Artificial Intelligence in Medicine, Volume 118, 2021
Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related
breathing disorder. It is caused by an increased upper airway resistance during
sleep, which determines episodes of partial or complete interruption of
airflow. The detection and treatment of OSAS is particularly important in
stroke patients, because the presence of severe OSAS is associated with higher
mortality, worse neurological deficits, worse functional outcome after
rehabilitation, and a higher likelihood of uncontrolled hypertension. The gold
standard test for diagnosing OSAS is polysomnography (PSG). Unfortunately,
performing a PSG in an electrically hostile environment, like a stroke unit, on
neurologically impaired patients is a difficult task; also, the number of
strokes per day outnumbers the availability of polysomnographs and dedicated
healthcare professionals. Thus, a simple and automated recognition system to
identify OSAS among acute stroke patients, relying on routinely recorded vital
signs, is desirable. The majority of the work done so far focuses on data
recorded in ideal conditions and highly selected patients, and thus it is
hardly exploitable in real-life settings, where it would be of actual use. In
this paper, we propose a convolutional deep learning architecture able to
reduce the temporal resolution of raw waveform data, like physiological
signals, extracting key features that can be used for further processing. We
exploit models based on such an architecture to detect OSAS events in stroke
unit recordings obtained from the monitoring of unselected patients. Unlike
existing approaches, annotations are performed at one-second granularity,
allowing physicians to better interpret the model outcome. Results are
considered to be satisfactory by the domain experts. Moreover, based on a
widely-used benchmark, we show that the proposed approach outperforms current
state-of-the-art solutions.
Andrea Bernardini, Andrea Brunello, Gian Luigi Gigli, Angelo Montanari, Nicola Saccomanno
2023-02-10
