Breathing Cycle Detection for Respiratory Tele-health Systems

Abstract

Recent advancements in wearable devices have enabled the acquisition of lung sounds in real time. By analyzing these signals, key indicators such as respiratory cycles and heart sound components can be extracted, hence enabling the development of tele-health solutions for remote assessment of pulmonary conditions. Particularly, detecting respiratory cycles within the collected sound data plays a crucial role in both clinical and diagnostic applications. Accurate identification of breathing patterns facilitates the assessment of respiratory function and supports early detection of anomalies, including COPD, pneumonia, asthma, and COVID-19. In this paper, we promote a two-step process that first estimates breathing sound signal envelope (in time domain) and then analyzes the envelope peaks/valleys to calculate the respiratory cycle. Three methods that follow such a process are proposed. We examine the practicality, scalability, and efficacy of these methods in both healthy and pathological cases, highlighting their potential for integration into real-world respiratory monitoring and screening systems. We further evaluate their performance using the public dataset ICBHI-2017, which allows comparative analysis of various respiratory conditions.