Multimedia Respiratory Database (RespiratoryDatabase@TR): Auscultation Sounds and Chest X-rays

Altan, Gökhan; Kutlu, Yakup; Garbi, Yusuf; Pekmezci, Adnan Özhan; Nural, Serkan

doi:10.28978/nesciences.349282

Cited by 27 publications

(23 citation statements)

References 16 publications

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“…As a public multimedia respiratory database, RespiratoryDatabase@TR contains 12-channel lung sounds, 4-channel heart sounds, spirometry metrics, and chest X-rays for each subject [ 25 ]. The respiratory data were obtained by two pulmonologist clinicians using a Littmann3200 digital stethoscope to record the left (L) and right (R) channels in each lung region simultaneously.…”

Section: Methodsmentioning

confidence: 99%

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Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

Zhao

Liu

et al. 2021

BMC Pulm Med

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Background Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people’s health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice. Results This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert–Huang transform. And then, channels and features were screened by the reliefF algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively. Conclusion This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.

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Section: Methodsmentioning

confidence: 99%

“…The distribution of COPD patients by gender is shown in Table 1 . Subjects had never used cigarettes or tobacco products, and none of their close relatives had asthma or COPD [ 25 ]. At the start of each recording, subjects were required to cough for the signal synchronization of each lung region’s left and right channels.…”

Section: Methodsmentioning

confidence: 99%

Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

Zhao

Liu

et al. 2021

BMC Pulm Med

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“…RespiratoryDatabase@TR is a highly versatile and major premise database, which focuses on chronic and common respiratory disease COPD. It includes 12-channel lung sounds, 4-channel heart sounds, spirometry metrics, and chest X-rays for each subject [20]. RespiratoryDatabase@TR has an ethical committee approval confirmed by Mustafa Kemal University, Turkey (06.…”

Section: Databasementioning

confidence: 99%

“…Therefore, no patient was excluded from the population by reasons of chronic conditions other than lung diseases such as asthma, chronic bronchitis, lower respiratory tract infection, etc. The demographic information, age, sex, and auscultation scenario are detailed in [20]. Lung sounds from different auscultation regions have different characteristics depending on the density of the obstructions and thickness of the tissue.…”

Section: Databasementioning

confidence: 99%

Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds

Altan¹,

Kutlu²,

Gökçen³

2020

Turk J Elec Eng & Comp Sci

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Chronic obstructive pulmonary disease (COPD) is one of the deadliest diseases which cannot be treated but can be kept under control in certain stages. COPD has five severities, including at-risk, mild, moderate, severe, and very severe stages. Diagnosis of COPD at early stages needs additional clinical tests for even experienced specialists. The study aims at detecting the severity of the COPD to start treatment for preventing the progression of the disease to the next levels. We analyzed 12-channel lung sounds with different COPD severities from RespiratoryDatabase@TR. The lung sounds were recorded from the clinical auscultation points from 41 patients on posterior (chest) and anterior (back) sides. 3D second-order difference plot was applied to extract characteristic abnormalities on lung sounds. Cuboid and octant-based quantizations were utilized to extract characteristic abnormalities on chaos plot. Deep extreme learning machines classifier (deep ELM), which is one of the most stable and fast deep learning algorithms, was utilized in the classification stage. Novel HessELM and LuELM autoencoder kernels were adapted to deep ELM and reached higher generalization capabilities with a faster training speed against the conventional ELM autoencoder. The proposed deep ELM model with LuELM autoecoder has separated five COPD severities with classification performance rates of 94.31%, 94.28%, 98.76%, and 0.9659 for overall accuracy, weighted-sensitivity, weighted-specificity, and area under the curve (AUC) value, respectively. The proposed deep analysis of 12-channel lung sounds provides a standardized and entire lung assessment for identification of COPD severity. Our study is a pioneering approach that directly focuses on lung sounds. Novel deep ELM kernels have performed a higher generalization and fast training compared to conventional kernels.

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“…Gorkem et al used a support vector machine (SVM), the k-nearest neighbor approach, and a multilayer perceptron to detect pulmonary crackles 24 . Gokhan et al proposed the automatic detection of the respiratory cycle and collected synchronous auscultation sounds from COPD patients 28 , 31 , 32 . Interestingly, they demonstrated that deep learning is useful for diagnosing COPD and classifying the severity of COPD with significantly high-performance rates 28 , 29 .…”

Section: Introductionmentioning

confidence: 99%

Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning

Kim

Hyon

Jung

et al. 2021

Sci Rep

107

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Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician’s considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases.

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Multimedia Respiratory Database (RespiratoryDatabase@TR): Auscultation Sounds and Chest X-rays

Cited by 27 publications

References 16 publications

Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds

Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning

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