Jianying Shi¹, Shengchao Chen¹, Benguo Yu², Yi Ren^3,*, Guanjun Wang^1,4,*, 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 >

Hassan Ashraf¹, Asim Waris^1,*, Syed Omer Gilani¹, Muhammad Umair Tariq¹, Hani Alquhayz²

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 799-815, 2021, DOI:10.32604/iasc.2021.014765

Abstract Empirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose a signal into its Intrinsic Mode Functions (IMF). EMD has attained great attention due to its capabilities to process a signal in the frequency-time domain without altering the signal into the frequency domain. EMD-based signal denoising techniques have shown great potential to denoise nonlinear and nonstationary signals without compromising the signal’s characteristics. The denoising procedure comprises three steps, i.e., signal decomposition, IMF thresholding, and signal reconstruction. Thresholding is performed to assess which IMFs contain noise. In this study, Interval Thresholding (IT), Iterative Interval Thresholding (IIT),… More >