Rodriguez-Olivares Noe A, Nava-Balanzar Luciano, Barriga-Rodriguez Leonardo
Division of Electrical Engineering and ElectronicsCenter for Engineering and Industrial Development (CIDESI) Queretaro 76125 Mexico.
School of EngineeringAnahuac University Queretaro 76246 Mexico.
IEEE Trans Instrum Meas. 2021 Sep 29;70:4007610. doi: 10.1109/TIM.2021.3116307. eCollection 2021.
In invasive mechanical ventilation (IMV), it is critical that the flow value is estimated correctly, as it is used as a trigger variable for ventilatory assistance. Furthermore, the numerical integration of the flow allows the calculation of the total volume per breath (tidal volume), which clinicians use to identify trauma or lung capacity in the patient. The current COVID-19 pandemic has demonstrated the need to develop safe and efficient techniques for measuring this spirometry variable because many mechanical ventilators delivered to hospitals were unable to measure it directly. A good device to estimate flow is a D-lite sensor, which works by the Venturi effect, is cheap, reusable, and proximal to the patient. However, the regressions applied to the flow estimation model are limited for use in real conditions. This article presents a flow estimation method that uses a D-Lite device, a fraction of inspired oxygen (FiO) cell, and two pressure sensors as critical items. Our novel method adapts the dichotomous search algorithm instead of conventional regression algorithms to estimate flow using a D-lite sensor; this change in the standard procedure allowed us a fast calibration process, a good low-flow estimation, and low computational time for flow estimation. The method was validated experimentally to compute the tidal volume according to the measurement requirement error range of +/-10%. The consideration of FiO percentage in the gas mixture and the good low-flow estimation make this novel method useful for real ventilation conditions. The flow calculations have been performed at different ambient conditions and compared with gas analyzers show an average relative error of up to 4.86%. Finally, we present an analysis of the error flow estimation considering the variation in each variable. Technical recommendations for applying this novel method to achieve IMV safely are presented, based on the capabilities of the embedded system used by developers.
在有创机械通气(IMV)中,正确估计流量值至关重要,因为它被用作通气辅助的触发变量。此外,流量的数值积分可用于计算每次呼吸的总体积(潮气量),临床医生用此来识别患者的创伤情况或肺容量。当前的新冠疫情表明,开发安全有效的技术来测量这一肺功能变量很有必要,因为许多交付给医院的机械通气机无法直接测量它。一种用于估计流量的良好设备是D-lite传感器,它利用文丘里效应工作,价格便宜、可重复使用且靠近患者。然而,应用于流量估计模型的回归在实际条件下的使用受到限制。本文提出了一种流量估计方法,该方法使用D-Lite设备、吸入氧分数(FiO)传感器和两个压力传感器作为关键部件。我们的新方法采用二分搜索算法而非传统回归算法,利用D-lite传感器估计流量;这种标准程序的改变使我们能够实现快速校准过程、良好的低流量估计以及低流量估计计算时间。该方法通过实验验证,根据测量要求误差范围±10%来计算潮气量。考虑气体混合物中的FiO百分比以及良好的低流量估计,使得这种新方法适用于实际通气条件。在不同环境条件下进行的流量计算,并与气体分析仪进行比较,结果显示平均相对误差高达4.86%。最后,我们对考虑每个变量变化的误差流量估计进行了分析。基于开发者使用的嵌入式系统的能力,提出了将这种新方法安全应用于IMV的技术建议。