Hwang Chi Shin, Kim Yong Hwan, Hyun Jung Kyun, Kim Joon Hwang, Lee Seo Rak, Kim Choong Min, Nam Jung Woo, Kim Eun Young
Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea.
Division of Trauma and Surgical Critical Care, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea.
Bioengineering (Basel). 2023 Oct 19;10(10):1222. doi: 10.3390/bioengineering10101222.
The respiratory rate (RR) is a significant indicator to evaluate a patient's prognosis and status; however, it requires specific instrumentation or estimates from other monitored signals. A photoplethysmogram (PPG) is extensively used in clinical environments as well as in intensive care units (ICUs) to primarily monitor peripheral circulation while capturing indirect information about intrathoracic pressure changes. This study aims to apply and evaluate several deep learning models using a PPG for the continuous and accurate estimation of the RRs of patients. The dataset was collected twice for 2 min each in 100 patients aged 18 years and older from the surgical intensive care unit of a tertiary referral hospital. The BIDMC and CapnoBase public datasets were also analyzed. The collected dataset was preprocessed and split according to the 5-fold cross-validation. We used seven deep learning models, including our own Dilated Residual Neural Network, to check how accurately the RR estimates match the ground truth using the mean absolute error (MAE). As a result, when validated using the collected dataset, our model showed the best results with a 1.2628 ± 0.2697 MAE on BIDMC and RespNet and with a 3.1268 ± 0.6363 MAE on our dataset, respectively. In conclusion, RR estimation using PPG-derived models is still challenging and has many limitations. However, if there is an equal amount of data from various breathing groups to train, we expect that various models, including our Dilated ResNet model, which showed good results, can achieve better results than the current ones.
呼吸频率（RR）是评估患者预后和状况的重要指标；然而，它需要特定的仪器设备或从其他监测信号进行估算。光电容积脉搏波描记图（PPG）在临床环境以及重症监护病房（ICU）中被广泛使用，主要用于监测外周循环，同时获取有关胸内压变化的间接信息。本研究旨在应用和评估几种使用PPG的深度学习模型，以连续、准确地估算患者的呼吸频率。该数据集是从一家三级转诊医院的外科重症监护病房中，对100名18岁及以上的患者进行采集的，每次采集2分钟，共采集两次。还对BIDMC和CapnoBase公共数据集进行了分析。所收集的数据集经过预处理，并按照五折交叉验证进行划分。我们使用了七种深度学习模型，包括我们自己的扩张残差神经网络，通过平均绝对误差（MAE）来检查呼吸频率估算值与真实值的匹配程度。结果，在使用所收集的数据集进行验证时，我们的模型分别在BIDMC和RespNet数据集上取得了最佳结果，MAE为1.2628±0.2697，在我们自己的数据集上MAE为3.1268±0.6363。总之，使用基于PPG的模型进行呼吸频率估算仍然具有挑战性，并且存在许多局限性。然而，如果有来自各个呼吸组的等量数据用于训练，我们预计包括表现良好的我们的扩张残差网络模型在内的各种模型，能够取得比当前模型更好的结果。
