Abstract
The Sleep Apnea Syndrome is a sleep disorder characterized by frequently repeated respiratory disorders during sleep. It needs the simultaneous recording of many physiological parameters to be diagnosed. The analysis of these curves is a time consuming task made by sleep Physicians. First, they detect some physiological events on each curve and then, they point out links between respiratory events and their consequences. To support the diagnosis, we used symbolic fusion on elementary events, which connects events to their sleep context - sleep-stage and body position - and to the respiratory event responsible of their occurrence. The reference indicator is the Apnea-Hypopnea Index (AHI), defined as the average hourly frequency of arisen of Apneas or Hypopneas while the patient is sleeping. We worked on the polysomnography of 59 patients, that were first completely analyzed by a sleep Physician and then analyzed by our method. We compared the ratio of the AHI got by the automatic analysis and the AHI got by the sleep Physician.
Globally, we overvalued the count of apneas and hypopneas for the group of patients with AHI ≤ 5, that are considered as healthy patients. In average, for these patients, δ = 2,71. For patients with mild or moderate Sleep Apnea Syndrome we globally found a similar AHI. In average, for these patients, δ = 1,04. For patients with severe Sleep Apnea Syndrome, we undervalued a little the count of respiratory events. In average, for these patients, δ = 0,83. This leads to the same severity class for most of the patients.
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Ugon, A., Ganascia, JG., Philippe, C., Amiel, H., Lévy, P. (2011). How to Use Symbolic Fusion to Support the Sleep Apnea Syndrome Diagnosis. In: Peleg, M., Lavrač, N., Combi, C. (eds) Artificial Intelligence in Medicine. AIME 2011. Lecture Notes in Computer Science(), vol 6747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22218-4_6
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DOI: https://doi.org/10.1007/978-3-642-22218-4_6
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