Simulation of Mathematical Model for Lung and Mechanical Ventilation

A mathematical model of lung behavior describes the lung status during normal breathing and artificial ventilation in patients. This paper proposes a new approach of Mathematical Model (MM) improvement for mechanical ventilation including Positive End Expiration Pressure (PEEP) and dynamic compliance (C) , which represents the relation between lung volume and pressure during artificial ventilation.MM has been expressed using linear, quadratic and exponential equations to represent the combination of inspiration and expiration in case Pressure Controlled Ventilator (PCV) and Volume Controlled Ventilator (VCV). Additionally, VCV and PCV signals have been simulated for both ideal and practical case. The MM has been constructed by Matlab platform, where the simulator monitors artificial ventilation pressure, volume and flow curves of VCV and PCV with new considerations PEEP and dynamic compliance monitoring. The simulated MM provides a simple environment for testing and monitoring VCV and PCV and the lung function laboratory.It can be used for instruction and training

Modeling lung functionality in volume-controlled ventilation for critical care patients

Mechanical ventilators are the instruments that assist breathing of the patients having respiratory diseases e.g., pneumonia and coronavirus disease 2019 (COVID-19). This paper presents a modified lung model under volume-controlled ventilation to describe the lung volume and air flow in terms of air pressure signal from the ventilator. A negative feedback is incorporated in the model to balance the lung volume that is influenced by a lung parameter called positive end expiration pressure. We partially solved the lung model equation which takes the form of a first-order differential equation and then unknown parameters associated with the model were computed using a nonlinear least-squares method. Experimental data required for parameter identification and validation of the lung model were obtained by running a volume-controlled ventilator connected to a reference device and an artificial lung. The proposed model considering negative feedback achieves a better accuracy than that without feedback as demonstrated by test results. The developed model can be used in intensive care units (ICU) to evaluate mechanical ventilation performance and lung functionality in real-time

Performance of Adaptive Noise Cancellation with Normalized Last-Mean-Square Based on the Signal-to-Noise Ratio of Lung and Heart Sound Separation

The adaptive algorithm satisfies the present needs on technology for diagnosis biosignals as lung sound signals (LSSs) and accurate techniques for the separation of heart sound signals (HSSs) and other background noise from LSS. This study investigates an improved adaptive noise cancellation (ANC) based on normalized last-mean-square (NLMS) algorithm. The parameters of ANC-NLMS algorithm are the filter length Lj parameter, which is determined in 2n sequence of 2, 4, 8, 16, … , 2048, and the step size (μn), which is automatically randomly identified using variable μn (VSS) optimization. Initially, the algorithm is subjected experimentally to identify the optimal μn range that works with 11 Lj values as a specific case. This case is used to study the improved performance of the proposed method based on the signal-to-noise ratio and mean square error. Moreover, the performance is evaluated four times for four μn values, each of which with all Lj to obtain the output SNRout matrix (4 × 11). The improvement level is estimated and compared with the SNRin prior to the application of the proposed algorithm and after SNRouts. The proposed method achieves high-performance ANC-NLMS algorithm by optimizing VSS when it is close to zero at determining Lj, at which the algorithm shows the capability to separate HSS from LSS. Furthermore, the SNRout of normal LSS starts to improve at Lj of 64 and Lj limit of 1024. The SNRout of abnormal LSS starts from a Lj value of 512 to more than 2048 for all determined μn. Results revealed that the SNRout of the abnormal LSS is small (negative value), whereas that in the normal LSS is large (reaches a positive value). Finally, the designed ANC-NLMS algorithm can separate HSS from LSS. This algorithm can also achieve a good performance by optimizing VSS at the determined 11 Lj values. Additionally, the steps of the proposed method and the obtained SNRout may be used to classify LSS by using a computer

A mathematical model of lung functionality using pressure signal for volume-controlled ventilation

Mechanical Ventilation is used to support the respiratory system malfunction by assisting recovery breathing process which could result from diseases and viruses such as pneumonia and COVID-19. Mathematical models are used to study and simulate the respiratory system supported by mechanical ventilation using different modes such as volume-controlled ventilation (VCV). In this research, a single compartment lung model ventilated by VCV is developed during real time mechanical ventilation using pressure signal. This mathematical model describes the lung volume and compliance correctly considering positive end expiration pressure (PEEP) value. The model is implemented using LabVIEW tools and can be used to monitor the volume, flow and compliance as outputs of the model. Two experiments are carried out on the proposed lung model at three input scenarios of volume (400, 500 and 600 ml) for each experiment considering a PEEP value. To validate the model, an artificial lung connected to a VCV with the same scenarios is used. Validation check is conducted by comparing the outputs of the lung model to that of the artificial lung. The experimental results showed that the measured lung model outputs with negative feedback are the same for pressure and flow as the outputs without negative feedback, whereas the measured volume is comparatively lower for negative feedback. Average percent error in the experiment with negative feedback (5.14%) is smaller compared to the experiment without negative feedback (9.28%). Furthermore, the average error of the calculated compliance decreases from 16% (without negative feedback) to 2% (with negative feedback). The obtained results of the proposed method showed good performance and acceptable accuracy. Thus, the model facilitates the clinicians and practitioners as a training tool to learn real-time mechanical ventilation functionalities