Control in Physiology and Medicine

Bequette, B. Wayne

doi:10.1016/b978-0-12-411557-6.00002-1

Cited by 3 publications

(3 citation statements)

References 72 publications

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“…The regression function for SVR is expressed as in where f(x) denotes the predicted values, x is the input variable, w is the weight coefficient, b is the deviation value, ∅x is the high dimensional feature space. The regularized risk function is given as in (7) where is a regularized term, c is the penalty parameter, and the empirical error is , ε is the loss function. The loss function is minimized to determine the values of w and b and then predicted output is identified.…”

Section: Infusion Ratementioning

confidence: 99%

“…(6) Bequette (2013) conducted research on the use of various infusion pumps for anesthesia based on drug dosage and medication infusion for patients. (7) The ANOVA test has been used to examine the practical constraints of delivering the desired volume with three infusion pumps at a specified flow rate. (8) Statistical studies of infusion pump performance address issues about effective drug administration.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning approach to predict delay in smart infusion pump

Alamelu

Sivanantham

2022

Salud, Ciencia y Tecnología

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Wireless smart infusion pumps are currently under development. It is critical to ensure that the patient receives the correct drug concentration. Practically, the performance of the pump has relied on the minimum startup delay. The minimization of the startup delay is prominent in open-type infusion pumps and rarely in closed types. The emphasis on reducing startup delay puts practitioners and caregivers at ease while ensuring patient safety. The startup delay of the infusion pump is based on the flow rate and the lag time. The prediction of the flow rate and lag time for an infusion pump is necessitated to ensure a safe drug dosage for the patient. Currently, machine learning methods and computational methods to predict the desired parameter are widely used in healthcare applications and medical device performance. The reduction of start-up delay can be achieved by predicting its associated parameters lag time and flow rate. The flow rate is dependent on the speed of the infusion pump, which has to be calculated based on the number of gears and revolutions. The speed of the pump has to be predicted for accurate flow delivery. Our present research attempts to predict the lag time of an infusion pump using different kernel functions of support vector regression (SVR). The performance of the SVR for each kernel function is compared with R2, RMSE, MAE, and prediction accuracy. The prediction accuracy of 99,7 % has been obtained in optimized SVM.

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Section: Infusion Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning approach to predict delay in smart infusion pump

Alamelu

Sivanantham

2022

Salud, Ciencia y Tecnología

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“…Looking at the importance of DPM and SNP in control of MABP and CO in cardiac patients, the lack of automatic control for the simultaneous regulation of MABP and CO may lead to increase in the time spent by ICU nurses in attending the cardiac patient for monitoring blood pressure. According to a survey (Bequette, 2013), 73% of the myocardial revascularization patients suffer from post-operative hypertension and require control of blood pressure. The same survey revealed that the Intensive Care Unit (ICU) nurses spend 26% of their time in attending the hypertensive patient post-operation for monitoring blood pressure.…”

mentioning

confidence: 99%

The Impact of Fractional-Order Control on Blood Pressure Regulation

Nagarsheth

Sharma

2021

International Journal of E-Health and Medical Communications

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This paper presents a fractional-order framework for control of blood pressure regulation system. A new perspective is explored to control the blood pressure in lieu of the conventional control framework. A multi-variable scenario is adopted to control two outputs: mean arterial blood pressure (MABP) and cardiac output (CO) simultaneously. Three fractional-order controllers are designed and tuned optimally for the multi-input multi-output (MIMO) blood pressure regulation system. To test the effectiveness of the designed fractional controllers, control investigations are carried out based on controller performance indices and sensitivity performance indices. Stability analysis and sensitivity analysis are carried out in order to assure stable as well as robust feedback design. Sensitivity analysis of the paper reveals the controller's ability to handle model uncertainties of the blood pressure regulation system. Numerical simulation results of the paper unfold the best suitable fractional-order controller for the enhanced closed-loop performance of the blood pressure regulation system.

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