David G. Mason

Automated delivery of muscle relaxants using fuzzy logic control

The authors feel that fuzzy logic controllers are a promising means for developing biocontrol systems. They help to account for uncertainty in the measured signal. They are also an easy way to implement a nonlinear controller to match significantly nonlinear and variable processes as commonly encountered in biomedical applications. Initial trials show this approach is suitable for clinical application. Clinical trials are continuing with the fuzzy PD+I and self-organizing fuzzy controllers using atracurium. Work will then commence to extend this sytem for use with other muscle relaxants such as mivacurium.<<ETX>>

Development of a portable closed-loop atracurium infusion system: systems methodology and safety issues.

Safety of closed-loop drug infusion systems is an issue often raised as a matter of concern. As a result, many closed-loop control systems are reported in the literature merely as computer simulation studies and few ever reach the stage of physical realisation and formal clinical evaluation. We address the safety issues involved with such systems by describing the development of a portable closed-loop control system for atracurium-induced muscle relaxation. This is a safety-critical system particularly when applied to brain and eye surgery where movement could have serious deleterious effects. The benefits of closed-loop muscle relaxation in providing stable surgical operating conditions over a wide range of patient sensitivities while infusing the minimum amount of drug makes this a worthwhile aim and serves to demonstrate safety issues which are generally applicable to other closed-loop drug infusion systems. It is hoped that the described methodology will facilitate and encourage the clinical application of closed-loop drug infusion systems so that clinical staff and patients may receive the benefits of closed-loop drug therapy.

Self-learning fuzzy control of atracurium-induced neuromuscular block during surgery

Self-learning fuzzy logic control has the important property of accommodating uncertain, non-linear and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time, without the need for detailed process modelling. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block (NMB) of patients in the operating theatre. Here, the patient response exhibits high non-linearity, and individual patient dose requirements can vary five-fold during an operating procedure. A portable control system was developed to assess the clinical performance of a simplified self-learning fuzzy controller in this application. A Paragraph (Vital Signs) NMB device monitored T1, the height of the first twitch in a train-of-four nerve stimulation mode. Using a T1 setpoint=10% of baseline in ten patients undergoing general surgery, a mean T1 error of 0.45% (SD=0.44%) is found while a 0.13–0.70 mg k−1 h−1 range in the mean atracurium infusion rate is accommodated. The result compares favourably with more complex and computationally-intensive model-based control strategies for the infusion of atracurium.

Self-learning fuzzy control with temporal knowledge for atracurium-induced neuromuscular block during surgery

Self-learning fuzzy logic control has the important property of accommodating uncertain, nonlinear, and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time without the need for detailed process modeling. In this study we utilize temporal knowledge of generated rules to improve control performance. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block of patients in the operating theater where the patient response exhibits high nonlinearity and individual patient dose requirements may vary fivefold during an operating procedure. We developed a computer control system utilizing Relaxograph (Datex) measurements to assess the clinical performance of a self-learning fuzzy controller in this application. Using a T1 setpoint of 10% of baseline in 10 patients undergoing general surgery, we found a mean T1 error of 0.28% (SD = 0.39%) while accommodating a 0.25 to 0.38 mg/kg/h range in the mean atracurium infusion rate. This result compares favorably with more complex and computationally intensive model-based control strategies for atracurium infusion.

Self-Learning Fuzzy Logic Control in Medicine

Self-learning fuzzy logic control has the important property of accommodating uncertain, non-linear and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real-time without the need for detailed process modelling. Medicine abounds with suitable applications for this technique. Following an outline of the methodology we demonstrate its clinical effectiveness for application in anaesthesia. We have investigated its application to atracurium-induced neuromuscular block during surgery and have observed improved control over complex numerical techniques. This self-learning fuzzy control technique shows much promise for other medical applications such as post-operative blood pressure management, intra-operative control of anaesthetic depth, and multivariable circulatory management of intensive care patients.

A portable self‐learning fuzzy logic control system for muscle relaxation

We have assessed the practicality and performance of the Vital Signs Paragraph neuromuscular blockade monitor as part of a ‘self‐learning’ fuzzy logic control feedback system used to administer atracurium to a required depth of neuromuscular blockade. Fifteen patients undergoing surgery expected to last longer than 90u2003min entered the study. A Vital Signs Paragraph was used to measure the degree of neuromuscular blockade and control it such that the first twitch of the train‐of‐four was kept at 10% of its baseline value. The controller instructed a Graseby Medical 3400 infusion pump to administer an atracurium infusion to maintain this level of blockade. Five patients (33%) were withdrawn from the study due to inadequate piezo‐electric sensor function. In the remaining 10 patients, the system achieved stable control of neuromuscular blockade with a mean (range) error for the first twitch of the train‐of‐four of −0.45u2003(−1.06 to 0.13)%. The mean atracurium infusion rate ranged from 0.13 to 0.67u2003mg.kg−1.h−1. These results compare reasonably well with previous results using the Datex Relaxograph, whilst the system itself was portable and easy to use. However, the reliability of the system was limited due to variability in the sensitivity of piezoelectric sensors.

Development of a pharmacokinetic model-based infusion system for ketamine analgesia.

Model-driven infusion systems in anaesthesia overcome the difficulties in obtaining on-line measurements of controlled variables. A linear pharmacokinetic model for ketamine was used to achieve target blood concentrations and was implemented using a palmtop PC. Although the use of ketamine for analgesia in total intravenous anaesthesia with propofol has been reported, this is the first such application to spontaneously breathing patients. Preliminary results show this to be a useful system, which may easily be applied to other intravenous anaesthetic agents.

Automated drug delivery in muscle relaxant anaesthesia using self-organizing fuzzy logic control

The general trend in muscle relaxant anaesthesia is for shorter acting agents which allow the patient to recover from paralysis quickly with minimal use of reversal agent. This necessitates continuous infusion schemes which must accommodate a wide range of patient characteristics. This paper investigates the application of self-organizing fuzzy logic control (SOFLC) to automated infusion of atracurium. This scheme offers a simple means to develop a nonlinear controller which quickly establishes the level of muscle relaxant required by each individual patient in a self-learning fashion. Preliminary results show this approach is suitable for clinical application.

Self-Learning Fuzzy Logic Control of Anaesthetic Intravenous Infusions

The first successful clinical application of self-learning fuzzy logic control is described. The novel control design allows each control session to commence with a completely blank rule base and perform on-line learning of the fuzzy control rules required for each patient presented to it. This direct adaptive controller utilises a performance index which gives a measure of the error from a pre-specified desired trajectory. This value is simply added to the consequent of previously on-line generated fuzzy control rules. The modified rule is added to the fuzzy rule base and included in fuzzy inferencing to determine the current fuzzy controller output value. The current controller output value is used to generate a control rule which will be modified later. This intelligent control scheme is suitable for uncertain, non-linear and time-varying processes and is therefore well-suited to medical applications. We have demonstrated the clinical utility of this control scheme in feedback control of muscle relaxation during surgery. We have also demonstrated the feasibility in adapting the control parameters for different muscle relaxants without the need for detailed process modelling or any computer simulation studies but relying on clinical experience and available drug data sheets. Our investigation has progressed to consider a more realistic clinical scenario with multiple measurements being used to manage multiple drug infusions; haemodynamic support of septic shock. Computer simulation studies show good feasibility for self-learning fuzzy logic control in this clinical application.