The Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
The Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.
Sensors (Basel). 2021 Feb 8;21(4):1184. doi: 10.3390/s21041184.
Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always has been a research subject in recent years. Being simple and cost-effective, electrocardiogram-based commercial products can be found in the market that provides cardiac diagnostic information for assessment, including heart rate measurement and atrial fibrillation identification. Based on a data-driven and self-adaptive approach, this study aims to estimate heart rate and respiratory rate simultaneously from one lead electrocardiogram signal. In contrast to ensemble empirical mode decomposition with principle component analysis, performed in the time domain, our method uses spectral data fusion, together with intrinsic mode functions using ensemble empirical mode decomposition obtains a more accurate heart rate and respiratory rate. Equipped with a rule-based selection of defined frequency levels for respiratory rate (RR) estimation, the proposed method obtains (0.92, 1.32) beat per minute for the heart rate and (2.20, 2.92) breath per minute for the respiratory rate as their mean absolute error and root mean square error, respectively outperforming other existing methods.
心肺监测对于诊断和处理多种疾病（如压力和睡眠障碍）非常重要和有用。可穿戴式动态系统可以为监测提供连续、舒适和廉价的手段；近年来一直是研究课题。基于心电图的商业产品简单且具有成本效益，可在市场上找到，为评估提供心脏诊断信息，包括心率测量和心房颤动识别。本研究旨在基于数据驱动和自适应方法，从单导联心电图信号中同时估计心率和呼吸率。与在时域中执行的基于集合经验模态分解和主成分分析的方法不同，我们的方法使用频谱数据融合，以及基于集合经验模态分解的固有模态函数，可获得更准确的心率和呼吸率。所提出的方法使用基于规则的选择定义的频率水平进行呼吸率（RR）估计，其心率的平均绝对误差和均方根误差分别为（0.92，1.32）次/分钟，呼吸率的平均绝对误差和均方根误差分别为（2.20，2.92）次/分钟，优于其他现有方法。
