Sleep apnea is a common condition that is characterized by sleep-disordered breathing. Worldwide the number of apnea cases has increased and there has been a growing number of patients suffering from apnea complications. Unfortunately, many cases remain undetected, because expensive and inconvenient examination methods are formidable barriers with regard to the diagnostics. Furthermore, treatment monitoring depends on the same methods which also underpin the initial diagnosis; hence issues related to the examination methods cause difficulties with managing sleep apnea as well. Computer-Aided Diagnosis (CAD) systems could be a tool to increase the efficiency and efficacy of diagnosis. To investigate this hypothesis, we designed a deep learning model that classifies beat-to-beat interval traces, medically known as RR intervals, into apnea versus non-apnea. The RR intervals were extracted from Electrocardiogram (ECG) signals contained in the Apnea-ECG benchmark Database. Before feeding the RR intervals to the classification algorithm, the signal was band-pass filtered with an Ornstein–Uhlenbeck third-order Gaussian process. 10-fold cross-validation indicated that the Long Short-Term Memory (LSTM) network has 99.80% accuracy, 99.85% sensitivity, and 99.73% specificity. With hold-out validation, the same network achieved 81.30% accuracy, 59.90% sensitivity, and 91.75% specificity. During the design, we learned that the band-pass filter improved classification accuracy by over 20%. The increased performance resulted from the fact that neural activation functions can process a DC free signal more efficiently. The result is likely transferable to the design of other RR interval based CAD systems, where the filter can help to improve classification performance.

睡眠呼吸暂停是一种以睡眠呼吸障碍为特征的常见疾病。在世界范围内，呼吸暂停病例的数量增加了，患有呼吸暂停并发症的患者的人数也在增加。不幸的是，由于昂贵且不便的检查方法在诊断方面存在巨大的障碍，因此许多病例仍未被发现。此外，治疗监测取决于同样是初步诊断基础的相同方法。因此，与检查方法有关的问题也导致难以控制睡眠呼吸暂停。计算机辅助诊断（CAD）系统可以成为提高诊断效率和功效的工具。为了研究这一假设，我们设计了一种深度学习模型，该模型将逐次间隔间隔轨迹（医学上称为RR间隔）分类，分为呼吸暂停和非呼吸暂停。RR间隔是从Apnea-ECG基准数据库中包含的心电图（ECG）信号中提取的。在将RR间隔输入分类算法之前，使用Ornstein–Uhlenbeck三阶高斯过程对信号进行带通滤波。10倍交叉验证表明，长期短期记忆（LSTM）网络具有99.80％的准确性，99.85％的灵敏度和99.73％的特异性。通过保留验证，同一网络可实现81.30％的准确性，59.90％的灵敏度和91.75％的特异性。在设计过程中，我们了解到，带通滤波器将分类精度提高了20％以上。性能的提高归因于神经激活功能可以更有效地处理无直流信号。