Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller

Karar, Mohamed Esmail; El-Brawany, Mohamed A.

doi:10.4137/becb.s6495

Cited by 21 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PI controller is the most commonly used control algorithm among the conventional controllers. It works with the combination of proportional and integral mode (16)(17)(18) . The ideal form of PI controller is represented as C out = C out bias + K c e(t) + K c τ i ∫ e(t)dt (15) https://www.indjst.org/ Where, C out is the controller output C out bias is the controller bias e(t) is the process error where e(t)=SV-PV SV is the reference set point and PV the measured process output K c is the controller gain (tuning parameter) and τ i is the integral time (tuning parameter), where K C =K P and K I =1/τ i .…”

Section: Elimination and Dispersalmentioning

confidence: 99%

Modeling and optimized controller for cardiac drug infusion system

Sowparnika¹,

Thirumarimurugan²,

Vinoth³

2020

IJST

View full text Add to dashboard Cite

Background: Manual drug infusion during surgeries is inaccurate and timeconsuming which has been adopted technique in most of the hospitals. Methods: Based on clinical data, it is evident that the manual control is inaccurate and takes prolonged time to bring into effect, if any change in infusion rate is required during clinical practice. Considering this drawback, the modeling of cardiovascular system (CVS) and baroreceptor (BR) is developed using miscellaneous differential equations based on compartmental approach. The control variables are mean arterial pressure (MAP) and cardiac output (CO) obtained from CVS-BR model. Findings: The manipulated variables are noradrenaline (NAR) and nitroglycerine (NG) infusion rate which is modeled using the relationship between volume and drug mass effect equations on CVS-BR model. For the open loop transfer function derived from the model, relative gain array (RGA) analysis is performed to identify the influence of maximum effect of manipulated variables on the physiological parameters. The simulation results obtained from MATLAB are correlated using time domain specifications and error criteria. The performance index reveals the least error and facilitates in accurate infusion of drugs to the patient during cardiovascular surgeries. Novelty: The automatic controller during surgery provides safe operating condition and speedy recovery of the patients and also helps the anaesthetist to monitor and regulate the physiological variables.

show abstract

Section: Elimination and Dispersalmentioning

confidence: 99%

Modeling and optimized controller for cardiac drug infusion system

Sowparnika¹,

Thirumarimurugan²,

Vinoth³

2020

IJST

View full text Add to dashboard Cite

show abstract

“…In order to aid the anesthetist in the monitoring and control of DOA, recent research has investigated the design of systems for accurately administering and adjusting the delivery of anesthetic in direct response to patients' physiological changes based on intelligently adaptive closed-loop control systems [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The main limitations of these systems are that BIS is one of the main reference value used to control DOA of patients, which is prone to disruption when the signal is interfered with.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Monitoring Scheme for Automatic Control of Anaesthesia in dynamic surgical environments based on Bispectral Index and Blood Pressure

Doctor

Fan

et al. 2018

J Med Syst

View full text Add to dashboard Cite

During surgical procedures, bispectral index (BIS) is a well-known measure used to determine the patient's depth of anesthesia (DOA). However, BIS readings can be subject to interference from many factors during surgery, and other parameters such as blood pressure (BP) and heart rate (HR) can provide more stable indicators. However, anesthesiologist still consider BIS as a primary measure to determine if the patient is correctly anaesthetized while relaying on the other physiological parameters to monitor and ensure the patient's status is maintained. The automatic control of administering anesthesia using intelligent control systems has been the subject of recent research in order to alleviate the burden on the anesthetist to manually adjust drug dosage in response physiological changes for sustaining DOA. A system proposed for the automatic control of anesthesia based on type-2 Self Organizing Fuzzy Logic Controllers (T2-SOFLCs) has been shown to be effective in the control of DOA under simulated scenarios while contending with uncertainties due to signal noise and dynamic changes in pharmacodynamics (PD) and pharmacokinetic (PK) effects of the drug on the body. This study considers both BIS and BP as part of an adaptive automatic control scheme, which can adjust to the monitoring of either parameter in response to changes in the availability and reliability of BIS signals during surgery. The simulation of different control schemes using BIS data obtained during real surgical procedures to emulate noise and interference factors have been conducted. The use of either or both combined parameters for controlling the delivery Propofol to maintain safe target set points for DOA are evaluated. The results show that combing BIS and BP based on the proposed adaptive control scheme can ensure the target set points and the correct amount of drug in the body is maintained even with the intermittent loss of BIS signal that could otherwise disrupt an automated control system.

show abstract

“…Here an FLC is initially pre-defined and an on-line adaptation algorithm is used to tune its input and output type-1 membership function parameters during the simulation run. In [14] a fuzzy neural network (FNN) is proposed to automatically manage the hemodynamic variables specifically, MAP and cardiac output (CO), of patients with hypertension and congestive heart failure by simultaneous infusion of cardiac drugs such as vasodilators and inotropic agents. The parameters of the FNN are initialised based on physician expert experience.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

“…The FNN in [14] is a multi-variable system for controlling MAP and CO in a postoperative scenario. Two separate sub-controllers are used in [14], which is a similar concept as the decomposition of the multi-variable structure used for SOFLC. However a major difference between the FNN and our SOFLC is that the FNN is able to modify the MFs whereas the SOFLC can self-adapt by modifying the fuzzy control rules.…”

mentioning

confidence: 99%

See 1 more Smart Citation