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

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

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.

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part II. Clinical implementation and evaluation

OBJECTIVE

Patients emerging from cardiac surgery can display varying degrees of cardiovascular instability arising from potentially complex, multi-factorial and interlinked causes. Stabilization and control of the cardiovascular system are currently managed by healthcare experts using experiential knowledge, and, in some centers, manually inputted decision pathway algorithms. This paper describes a clinical trial undertaken to determine the basic functioning of a clinical decision support system (CDSS) designed and constructed by the authors to facilitate the control of the major cardiovascular components in the early post-operative phase. Part II follows Part I's description of the software and simulation testing of the CDSS, and describes the hardware setup of a patient monitoring and CDSS. The system is evaluated on three post-cardiac surgery intensive care patients whom had all undergone cardio-pulmonary bypass.

METHODS

The study was approved by the Sheffield Teaching Hospitals National Health Service (NHS) Foundation Trust Research Ethics Committee and conducted at the North Trent cardio-thoracic surgical unit and cardiac intensive care unit (CICU), Northern General Hospital, Sheffield (UK). Patients considered as 'very likely' to require active intervention to support the cardiovascular function following routine cardiac surgery were recruited during pre-operative surgical and anesthetic assessment, giving written informed consent when admitted for their operation. These patients underwent routine induction and maintenance of anesthesia by a non-study consultant anesthetist and the operation performed. There were no restrictions placed on the types of invasive monitoring used, on the use of trans-oesophageal echocardiography, drug selection, or the anesthetic agents selected by the clinicians performing the operations. All patients had full, routine invasive and non-invasive monitoring applied, including electrocardiography, central venous and peripheral arterial catheterisation, urine outputs and central temperature. After chest closure the patients were transferred to the CICU, sedated and ventilated, and the study commenced by the study anesthetist (1st author). The patients were in a clinically stable condition when admitted to the unit, and were attended by the treating clinicians until the handover to the study anesthetist occurred. The LiDCOplus (lithium dilution cardiac output) monitor (LiDCO Limited, Flowers Building, Granta Park, Cambridge CB1 6GU, United Kingdom) was calibrated after attachment to the patient's arterial line, and the patient's beat-to-beat hemodynamic data transferred to the host laptop computer. The CDSS graphical interface displays the patient's clinical details and specific cardiovascular data and prompts the anesthetist to input the target ranges for each parameter, and select a suitable advisor frequency. This is the frequency with which the therapeutic advice is displayed on screen with an audible prompt for a control inputs from the anesthetist. In each case this was selected to be 30s. When the study anesthetist agreed with the CDSS advice (administration of fluid, commencing a drug, altering the drug infusion rate) the syringe motif on the "Advisor Infusion Rates" panel of the graphical interface was 'clicked' on and the infusion rate immediately and manually inputted to Graseby 3400 pumps. If any disagreement between the anesthetist and the computer's advice arose, the syringe motif on the "Expert Infusion Rates" panel of the preferred drug was 'clicked' on and the expert's therapeutic decision (e.g. infusion rate) was entered in the corresponding data field and then applied to the pump. During all trials, data was stored for off-line analysis.

RESULTS

The CDSS successfully selected suitable drug therapies for each case and advised reasonable and appropriate infusion rates such that the study anesthetist did not have to override the suggested CDSS instructions and infusion rates. Under differing clinical conditions the system was able to maintain clinically appropriate and stable control of the cardiovascular system (CVS), with good profiles under noisy physiological measurements, and was readily able to regain control following transient deterioration of the patient hemodynamic parameters (coughing, or during blood sampling).

Fuzzy logic control for intracranial pressure via continuous propofol sedation in a neurosurgical intensive care unit

The major goal of this paper is to provide automatically continuous propofol sedation for patients with severe head injury, unconsciousness, and mechanical ventilation in order to reduce the effect of agitation on intracranial pressure (ICP) using fuzzy logic control in a neurosurgical intensive care unit (NICU). Seventeen patients were divided into three groups in which control was provided with three different controllers. Experimental control periods were of 60min duration in all cases. Group A used a conventional rule-based controller (RBC), Group B a fuzzy logic controller (FLC), and Group C a self-organizing fuzzy logic controller (SOFLC). The performance of the controllers was analyzed by ICP pattern of sedation. The ICP pattern of errors was analyzed for mean and root mean square deviation (RMSD) for the entire duration of control (i.e., 1h). The results indicate that FLC can easily mimic the rule-base of human experts (i.e., neurosurgeons) to achieve stable sedation similar to the RBC group. Furthermore, the results also show that a SOFLC can provide more stable sedation of ICP pattern because it can modify the fuzzy rule-base to compensate for inter-patient variations.

