Characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features

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

doi:10.1016/j.compbiomed.2018.10.035

Cited by 35 publications

(8 citation statements)

References 49 publications

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“…In a similar study, the correlation of wheeze sounds with asthmatic severity was analyzed in 55 asthmatic patients using three ML algorithms, including the ensemble, support vector machine and k-nearest neighbor. The ensemble algorithm showed better performance, and the wheeze sound was identified as a sensitive and specific predictor of asthma severity 52 .…”

Section: Ai/ml and Asthmamentioning

confidence: 99%

“…Despite these current limitations, AI/ML techniques are needed in the medical field due to the special ability to efficiently analyze and integrate large and heterogeneous data. [27, 28,52], [67,78,79], [94,102,104] Random forest Random forest is an ensemble learning method. It contains multiple decision trees and integrates these decision trees to category of data.…”

Section: General Concepts Terminologies and Limitations Of Ai/mlmentioning

confidence: 99%

“…However, selecting the optimal kernel function is essential. [28,32,35], [52,59,[66][67][68], [75,78,79], [95,99,102], [103,104,108] Artificial neural network This is a kind of hierarchical nonlinear mapping network based on neurons and activation functions. Its structure includes three main parts, namely input layer, hidden layer and output layer.…”

Section: General Concepts Terminologies and Limitations Of Ai/mlmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

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Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

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Section: Ai/ml and Asthmamentioning

confidence: 99%

Section: General Concepts Terminologies and Limitations Of Ai/mlmentioning

confidence: 99%

Section: General Concepts Terminologies and Limitations Of Ai/mlmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

View full text Add to dashboard Cite

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“…Overall best results gained by these methods for the first, moderate, and the last stage were 95, 88, and 90%, respectively. Still, that method identifies only the wheezing sounds (not other abnormal RS subclasses) and lacks focus on better representation of patterns, e.g., frequency and phase ( 28 ). An Overview of deep learning was done for radiology for disease detection, classification, quantification, and segmentation.…”

Section: Related Workmentioning

confidence: 99%

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

et al. 2021

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Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

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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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Characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features

Cited by 35 publications

References 49 publications

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

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

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