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 patients who suffered a stroke, 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; moreover, the number of strokes per day vastly outnumbers the availability of polysomnographs and dedicated healthcare professionals. Hence, a simple and automated recognition system to identify OSAS cases among acute stroke patients, relying on routinely recorded vital signs, is highly desirable. The vast 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 circumstances, where it would be of actual use. In this paper, we propose a novel convolutional deep learning architecture able to effectively 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, through tests run on a widely-used public OSAS dataset, we show that the proposed approach outperforms current state-of-the-art solutions.

阻塞性睡眠呼吸暂停综合征 (OSAS) 是最常见的睡眠相关呼吸障碍。它是由睡眠期间上呼吸道阻力增加引起的，这决定了气流部分或完全中断的发作。OSAS 的检测和治疗对中风患者尤为重要，因为严重 OSAS 的存在与更高的死亡率、更严重的神经功能缺损、康复后更差的功能结果以及更高的不受控制的高血压可能性有关。诊断 OSAS 的金标准测试是多导睡眠图 (PSG)。不幸的是，在对神经系统受损的患者进行电击治疗的环境（如卒中单元）中执行 PSG 是一项艰巨的任务。而且，每天中风的次数远远超过多导睡眠仪和专职医疗保健专业人员的可用性。因此，非常需要一种简单且自动的识别系统，以根据常规记录的生命体征来识别急性中风患者的 OSAS 病例。迄今为止所做的绝大多数工作都集中在理想条件下记录的数据和高度选择的患者上，因此在实际使用的现实环境中很难利用它。在本文中，我们提出了一种新颖的卷积深度学习架构，能够有效降低原始波形数据（如生理信号）的时间分辨率，提取可用于进一步处理的关键特征。我们利用基于这种架构的模型来检测通过监测未选择的患者获得的卒中单元记录中的 OSAS 事件。与现有方法不同，注释以一秒的粒度执行，允许医生更好地解释模型结果。结果被领域专家认为是令人满意的。此外，通过在广泛使用的公共 OSAS 数据集上运行的测试，我们表明所提出的方法优于当前最先进的解决方案。