Medical Sound Signal Analysis

Medical sound signal analysis represents a rapidly advancing interdisciplinary domain, integrating biomedical engineering, signal processing, and artificial intelligence to aid in diagnosing and monitoring diverse physiological and pathological states. Auscultatory signals—such as heart sounds, respiratory noises, and gastrointestinal acoustics—contain clinically vital information about internal organ function. Traditionally reliant on subjective interpretation via acoustic stethoscopes, this practice has been revolutionized by digital sensors and data-driven computational frameworks. This chapter provides a comprehensive review of the acquisition, preprocessing, and analysis methods for these signals, detailing relevant sensor technologies and exploring feature extraction approaches in the time-domain, frequency-domain, and time–frequency-domain. Based on a taxonomy of medical acoustics, it primarily introduces the application of classical and deep learning models in pattern recognition and disease classification within Heart Sound Analysis, Lung Sound Analysis, Bowel Sound Analysis, Neurological Speech and Vocal Analysis, Sleep Sound Analysis, and Maternal-Fetal Signal Analysis. Furthermore, contemporary challenges including data scarcity, interpretability, and real-time deployment are addressed, alongside future directions for intelligent auscultation within digital health and precision medicine. Through an interdisciplinary lens, the text highlights the transformative potential of automated analysis in enabling scalable, accurate, and equitable healthcare solutions.