiratory disease or healthy subjects by applying the testing procedure on the test feature frames of spectrogram. Evaluation of the respiratory disease binary classification is performed by considering 80% and 20% of the extracted spectrogram features for training and testing. An automated system is developed to classify the respiratory diseases namely upper respiratory tract infection (URTI), pneumonia, bronchitis, bronchiectasis, and coronary obstructive pulmonary disease (COPD) against healthy subjects from breathing & wheezing sounds. Decision level fusion of spectrogram, Melspectrogram and Gammatone gram features with CNN for modelling & classification is done and the system has deliberated the accuracy of 98%. Combination of Gammatone gram and CNN has provided very good results for binary classification of pulmonary diseases against healthy subjects. This system is realized in real time by using Raspberry Pi hardware and this system provides the validation error of 14%. This automated system would be useful for COVID testing using breathing sounds if respiratory sound database with breathing sound recordings from COVID patients would be available.

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“…This significantly diminishes the effectiveness of auscultation and diagnosis. In recent years, research on classification algorithms for lung sounds has increased [3][4] [5] [6]. However, the primary challenge faced in current lung sound recognition research is that traditional classification methods struggle to extract crucial information from lung sound features, resulting in suboptimal recognition performance.…”

Section: Introductionmentioning

confidence: 99%

Research on Heart and Lung Sound Separation Method Based on DAE-NMF-VMD

sun,

CHEN,

ZHANG

et al. 2023

Preprint

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Auscultation is the most effective method for diagnosing cardiovascular and respiratory diseases. However, stethoscopes typically capture mixed signals of heart and lung sounds, which can affect the auscultation effect of doctors.Therefore, the efficient separation of mixed heart and lung sound signals plays a crucial role in improving the diagnosis of cardiovascular and respiratory diseases. In this paper, we propose a blind source separation method for heart and lung sounds based on Deep Autoencoder (DAE), Non-Negative Matrix Factorization (NMF), and Variational Mode Decomposition (VMD).Firstly, DAE is employed to extract highly informative features from the heart and lung sound signals. Subsequently, NMF clustering is applied to group the heart and lung sounds based on their distinct periodicities, achieving the separation of the mixed heart and lung sounds. Finally, Variational Mode Decomposition is used for denoising the separated signals. Experimental results demonstrate that the proposed method effectively separates heart and lung sound signals and exhibits significant advantages in terms of standardized evaluation metrics when compared to Non-Negative Matrix Factorization methods and DAE-NMF algorithms without denoising.

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Identification of asthma severity levels through wheeze sound characterization and classification using integrated power features

Cited by 24 publications

References 51 publications

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Real Time Implementation of Robust Sound based Respiratory Disease Classification using Spectrogram and Deep Convolutional Neural Networks

Research on Heart and Lung Sound Separation Method Based on DAE-NMF-VMD

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