Helio Fernandez Tellez1,2, Nathalie Pattyn1,2,3, Olivier Mairesse2,3,4, Leja Dolenc-Groselj5, Ola Eiken6, Igor B Mekjavic7, P F Migeotte8, Em Macdonald-Nethercott9,10, Romain Meeusen1, Xavier Neyt2,11. 1. Vrije Universiteit Brussel, Human Physiology & Sportsmedicine Department, Brussels, Belgium. 2. Royal Military Academy of Brussels, VIPER Research Unit, Brussels, Belgium. 3. Vrije Universiteit Brussels, Biological Psychology Department, Brussels, Belgium. 4. Sleep Laboratory & Unit for Chronobiology - Brugmann University Hospital Free University of Brussels, Brussels, Belgium. 5. Clinical Institute of Clinical Neurophysiology, University Clinical Centre, Ljubljana, Slovenia. 6. Department of Environmental Physiology, Swedish Aerospace Physiology Centre, Royal Institute of Technology, Stockholm, Sweden. 7. Deptartment of Automation, Biocybernetics, and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia. 8. Université Libre de Bruxelles, Erasmus Hospital, Brussels, Belgium. 9. The Princess Alexandra Hospital NHS Trust, Harlow, UK. 10. Institut polaire français Paul-Emile Victor, France. 11. CISS Department, Royal Military Academy of Brussels, Brussels, Belgium.
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.
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.
Authors: P A Lanfranchi; A Braghiroli; E Bosimini; G Mazzuero; R Colombo; C F Donner; P Giannuzzi Journal: Circulation Date: 1999-03-23 Impact factor: 29.690
Authors: R Maestri; G D Pinna; E Robbi; M Varanini; M Emdin; M Raciti; M T La Rovere Journal: Comput Methods Programs Biomed Date: 2002-05 Impact factor: 5.428