S Bae, JW Kim, WY Cho, H Baek, S Son, B Lee… - arXiv preprint arXiv …, 2023 - arxiv.org
Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medical …
D Huang, L Wang, W Wang - IEEE Transactions on Biomedical …, 2023 - ieeexplore.ieee.org
Community-Acquired Pneumonia (CAP) is a significant cause of child mortality globally, due to the lack of ubiquitous monitoring methods. Clinically, the wireless stethoscope can be a …
Real-world sound signals exhibit various aspects of grouping and profiling behaviors, such as being recorded from identical sources, having similar environmental settings, or …
This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Our system initially performs audio feature extraction using Continuous …
HS Kim, HS Park - Journal of Electrical Engineering & Technology, 2023 - Springer
Abstract Machine learning methods for classifying respiratory anomalies have provided low classification accuracy. So high classification accuracy is required in order to utilize …
Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive respiratory monitoring, has demonstrated considerable promise in the early detection of …
This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Initially, our system begins with audio feature extraction using …
JW Kim, M Toikkanen, Y Choi, SE Moon… - arXiv preprint arXiv …, 2024 - arxiv.org
Respiratory sound classification (RSC) is challenging due to varied acoustic signatures, primarily influenced by patient demographics and recording environments. To address this …
Automatic voice pathology detection is promising for noninvasive screening and early intervention using sound signals. Nevertheless, existing methods are susceptible to …