Neonatal Respiration Monitoring System with Synchronized Oxygen Supply and Machine Learning‐Based Breathing Classification

Respiratory distress is a major contributor to neonatal morbidity and mortality. Preterm infants often require continuous monitoring and artificial respiratory support due to underdeveloped lungs. Effective respiratory support requires precise synchronization of oxygen delivery with an infant's natural breathing to minimize lung injury, air leaks, or impaired cerebral blood flow. This requires noninvasive systems with intelligent sensors capable of capturing high‐fidelity respiratory signals in real‐time to accurately detect breathing cycles. However, such systems typically involve complex and costly sensor fabrication processes, energy‐intensive designs, and extensive computational demands. This study presents a low‐cost, portable, and real‐time neonatal respiration monitoring system with synchronized oxygen delivery. The skin‐like sensor patch, fabricated through screen‐printing, demonstrated high sensitivity (≈75/με) within 0–20 000 με, rapid response time (≈140 ms), minimal drift (2%) over 7,000 cyclic tests, and negligible thermal variation (<0.5 °C over 8 h). A multistage signal processing algorithm, combined with machine learning‐based classification facilitates accurate real‐time differentiation among normal breathing, tachypnea, and apnea episodes. The developed system offers a practical, noninvasive, and comfortable solution for continuous neonatal respiratory monitoring, significantly benefiting neonates in remote areas where continuous monitoring and sophisticated equipment are unavailable.