Iqbal Talha, Elahi Adnan, Ganly Sandra, Wijns William, Shahzad Atif
Smart Sensor Lab, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, National University of Ireland, Galway, H91 TK33 Ireland.
Department of Electrical Engineering, National University of Ireland, Galway, H91 TK33 Ireland.
J Med Biol Eng. 2022;42(2):242-252. doi: 10.1007/s40846-022-00700-z. Epub 2022 Apr 7.
Respiratory rate can provide auxiliary information on the physiological changes within the human body, such as physical and emotional stress. In a clinical setup, the abnormal respiratory rate can be indicative of the deterioration of the patient's condition. Most of the existing algorithms for the estimation of respiratory rate using photoplethysmography (PPG) are sensitive to external noise and may require the selection of certain algorithm-specific parameters, through the trial-and-error method.
This paper proposes a new algorithm to estimate the respiratory rate using a photoplethysmography sensor signal for health monitoring. The algorithm is resistant to signal loss and can handle low-quality signals from the sensor. It combines selective windowing, preprocessing and signal conditioning, modified Welch filtering and postprocessing to achieve high accuracy and robustness to noise.
The Mean Absolute Error and the Root Mean Square Error of the proposed algorithm, with the optimal signal window size, are determined to be 2.05 breaths count per minute and 2.47 breaths count per minute, respectively, when tested on a publicly available dataset. These results present a significant improvement in accuracy over previously reported methods. The proposed algorithm achieved comparable results to the existing algorithms in the literature on the BIDMC dataset (containing data of 53 subjects, each recorded for 8 min) for other signal window sizes.
The results endorse that integration of the proposed algorithm to a commercially available pulse oximetry device would expand its functionality from the measurement of oxygen saturation level and heart rate to the continuous measurement of the respiratory rate with good efficiency at home and in a clinical setting.
The online version contains supplementary material available at 10.1007/s40846-022-00700-z.
呼吸频率可以提供有关人体生理变化的辅助信息，例如身体和情绪压力。在临床环境中，异常呼吸频率可能表明患者病情恶化。大多数现有的使用光电容积脉搏波描记法（PPG）估计呼吸频率的算法对外部噪声敏感，并且可能需要通过反复试验的方法选择某些特定于算法的参数。
本文提出了一种新的算法，用于使用光电容积脉搏波描记法传感器信号来估计呼吸频率，以进行健康监测。该算法对信号丢失具有抗性，并且可以处理来自传感器的低质量信号。它结合了选择性加窗、预处理和信号调理、改进的韦尔奇滤波和后处理，以实现高精度和对噪声的鲁棒性。
在所提出的算法中，当在公开可用数据集上进行测试时，具有最佳信号窗口大小的平均绝对误差和均方根误差分别确定为每分钟2.05次呼吸计数和每分钟2.47次呼吸计数。这些结果在准确性方面比先前报道的方法有了显著提高。在所提出的算法在BIDMC数据集（包含53名受试者的数据，每人记录8分钟）上针对其他信号窗口大小的测试中，取得了与文献中现有算法相当的结果。
结果表明，将所提出的算法集成到市售脉搏血氧仪设备中，将扩展其功能，从测量血氧饱和度水平和心率扩展到在家中和临床环境中高效连续测量呼吸频率。
在线版本包含可在10.1007/s40846-022-00700-z获取的补充材料。
