Respiratory Anomaly

Respiratory anomaly detection research focuses on developing accurate and efficient methods for identifying abnormal breathing patterns, primarily using non-invasive techniques like respiratory sound analysis and non-contact sensors. Current research heavily utilizes deep learning, employing architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and ensemble methods, often incorporating multimodal data (audio, physiological signals) to improve diagnostic accuracy. These advancements hold significant promise for improving early diagnosis and management of respiratory diseases, enhancing patient care, and potentially enabling remote monitoring in both clinical and home settings.