Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

Garcí­a-Ordás, María Teresa; Bení­tez-Andrades, José Alberto; García-Rodríguez, Isaías; Benavides, Carmen; Aláiz‐Moretón, Héctor

doi:10.3390/s20041214

Cited by 92 publications

(59 citation statements)

References 26 publications

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“…( 2020 ) 126 6898 4: Normal, crackles, wheezes crackles+wheezes Spectrograms CNN Accuracy: 71.15 Sensitivity: 61.00 Specificity: 86.00 García-Ordás et al. ( 2020 ) 126 920 6: Normal, asthma, pneumonia BRON, COPD, respiratory tract infection Spectrograms CNN Accuracy: N/A Sensitivity: 98.81 Specificity: 98.61 This study 213 1,483 6: Normal, asthma, pneumonia BRON, COPD. heart failure Spatial and temporal (CNN + BDLSTM) CNN + BDLSTM Accuracy: 99.62 Sensitivity: 98.43 Specificity: 99.69 …”

Section: Resultsmentioning

confidence: 87%

Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory

Fraiwan

Alkhodari

et al. 2021

J Ambient Intell Human Comput

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In this paper, a study is conducted to explore the ability of deep learning in recognizing pulmonary diseases from electronically recorded lung sounds. The selected data-set included a total of 103 patients obtained from locally recorded stethoscope lung sounds acquired at King Abdullah University Hospital, Jordan University of Science and Technology, Jordan. In addition, 110 patients data were added to the data-set from the Int. Conf. on Biomedical Health Informatics publicly available challenge database. Initially, all signals were checked to have a sampling frequency of 4 kHz and segmented into 5 s segments. Then, several preprocessing steps were undertaken to ensure smoother and less noisy signals. These steps included wavelet smoothing, displacement artifact removal, and z -score normalization. The deep learning network architecture consisted of two stages; convolutional neural networks and bidirectional long short-term memory units. The training of the model was evaluated based on a k-fold cross-validation scheme of tenfolds using several performance evaluation metrics including Cohen’s kappa, accuracy, sensitivity, specificity, precision, and F1-score. The developed algorithm achieved the highest average accuracy of 99.62% with a precision of 98.85% in classifying patients based on the pulmonary disease types using CNN + BDLSTM. Furthermore, a total agreement of 98.26% was obtained between the predictions and original classes within the training scheme. This study paves the way towards implementing deep learning models in clinical settings to assist clinicians in decision making related to the recognition of pulmonary diseases. Supplementary Information The online version supplementary material available at 10.1007/s12652-021-03184-y.

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Section: Resultsmentioning

confidence: 87%

Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory

Fraiwan

Alkhodari

et al. 2021

J Ambient Intell Human Comput

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“…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%

“…But the research work only focused to classify the adventitious sounds found in various pulmonic illnesses [ 28 ]. In another research, a novel approach called variational convolutional autoencoder is presented for unbalanced data and implemented on the same database [ 29 ].…”

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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“…In the near future, electronic stethoscopes with automatic machine learning based classification and interpretation of lung sounds might be helpful in this respect. 30 Differentiation between coarse and fine and between early and late crackles can probably be taken into account in future devices and mHealth solutions, being thus more easily integrated in routine clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Inspiratory crackles—early and late—revisited: identifying COPD by crackle characteristics

et al. 2021

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BackgroundThe significance of pulmonary crackles, by their timing during inspiration, was described by Nath and Capel in 1974, with early crackles associated with bronchial obstruction and late crackles with restrictive defects. Crackles are also described as ‘fine’ or ‘coarse’. We aimed to evaluate the usefulness of crackle characteristics in the diagnosis of chronic obstructive pulmonary disease (COPD).MethodsIn a population-based study, lung sounds were recorded at six auscultation sites and classified in participants aged 40 years or older. Inspiratory crackles were classified as ‘early’ or ‘late and into the types’ ‘coarse’ and ‘fine’ by two observers. A diagnosis of COPD was based on respiratory symptoms and forced expiratory volume in 1 s/forced inspiratory vital capacity below lower limit of normal, based on Global Lung Function Initiative 2012 reference. Associations between crackle characteristics and COPD were analysed by logistic regression. Kappa statistics was applied for evaluating interobserver agreement.ResultsOf 3684 subjects included in the analysis, 52.9% were female, 50.1% were ≥65 years and 204 (5.5%) had COPD. Basal inspiratory crackles were heard in 306 participants by observer 1 and in 323 by observer 2. When heard bilaterally COPD could be predicted with ORs of 2.59 (95% CI 1.36 to 4.91) and 3.20 (95% CI 1.71 to 5.98), annotated by observer 1 and 2, respectively, adjusted for sex and age. If bilateral crackles were coarse the corresponding ORs were 2.65 (95% CI 1.28 to 5.49) and 3.67 (95% CI 1.58 to 8.52) and when heard early during inspiration the ORs were 6.88 (95% CI 2.59 to 18.29) and 7.63 (95%CI 3.73 to 15.62). The positive predictive value for COPD was 23% when early crackles were heard over one or both lungs. We observed higher kappa values when classifying timing than type.Conclusions‘Early’ inspiratory crackles predicted COPD more strongly than ‘coarse’ inspiratory crackles. Identification of early crackles at the lung bases should imply a strong attention to the possibility of COPD.

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Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

Cited by 92 publications

References 26 publications

Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory

Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory

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

Inspiratory crackles—early and late—revisited: identifying COPD by crackle characteristics

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