Abstract
STUDY OBJECTIVES: Periodic breathing is sleep disordered breathing characterized by instability in the respiratory pattern that exhibits an oscillatory behavior. Periodic breathing is associated with increased mortality, and it is observed in a variety of situations, such as acute hypoxia, chronic heart failure, and damage to respiratory centers. The standard quantification for the diagnosis of sleep related breathing disorders is the apnea-hypopnea index (AHI), which measures the proportion of apneic/ hypopneic events during polysomnography. Determining the AHI is labor-intensive and requires the simultaneous recording of airflow and oxygen saturation. In this paper, we propose an automated, simple, and novel methodology for the detection and qualification of periodic breathing: the estimated amplitude modulation index (eAMI). PATIENTS OR PARTICIPANTS: Antarctic Cohort (3800 meters): 13 normal individuals. Sleep Clinic Cohort: 39 different patients suffering from diverse sleep-related pathologies. MEASUREMENTS AND RESULTS: When tested in a population with high levels of periodic breathing (Antarctic Cohort), eAMI was closely correlated with AHI (r = 0.95, P < 0.001). When tested in the clinical setting, the proposed method was able to detect portions of the signal in which subclinical periodic breathing was validated by an expert (n = 93; accuracy = 0.85). Average eAMI was also correlated with the loop gain for the combined clinical and Antarctica cohorts (r = 0.58, P < 0.001). CONCLUSIONS: In terms of quantification and temporal resolution, the eAMI is able to estimate the strength of periodic breathing and the underlying loop gain at any given time within a record. The impaired prognosis associated with periodic breathing makes its automated detection and early diagnosis of clinical relevance.