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

Abstract

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.

Performance Evaluation of Capsule Networks for Classification of Plant Leaf Diseases

“…The test results were evaluated using independent test characteristics. Accuracy, sensitivity, and specificity were calculated using confusion matrix of the trained CapsNet model [27].…”

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

“…First, they combined the three-dimensional feature-extraction technique with DBN to classify COPD0 and COPD4 [ 19 ]. After that, they used 3D second-order difference plot to extract characteristic abnormalities on lung sounds and then used the deep extreme learning machines classifier to complete the five classifications of COPD severity [ 21 ]. Compared with them, our research has the advantage of being more suitable for clinical needs and establishing a channel selection model based on the reliefF algorithm.…”

“…Based on the statistical characteristics of HHT of lung sounds, Altan et al used DBN to separate COPD patients from healthy subjects with an accuracy of 93.67% [ 20 ]. Altan et al used the 3D second-order difference plot to extract characteristic abnormalities on lung sounds and then used the deep extreme learning machines classifier to classify the severity of COPD, with an accuracy of 94.31% [ 21 ]. Ahmet extracted the features of lung sounds through empirical wavelet transform and then input them into many models to distinguish COPD patients from healthy subjects [ 22 ].…”

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

“…There are several studies that tried to automatically analyze and classify respiratory sounds 15 , 22 – 30 . An interesting study quantified and characterized lung sounds in patients with pneumonia for generating acoustic pneumonia scores 22 .…”