Respiratory disorders have become the third largest cause of death worldwide, which can be assessed by one of the two key diagnostic modalities: breathing patterns (BPs) or the airflow signals, and respiratory sounds (RSs). In recent years, a few research has been conducted on finding correlation between these two modalities which indicate the structural flaws of lungs under disease condition. In this letter, we propose 'RS-2-BP': a unified deep learning framework for deriving the electrical impedance tomography-based airflow signals from respiratory sounds using a hybrid neural network architecture, namely ReSTL, that comprises cascaded standard and residual shrinkage convolution blocks, followed by feature refined transformer encoders and long-short term memory (LSTM) units. The proposed framework is extensively evaluated using the publicly available BRACETS dataset. Experimental results suggest that our ReSTL can accurately derive the BPs from RSs with an average mean absolute error of 0.024±0.011, 0.485±0.118, 0.020±0.011, 0.134±0.068, and 0.031 ± 0.019, respectively for five different tasks. Further these derived BPs can be used for extracting different respiratory vitals, identifying disease conditions efficiently, and retrieving salient breathing cycle information from the RSs.

Objective: Respiratory diseases are the world’s third leading cause of mortality. Early detection is critical in dealing with respiratory diseases as it improves the effectiveness of intervention, including treatment and reducing the spread. The main aim of this paper is to propose a novel lightweight inception network to classify wide spectrum of respiratory diseases using lung sound signals. Methods: The proposed framework consists of three stages: (a) preprocessing, (b) melspectrogram extraction and conversion into a 3-channel image, lastly (c) classification of the melspectrogram images into different pathological classes using proposed lightweight inception network, namely RDsLINet. Results: Utilizing the proposed architecture, we have achieved high classification accuracy of 96%, 99.5%, 91.6% for seven class classification, six class classification and healthy vs. asthma classification. To the best of our knowledge this is the first work on seven class respiratory disease classification using lung sounds. Whereas, our proposed network outperforms all the existing published works for six class and binary classification. Conclusion: The suggested framework makes use of deep learning methods and offers a standardized evaluation with strong categorization capabilities. In order to distinguish between a wide range of respiratory diseases, our study is a pioneering one that focuses exclusively on lung sounds. Significance: The proposed framework can be translated to real time clinical application which will facilitate the prospect of automated respiratory health screening using lung sounds.

Chronic obstructive pulmonary disease (COPD) is a major public health concern across the world. Since it is an incurable disease, early detection and accurate diagnosis are very crucial for preventing the progression of the disease. Lung sounds provide reliable and accurate prognoses for identifying respiratory diseases. Recently, Altan et al. recorded 12-channel real-time lung sound dataset, namely RespiratoryDatabase@TR, for five different severity levels of COPD at Antakya State Hospital Turkey, and proposed deep learning frameworks for two-class COPD classification and five-class classification using a deep belief network (DBN) classifier and extreme learning machine (ELM) classifier respectively. A classification accuracy of 95.84% and 94.31% were achieved for two-class and five-class respectively. In this paper, we have proposed a melspectrogram snippet representation learning framework for both two-class and five-class COPD classification. The proposed framework consists of the following stages: preprocessing, melspectrogram snippet representation generation from lung sound and fine tuning of a pretrained YAMNet. Experimental analysis on the RespiratoryDatabase@TR dataset demonstrates that the proposed framework achieves accuracies of 99.25% and 96.14% for binary and multi-class COPD severity classification respectively, which is superior to the only existing methods proposed by Altan et al. for severity analysis of COPD using lung sounds.