Sleep apnea is one of the most common sleep disorders and the consequences of undiagnosed sleep apnea can be very severe, ranging from increased blood pressure to heart failure. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is an overnight polysomnography in a dedicated sleep laboratory. Yet, these tests are expensive and beds are limited as trained staff needs to analyze the entire recording. An automated detection method would allow a faster diagnosis and more patients to be analyzed. Most algorithms for automated sleep apnea detection use a set of human-engineered features, potentially missing important sleep apnea markers. In this work, we present an algorithm based on state-of-the-art deep learning models for automatically extracting features and detecting sleep apnea events in respiratory signals. The algorithm is evaluated on the Sleep-Heart-Health-Study-1 dataset and provides per-epoch sensitivity and specificity scores comparable to the state-of-the-art. Furthermore, when these predictions are mapped to the apnea-hypopnea-index, a considerable improvement in per-patient scoring is achieved over conventional methods. This work presents a powerful aid for trained staff to quickly diagnose sleep apnea.

No abstract

No abstract

Sleep apnea is one of the most common sleep disorders. It is characterized by the cessation of breathing during sleep due to airway blockages (obstructive sleep apnea) or disturbances in the signals from the brain (central sleep apnea). The gold standard for diagnosing sleep apnea is performing an overnight polysomnography recording which contains, among others, a wide array of respiratory signals. Respiration information can also be extracted from other physiological signals such as an electrocardiogram or from a bio-impedance measurement on the chest. Studies have shown that algorithms can be developed for automated sleep apnea detection using one of these many respiratory signals. In this work, the predictive power of these different respiratory signals is analyzed and compared. The results provide useful insights into the comparative predictive power of the different respiratory signals in a realistic setting for automated sleep apnea detection and provide a basis for the development of less obtrusive measurement techniques.

Bone age is an essential measure of skeletal maturity in children with growth disorders. It is typically assessed by a trained physician using radiographs of the hand and a reference model. However, it has been described that the reference models leave room for interpretation leading to a large inter-observer and intra-observer variation. In this work, we explore a novel method for automated bone age assessment to assist physicians with their estimation. It consists of a powerful combination of deep learning and Gaussian process regression. Using this combination, sensitivity of the deep learning model to rotations and flips of the input images can be exploited to increase overall predictive performance compared to only using the deep learning network. We validate our approach retrospectively on a set of 12611 radiographs of patients between 0 and 19 years of age.