ArticleSensors (Basel, Switzerland)2020
An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Hybrid deep learning model for multimodal vocal and lung signal analysis in health monitoring.Scientific reports · 2026Article
- A Novel AAF-SwinT Model for Automatic Recognition of Abnormal Goat Lung Sounds.Animals : an open access journal from MDPI · 2026Article
- Short-Term Prognosis in Acute Asthma Exacerbations: A Comparative Evaluation of Machine Learning Models Using Spirometry and Tracheal Respiratory Sound Analysis.International journal of general medicine · 2026Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.Scientific reports · 2025Article
- Characteristics of lung sounds in early infants using automated analysis.European journal of pediatrics · 2025Article
- Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease.Scientific reports · 2025Observational
- Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.Physiological measurement · 2025Article
- Explainable federated learning scheme for secure healthcare data sharing.Health information science and systems · 2024Article
- A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Wheeze and Crackle Discrimination Algorithm in Pneumonia Respiratory Signals.Conference proceedings. IEEE Colombian Conference on Communications and Computing · 2024Article
- Review
- Computerized respiratory sound based diagnosis of pneumonia.Medical & biological engineering & computing · 2024Article
- A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases.Diagnostics (Basel, Switzerland) · 2023Article
- Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Article
- Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging.Sensors (Basel, Switzerland) · 2023Review
- Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.