Classification of Normal, Asthma and COPD Subjects Using Multichannel Lung Sound Signals

Islam, Md. Ariful; Bandyopadhyaya, Irin; Bhattacharyya, Parthasarathi; Saha, Goutam

doi:10.1109/iccsp.2018.8524439

Cited by 24 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The proposed technique has provided an efficient approach with outstanding classification accuracy. It has outperformed as compared to existing techniques on other multiple pulmonic pathologies from LS analysis due to its simple statistical features, low computation, and accuracy [ 6 , 7 , 12 , 20 , 22 , 24 , 25 , 28 , 29 ]. The performance analysis of the proposed diagnostic technique with existing pulmonic pathologies methods is shown in Table 9 .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Naqvi

Choudhry

2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The proposed method demonstrated the low specificity and sensitivity as compared to most of the latest research work and required authentication on real-time or authentic data. [ 22 ]. The intensity of asthma is measured via wheeze analysis.…”

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

View full text Add to dashboard Cite

show abstract

“…The most common approach is placing the device on the chest area, like a typical auscultation device. This type of data acquisition can be conducted by digital stethoscopes, and the authors in [ 47 , 52 , 53 , 56 , 58 , 59 , 60 , 61 , 62 , 63 , 66 ] utilized data which were derived from this kind of data retrieval methodology. Most of the studies mention that they did not execute the data acquisition phase themselves, but they exploited open-source or proprietary datasets.…”

Section: Lung and Breath Sounds Analysis Of Lower Respiratory Symptomsmentioning

confidence: 99%

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

Kapetanidis,

Kalioras,

Tsakonas

et al. 2024

Sensors

View full text Add to dashboard Cite

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases’ symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.

show abstract

“…However, a spirometer requires a skilled operator, is expensive, ineffective at detecting obstructive-restrictive defects, and necessitates a large number of patient breathing motions and forceful breathing. 12 An arterial blood gas analyzer (ABG) can also be used to assess lung problems using arterial blood gas exchange (pO2 and pCO2). 13 However, the ABG test is expensive and requires an invasive procedure.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the constraints of the manual diagnosis technique, many machine learning and deep learning approaches for automatic classification of lung disease based on lung sound signals have been developed in various literatures. 12 , 17–22 For instance, Islam et al, 12 proposed a Welch spectral method for estimation of power spectral density (PSD) of lung sounds and a support vector machine (SVM) for classification of lung diseases as normal, asthma, and COPD claiming an accuracy of 93.3%. Similarly, Haider et al, 20 proposed a respiratory sound-based classification of pulmonary diseases as normal or COPD using SVM and an accuracy of 83.6% was reported.…”

Section: Introductionmentioning

confidence: 99%

Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases

Tessema¹,

Nemomssa²,

Simegn³

2022

MDER

View full text Add to dashboard Cite

Purpose Lung diseases are the third leading cause of death worldwide. Stethoscope-based auscultation is the most commonly used, non-invasive, inexpensive, and primary diagnostic approach for assessing lung conditions. However, the manual auscultation-based diagnosis procedure is prone to error, and its accuracy is dependent on the physician’s experience and hearing capacity. Moreover, the stethoscope recording is vulnerable to different noises that can mask the important features of lung sounds which may lead to misdiagnosis. In this paper, a method for the acquisition of lung sound signals and classification of the top 7 lung diseases has been proposed for improving the efficacy of auscultation diagnosis of pulmonary disease. Methods An electronic stethoscope has been constructed for signal acquisition. Lung sound signals were then collected from people with COPD, upper respiratory tract infections (URTI), lower respiratory tract infections (LRTI), pneumonia, bronchiectasis, bronchiolitis, asthma, and healthy people. Lung sounds were analyzed using a wavelet multiresolution analysis. To choose the most relevant features, feature selection using one-way ANOVA was performed. The classification accuracy of various machine learning classifiers was compared, and the Fine Gaussian SVM was chosen for final classification due to its superior performance. Model optimization was accomplished through the application of Bayesian optimization techniques. Results A test classification accuracy of 99%, specificity of 99.2%, and sensitivity of 99.04%, have been achieved for the 7 lung diseases using the optimized Fine Gaussian SVM classifier. Conclusion Our experimental results demonstrate that the proposed method has the potential to be used as a decision support system for the classification of lung diseases, especially in those areas where the expertise and the means are limited.

show abstract

Classification of Normal, Asthma and COPD Subjects Using Multichannel Lung Sound Signals

Cited by 24 publications

References 15 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

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases

Contact Info

Product

Resources

About