An explainable COVID-19 detection system based on human sounds.
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2022-12
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Acoustic signals generated by the human body have often been used as biomarkers to diagnose and monitor diseases. As the pathogenesis of COVID-19 indicates impairments in the respiratory system, digital acoustic biomarkers of COVID-19 are under investigation. In this paper, we explore an accurate and explainable COVID-19 diagnosis approach based on human speech, cough, and breath data using the power of machine learning. We first analyze our design space considerations from the data aspect and model aspect. Then, we perform data augmentation, Mel-spectrogram transformation, and develop a deep residual architecture-based model for prediction. Experimental results show that our system outperforms the baseline, with the ROC-AUC result increased by 5.47%. Finally, we perform an interpretation analysis based on the visualization of the activation map to further validate the model.
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Li, Huining, Xingyu Chen, Xiaoye Qian, Huan Chen, Zhengxiong Li, Soumyadeep Bhattacharjee, Hanbin Zhang, Ming-Chun Huang, et al. (2022). An explainable COVID-19 detection system based on human sounds. Smart health (Amsterdam, Netherlands), 26. p. 100332. 10.1016/j.smhl.2022.100332 Retrieved from https://hdl.handle.net/10161/31317.
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Ming-Chun Huang
Huang has a B.S (2007) in Electrical Engineering at Tsing Hua University, Taiwan, an M.S. (2010) in Electrical Engineering at the University of Southern California, and a Ph.D. (2014) in Computer Science at the University of California, Los Angeles. Prior to joining Duke Kunshan University in 2021, he was an Associate Professor at Case Western Reserve University (2014-2021). His research focus is the intersection among Precision Health and Medicine, Internet-of-Things, Machine Learning and Informatics, Motion and Physiological Signal Sensing. He had over 15 years of research experience conducting interdisciplinary scientific projects with researchers from distinct areas (e.g., Biomedical Engineering, Medicine, and Nursing). He had successfully administered past funded projects and productively published over a hundred peer-reviewed publications, 6 invention patents and software copyrights, and won 7 best paper awards/runner-up, 3000+ citations. His research has been reported in hundreds of high-impact media outlets. For the nature of richness and high impact of the research topics he was involved in, his research results in a plethora of new knowledge in aspects ranging from innovative IoT sensing technology, closed-loop AI analytics methodology, optimized clinical decision-making, and just-in-time patient risk assessment.
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