Automatic Anesthesia Depth Control System For Isoflurane Concentration

Mehr, Akasha; Shah, Syed Irtiza Ali; Shaban, Muhammad; Shahid, Muhammad Badar; Awais, Malik Muhammad

doi:10.1109/ibcast54850.2022.9990208

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…3,2024 DOI: 10.22266/ijies2024.0630.48 monitoring of their potentially harmful symptoms are distinct. In theory, a closed-loop controller might be used to generate a personalized general anesthetic without the need for expensive and time-consuming hereditary pre-screening [2]. Regarding the amount of duties they complete each day, the number of anesthesiologists has increased in recent years.…”

Section: Introductionmentioning

confidence: 99%

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

2024

IJIES

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This paper presents an adaptive neural network acting like a proportional-integral-derivative (PID) controller that uses an intelligent meta-heuristic technique to improve the drug infusion rate (propofol) as a manipulated variable in closed-loop control of anesthesia systems using the Bispectral Index (BIS) as the primary controlled variable. The effect of propofol on the human body is modelled using the pharmacokinetic (PK) and pharmacodynamics (PD) models. A physiological dataset of patients, including gender, weight, height, age, and the like, determines the parameters of the PK/PD mathematical model. The proposed controller seeks to provide the optimal propofol control action, which is in charge of swiftly, precisely, and accurately maintaining a triad of hypnosis, analgesia, and neuromuscular blockade by infusing several drugs that are specific to each state. To train this neural network like a PID controller with the radial basis function (RBF) in a neuron, the meta-heuristic method is employed. The first technique is particle swarm optimization (PSO), which has been widely used in both data estimation and training because of its quick computing speed, while the second technique is the chaotic PSO algorithm, and the third technique is the modified CPSO algorithm (MCPSO). The fundamental proposed procedures of the MCPSO algorithm use the chaos method, including the coefficients of acceleration, and remove the two random parameters from the velocity update equation to generate more randomness in the search space to quickly solve the local minima problem. The PSO, CPSO, and MCPSO meta-heuristic algorithms use the mean square error (MSE) performance index to find and optimize the optimal or the nearly ideal gain parameters of the nonlinear neural network to function like a PID controller. The simulation results show that the proposed controller for different physiological dataset patients is characterized by its efficacy and resilience in terms of controlling the depth of the hypnosis state and the infusion rate of the anesthetic drug during surgery in order to avoid under-or over-dosing of the drug for the patient through the desired value of BIS (50) with minimizing the steady-state error, which is equal to zero without any oscillation. Moreover, the comparison results showed that the proposed RBF-NN-PID controller enhanced the time in one minute to reach the depth of anesthesia at the moderate hypnotic state when compared to the fractional-order adaptive highgain controller, in which the time to reach the depth of anesthesia is two minutes. In contrast, the adaptive neuro-fuzzy controller reached the depth of anesthesia in three minutes. Therefore, the time was improved by 50% and 67%, respectively. In particular, the surgery BIS index was kept at the BIS desired 50 at the moderate hypnotic state without any error and with no oscillation at steady-state.

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Section: Introductionmentioning

confidence: 99%

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

2024

IJIES

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“…However, with the fast advancement of automation in medical sciences, there is a growing interest in automating the anesthetic delivery process. An automated anesthetic depth control system for isoflurane concentration was suggested by Mehr et al in 2022, however, it did not sufficiently solve issues with heart monitoring [2]. Since anesthetic medications vitally alter heart rate and rhythm, this restriction may pose dangers.…”

Section: Introductionmentioning

confidence: 99%

Predefined Logics Based Anesthesia Delivery Module with Vital Monitoring

Mukhtiar,

Shabbir,

Liaquat

et al. 2023

JAET

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Anesthesia management is crucial during surgeries and in the intensive care unit to keep the patient unconscious and pain-free. The inaccurate dose may lead the patient to a critical condition and subsequently to a lifelong unconsciousness. Unlike manual system for anesthesia delivery, the automatic mechanism of drug administration may reduce the occurrence of human error greatly. Additionally, the use of vital sign monitoring during drug delivery may serve a crucial role in patient care during certain medical procedures. The anesthetist delivers the drug in milliliters over a specific time period precisely and this may lead to human error. Therefore, this work focuses on the design and development of an automatic anesthesia regularization system using microcontroller allowing vital sign monitoring to control the infusion speed of the medicine based on the patient's state. Different sensors are used to monitor heart rate, oxygen saturation, and temperature of the patient continuously and an alarm system is incorporated to go off in case of emergency. The drug dose must be known beforehand and is used as the predefined amount to configure and control data. The prototype has 4 pre-defined logics for easy operation and dose selection. When the logics are applied the device behaves accordingly, the system is driven by the stored logics and delivers the dose timely and precisely. In case of abnormal vitals, the device stops further delivery of the drug until the condition of the patient becomes stable again. The prototype successfully meets the demand of the algorithm by satisfying the conditions whilst the automation is useful in providing ease to anesthetists and comfort to patients during medical procedures. The result of this prototype extracted by comparing this model with commercially available MMED portable patient monitor and it has been observed that the system accuracy is around 95-98% as it’s a prototype that’s why initial findings are reported in this paper.

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Automatic Anesthesia Depth Control System For Isoflurane Concentration

Cited by 2 publications

References 19 publications

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

Predefined Logics Based Anesthesia Delivery Module with Vital Monitoring

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