asthma-detection-research-based-on-voice-signal-processing-machine

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

To analyze voice signals and identify asthma patients using voice signal analysis and machine learning techniques, we collected clear, low-noise ...

Chinese Academy of Sciences

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.

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

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.

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

In recent years, COVID-19 has placed enormous burdens on healthcare systems. Currently, hundreds of thousands of new cases are reported monthly. World Health Organization is managing COVID-19 as a long-term disease, indicating that an efficient and low-cost detection method remains necessary. Previous studies have shown competitive results on cough-based COVID-19 detection combined with deep learning methods. However, most studies have focused only on improving classification performance on single-source data while neglecting the impact of various factors in real-world applications.

Article	Year
Automated valvular heart disease detection using heart sound with a deep learning algorithm. International journal of cardiology. Heart & vasculature\| PubMed ID: 38482387	2024
Novel audio characteristic-dependent feature extraction and data augmentation methods for cough-based respiratory disease classification. Computers in biology and medicine\| PubMed ID: 39029433	2024
A comprehensive validation study on the influencing factors of cough-based COVID-19 detection through multi-center data with abundant metadata. Journal of biomedical informatics\| PubMed ID: 39993588	2025

Xueshuai Zhang

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