Automated detection of newborn sleep apnea using video monitoring system

Automated detection of neonatal sleep apnea is essential for constrained environments with high patient to nurse ratio. Existing studies on apnea detection mostly target adults, and use invasive sensors. Few approaches detect apnea using video monitoring, by identifying absence of respiratory motion. They apply frame differencing and thresholding, not suitable for neonates due to their subtle respiratory motion intermixed with other body movements. Proposed method first applies motion magnification. Subsequently, it filters respiration motion using dynamic thresholding. The technique is benchmarked with simulated motion of varying respiration frequencies. When validated with neonatal video data, proposed method achieves both > 90% sensitivity and specificity.