Novel audio characteristic-dependent feature extraction and data augmentation methods for cough-based respiratory disease classification.

Respiratory diseases are one of the major health problems worldwide. Early diagnosis of the disease types is of vital importance. As one of the main symptoms of many respiratory diseases, cough may contain information about different pathological changes in the respiratory system. Therefore, many researchers have used cough sounds to diagnose different diseases through artificial intelligence in recent years. The acoustic features and data augmentation methods commonly used in speech tasks are used to achieve better performance. Although these methods are applicable, previous studies have not considered the characteristics of cough sound signals. In this paper, we designed a cough-based respiratory disease classification system and proposed audio characteristic-dependent feature extraction and data augmentation methods. Firstly, according to the short durations and rapid transition of different cough stages, we proposed maximum overlapping mel-spectrogram to avoid missing inter-frame information caused by traditional framing methods. Secondly, we applied various data augmentation methods to mitigate the problem of limited labeled data. Based on the frequency energy distributions of different diseased cough audios, we proposed a parameter-independent self-energy-based augmentation method to enhance the differences between different frequency bands. Finally, in the model testing stage, we leveraged test-time augmentation to further improve the classification performance by fusing the test results of the original and multiple augmented audios. The proposed methods were validated on the Coswara dataset through stratified four-fold cross-validation. Compared to the baseline model using mel-spectrogram as input, the proposed methods achieved an average absolute performance improvement of 3.33% and 3.10% in macro Area Under the Receiver Operating Characteristic (macro AUC) and Unweighted Average Recall (UAR), respectively. The visualization results through Gradient-weighted Class Activation Mapping (Grad-CAM) showed the contributions of different features to model decisions.

Automated valvular heart disease detection using heart sound with a deep learning algorithm.

Background: Insufficient clinicians' auscultation ability delays the diagnosis and treatment of valvular heart disease (VHD); artificial intelligence provides a solution to compensate for the insufficiency in auscultation ability by distinguishing between heart murmurs and normal heart sounds. However, whether artificial intelligence can automatically diagnose VHD remains unknown. Our objective was to use deep learning to process and compare raw heart sound data to identify patients with VHD requiring intervention. Methods: Heart sounds from patients with VHD and healthy controls were collected using an electronic stethoscope. Echocardiographic findings were used as the gold standard for this study. According to the chronological order of enrollment, the early-enrolled samples were used to train the deep learning model, and the late-enrollment samples were used to validate the results. Results: The final study population comprised 499 patients (354 in the algorithm training group and 145 in the result validation group). The sensitivity, specificity, and accuracy of the deep-learning model for identifying various VHDs ranged from 71.4 to 100.0%, 83.5-100.0%, and 84.1-100.0%, respectively; the best diagnostic performance was observed for mitral stenosis, with a sensitivity of 100.0% (31.0-100.0%), a specificity of 100% (96.7-100.0%), and an accuracy of 100% (97.5-100.0%). Conclusions: Based on raw heart sound data, the deep learning model effectively identifies patients with various types of VHD who require intervention and assists in the screening, diagnosis, and follow-up of VHD.

Rethinking terrestrial dissolved organic matter in dam reservoirs before mixing: Linking photodegradation and biodegradation and the phenanthrene binding behavior.

With the increased construction of dam reservoirs and the demand for water security, terrestrial dissolved organic matter (DOM) has received attention because of its role in regulating water quality, ecological functions, and the fate and transport of pollutants in dam reservoirs. This study investigated the transformations of soil DOM and vegetation DOM of dam reservoirs following photodegradation and biodegradation before conservative mixing, as well as the resultant effects on phenanthrene binding. Based on the results, terrestrial DOM could undergo transformation via photodegradation and biodegradation before conservative mixing in dam reservoirs. Although both processes resulted in substantial decreases in DOM concentrations, the changes in chromophoric DOM and fluorescent DOM depended on the original DOM sources. Furthermore, the photodegradation of terrestrial DOM resulted in more pronounced photobleaching than photomineralization. In addition, photodegradation of terrestrial DOM resulted in the generation of DOM-derived by-products with low molecular weight and low aromaticity, whereas the biodegradation of terrestrial DOM resulted in DOM-derived by-products with low molecular weight and high aromaticity. Subsequently, the photodegradation and biodegradation of terrestrial DOM substantially enhanced the binding affinity of phenanthrene. Soil DOM is prior to vegetation DOM when predicting the ecological risk of HOCs. These results indicate that the terrestrial DOM in dam reservoirs should be reconsidered before conservative mixing. Further studies on the coupling effects of both biogeochemical processes, as well as on the relative contributions of soil DOM and vegetation DOM after transformation to the aquatic DOM in dam reservoirs, are required. This study provides information on the environmental effects of dam construction from the perspective of biogeochemical processes.