An increasing number of studies in newborn mice are being performed to determine the mechanisms of sleep apnea, which is the hallmark of early breathing disorders. Whole body plethysmography is the method of choice, as it does not require immobilization, which affects behavioral states and breathing. However, activity inside the plethysmograph may disturb the respiratory signal. Visual classification of the respiratory signal into ventilatory activity, activity-related disturbances, or apneas is so time-consuming as to considerably hamper the phenotyping of large pup samples. We propose an automatic classification of activity based on respiratory disturbances and of apneas based on spectral analysis. This method was validated in newborn mice on the day of birth and on postnatal days 2, 5, and 10, under normoxic and hypoxic (5% O 2) conditions. For both activity and apneas, visual and automatic scores showed high Pearson's correlation coefficients (0.92 and 0.98, respectively) and high intraclass correlation coefficients (0.96-0.99), supporting strong agreement between the two methods. The present results suggest that breathing disturbances may provide a valid indirect index of activity in freely moving newborn mice and that automatic apnea classification based on spectral analysis may be efficient in terms of precision and of time saved.