Zhao Qi, Liu Fang, Song Yide, Fan Xiaoya, Wang Yu, Yao Yudong, Mao Qian, Zhao Zheng
School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China.
School of Information Technology, Dalian Maritime University, Dalian 116026, China.
Bioengineering (Basel). 2023 Aug 30;10(9):1024. doi: 10.3390/bioengineering10091024.
The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.
呼吸频率（RR）在诊断和预后评估中都是一个关键的生理参数。由于直接测量存在挑战，在临床实践中，RR仍主要通过传统的人工计数呼吸法进行测量。已经开发了许多算法和机器学习模型，用于利用生理信号（如心电图（ECG）或/和光电容积脉搏波描记图（PPG）信号）来预测RR。然而，这些现有方法在可用数据集上的准确性仍然有限，并且它们对新数据的预测在实际临床应用中也不尽人意。在本文中，我们提出了一种带有初始模块的增强型Transformer模型，用于基于ECG和PPG信号预测RR。为了评估对新数据的泛化能力，我们的模型使用来自BIDMC和CapnoBase数据集的数据，通过受试者水平的十折交叉验证进行训练和测试。在测试集上，我们的模型比五种流行的基于深度学习的方法表现更优，平均绝对误差（1.2）比这些模型的最佳结果降低了36.5%，相关系数（0.85）提高了84.8%。此外，我们还提出了一种新的管道来预处理ECG和PPG信号，以提高模型性能。我们相信，TransRR模型的开发有望进一步加快自动RR估计的临床应用。
