Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3392-3395. doi: 10.1109/EMBC48229.2022.9871828.
Ambulatory respiration signal extraction system is required to maintain continuous surveillance of a patient with respiratory deficiency. The capnograph signal has received a lot of attention in recent years as a valuable indicator of respiratory conditions. However, the typical capnograph signal extraction method is quite expensive and also unpleasant to the patient due to the involvement of a nasal cannula. With the advent of wearable sensor technology, there has been significant research on the use of photoplethysmogram (PPG) signals as a less expensive alternative to extract respiratory information. In this paper, we propose CapNet, a novel deep learning-based framework which takes the regular PPG signal as input, and estimates the capnograph signal as output. Training, validation and testing of the proposed networks in CapNet is done using the IEEE TMBE Respiratory Rate Benchmark dataset by utilizing reference capnograph respiration signals. With a lower MSE and higher cross-correlation values, CapNet outperforms two traditional signal processing algorithms and another recently proposed deep neural network, RespNet. The proposed framework expectantly can be implementable and feasible for constant supervising of patients undergoing respiratory ailments.
需要一种可移动呼吸信号提取系统来对呼吸功能不全的患者进行持续监护。近年来，呼气末二氧化碳图信号作为呼吸状况的一个有价值的指标，受到了广泛关注。然而，典型的呼气末二氧化碳图信号提取方法由于涉及到鼻氧管，因此价格昂贵，且患者体验不佳。随着可穿戴传感器技术的出现，人们已经对使用光体积描记图（PPG）信号作为一种更经济的替代方法来提取呼吸信息进行了大量研究。在本文中，我们提出了 CapNet，这是一种基于深度学习的新框架，它以常规的 PPG 信号作为输入，并估计出呼气末二氧化碳图信号作为输出。通过利用参考呼气末二氧化碳图呼吸信号，使用 IEEE TMBE 呼吸率基准数据集对所提出的网络进行训练、验证和测试。CapNet 的均方误差（MSE）更低，互相关值更高，因此优于两种传统的信号处理算法和另一种最近提出的深度神经网络 RespNet。该框架有望可用于对患有呼吸疾病的患者进行持续监护。
