Abstract
Cheyne-Stokes respiration (CSR) has a high prevalence among newborns, especially among preterm babies. Although doctors generally recognize the phenomenon, they are not able to assess the severity of CSR in individual infants. CSR is characterized by cyclical weakening and strengthening of respirations with apnea. In this study, we developed an apnea detection method, and a CSR detection method using detected apneic events. We detected apnea using two features of respiratory waveforms. The first feature is frequency information calculated from wavelet coefficients. The second is information based on the shape of the waveform. In our CSR detection method, we used a spurious periodicity feature to determine CSR sections. The waveform is calculated by a respiratory monitoring system that uses a fiber-grating vision sensor to measure the vertical motion of the infant's thoracic and abdominal regions during respiration. Our method is effective at detecting apnea (sensitivity: 94.3 %, specificity: 99.7 %).
