Kumar Amit Krishan, Jain Snigdha, Jain Shirin, Ritam M, Xia Yuanqing, Chandra Rohitash
Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam; State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
Department of Electronics and Communications Engineering, Indian Institute of Technology Guwahati, Assam, India.
Comput Methods Programs Biomed. 2023 Apr;231:107421. doi: 10.1016/j.cmpb.2023.107421. Epub 2023 Feb 15.
The use of machine learning methods for modelling bio-systems is becoming prominent which can further improve bio-medical technologies. Physics-informed neural networks (PINNs) can embed the knowledge of physical laws that govern a system during the model training process. PINNs utilise differential equations in the model which traditionally used numerical methods that are computationally complex.
We integrate PINNs with an entangled ladder network for modelling respiratory systems by considering a lungs conduction zone to evaluate the respiratory impedance for different initial conditions. We evaluate the respiratory impedance for the inhalation phase of breathing for a symmetric model of the human lungs using entanglement and continued fractions.
We obtain the impedance of the conduction zone of the lungs pulmonary airways using PINNs for nine different combinations of velocity and pressure of inhalation. We compare the results from PINNs with the finite element method using the mean absolute error and root mean square error. The results show that the impedance obtained with PINNs contrasts with the conventional forced oscillation test used for deducing the respiratory impedance. The results show similarity with the impedance plots for different respiratory diseases.
We find a decrease in impedance when the velocity of breathing is lowered gradually by 20%. Hence, the methodology can be used to design smart ventilators to the improve flow of breathing.
使用机器学习方法对生物系统进行建模正变得日益重要，这能够进一步改进生物医学技术。物理信息神经网络（PINNs）可以在模型训练过程中嵌入支配系统的物理定律知识。PINNs在模型中使用微分方程，而传统上使用计算复杂的数值方法。
我们将PINNs与纠缠梯网络相结合，通过考虑肺部传导区来对呼吸系统进行建模，以评估不同初始条件下的呼吸阻抗。我们使用纠缠和连分数来评估人体肺部对称模型呼吸吸气阶段的呼吸阻抗。
我们使用PINNs获得了肺部气道传导区在九种不同吸气速度和压力组合下的阻抗。我们使用平均绝对误差和均方根误差将PINNs的结果与有限元方法的结果进行比较。结果表明，PINNs获得的阻抗与用于推导呼吸阻抗的传统强迫振荡测试结果不同。结果显示与不同呼吸系统疾病的阻抗图相似。
我们发现当呼吸速度逐渐降低20%时，阻抗会降低。因此，该方法可用于设计智能呼吸机以改善呼吸流量。
