Chen Yongliang, Lv Yudan, Sun Xinyu, Poluektov Mikhail, Zhang Yuan, Penzel Thomas
IEEE J Biomed Health Inform. 2024 Dec;28(12):7447-7456. doi: 10.1109/JBHI.2024.3443340. Epub 2024 Dec 5.
By modeling the temporal dependencies of sleep sequence, advanced automatic sleep staging algorithms have achieved satisfactory performance, approaching the level of medical technicians and laying the foundation for clinical assistance. However, existing algorithms cannot adapt well to computing scenarios with limited computing power, such as portable sleep detection and consumer-level sleep disorder screening. In addition, existing algorithms still have the problem of N1 confusion. To address these issues, we propose an efficient sleep sequence network (ESSN) with an ingenious structure to achieve efficient automatic sleep staging at a low computational cost. A novel N1 structure loss is introduced based on the prior knowledge of N1 transition probability to alleviate the N1 stage confusion problem. On the SHHS dataset containing 5,793 subjects, the overall accuracy, macro F1, and Cohen's kappa of ESSN are 88.0%, 81.2%, and 0.831, respectively. When the input length is 200, the parameters and floating-point operations of ESSN are 0.27M and 0.35G, respectively. With a lead in accuracy, ESSN inference is twice as fast as L-SeqSleepNet on the same device. Therefore, our proposed model exhibits solid competitive advantages comparing to other state-of-the-art automatic sleep staging methods.
通过对睡眠序列的时间依赖性进行建模，先进的自动睡眠分期算法已取得了令人满意的性能，接近医学技术人员的水平，为临床辅助奠定了基础。然而，现有算法不能很好地适应计算能力有限的计算场景，如便携式睡眠检测和消费级睡眠障碍筛查。此外，现有算法仍存在N1期混淆的问题。为了解决这些问题，我们提出了一种结构巧妙的高效睡眠序列网络（ESSN），以低计算成本实现高效的自动睡眠分期。基于N1转换概率的先验知识引入了一种新颖的N1结构损失，以缓解N1期混淆问题。在包含5793名受试者的SHHS数据集上，ESSN的总体准确率、宏F1值和科恩卡帕系数分别为88.0%、81.2%和0.831。当输入长度为200时，ESSN的参数和浮点运算分别为0.27M和0.35G。在准确率方面领先，ESSN在同一设备上的推理速度是L-SeqSleepNet的两倍。因此，与其他最新的自动睡眠分期方法相比，我们提出的模型具有坚实的竞争优势。
