Jianying Shi1, Shengchao Chen1, Benguo Yu2, Yi Ren3,*, Guanjun Wang1,4,*, Chenyang Xue5
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3235-3258, 2023, DOI:10.32604/iasc.2023.041392
Abstract Respiratory infections in children increase the risk of fatal lung disease, making effective identification and analysis of breath sounds essential. However, most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system, and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy. In this work, we collected three types of breath sounds from children with normal (120 recordings), bronchitis (120 recordings), and pneumonia (120 recordings) at the posterior chest position using an off-the-shelf 3M electronic stethoscope. Three features were extracted from… More >
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