Evaluation of a closed-loop muscle relaxation control system

Automatic muscle relaxation control may reduce anesthesiologists' workload freeing them for other patient care requirements. In this report we describe a muscle relaxation controller designed for routine clinical application using rocuronium and the train-of-four count. A muscle relaxation monitor (TOF Watch SX) was connected to a laptop computer running a controller algorithm program that communicates with a syringe pump to form a closed-loop muscle relaxation system. The control algorithm uses proportional-integral and lookup table components and is designed to avoid the usability restrictions of existing controllers. The controller is optimized using an objective method to avoid the uncertainties of ''hand-crafted'' controller algorithms. Controller target was train-of-four count 1 or 2 and controller performance was evaluated in 15 patients. During 39 hours of closed-loop control, 96.1% of all twitches recorded were in the target range. Average rocuronium infusion rate was 0.36 mg.kg(-1).h(-1) (sd 0.18 mg.kg(-1).h(-1)). We show that the controller remains useful even in the presence of disturbances that can arise in routine clinical conditions. The muscle relaxation controller maintained the target train-of-four count values and may serve as a basis for the design of hardware and user interfaces for closed-loop muscle relaxation control in clinical conditions.

Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block

OBJECTIVE

The important task for anaesthetists is to provide an adequate degree of neuromuscular block during surgical operations, so that it should not be difficult to antagonize at the end of surgery. Therefore, this study examined the application of a simple technique (i.e., fuzzy logic) to an almost ideal muscle relaxant (i.e., rocuronium) at general anaesthesia in order to control the system more easily, efficiently, intelligently and safely during an operation.

METHODS

The characteristics of neuromuscular blockade induced by rocuronium were studied in 10 ASA I or II adult patients anaesthetized with inhalational (i.e., isoflurane) anaesthesia. A Datex Relaxograph was used to monitor neuromuscular block. And, ulnar nerve was stimulated supramaximally with repeated train-of-four via surface electrodes at the wrist. Initially a notebook personal computer was linked to a Datex Relaxograph to monitor electromyogram (EMG) signals which had been pruned by a three-level hierarchical structure of filters in order to design a controller for administering muscle relaxants. Furthermore, a four-level hierarchical fuzzy logic controller using the fuzzy logic and rule of thumb concept has been incorporated into the system. The Student's test was used to compare the variance between the groups. p < 0.05 was considered significant.

RESULTS

The system achieved stable control of muscle relaxation with a mean T1% error of -0.19 (SD 0.66) % accommodating a range in mean infusion rate (MIR) of 0.21-0.49 mg x kg(-1) x h(-1). When these results were compared with our previous ones using the same hierarchical structure applied to mivacurium, less variation in the T1% error (p < 0.05) but the same variation in infusion rate were observed. The controller activity of these two drugs showed no significant difference (p > 0.5). However, the consistent medium coefficient variance (CV) of the MIR of both rocuronium (i.e., 36.13 (SD 9.35) %) and mivacurium (i.e., 34.03 (SD 10.76) %) indicated a good controller activity.

CONCLUSIONS

The results showed that a hierarchical rule-based monitoring and fuzzy logic control architecture can provide stable control of neuromuscular block despite the considerable individual variation in neuromuscular block required among patients. Also, there was less variation in T1% error compared with that of previous study on mivacurium. Meanwhile, the consistent medium CV of the MIR of both rocuronium and mivacurium indicated a good controller activity which is able to withstand noise, diathermy effect, artifacts and surgical disturbances.

Pain model and fuzzy logic patient-controlled analgesia in shock-wave lithotripsy

Pain control in conscious patients was investigated using a push-button, demand-driven supply of drugs. A fuzzy logic patient-controlled analgesia (PCA) algorithm was compared with a conventional algorithm, for alfentanil administration in extracorporeal shock-wave lithotripsy. The conventional PCA algorithm used an initial dose of 0.25mg, a fixed infusion rate of 60 mg h(-1) and a fixed bolus size of 0.2 mg with a 1 min lockout. The fuzzy logic PCA algorithm used an initial dose of 0.25 mg, a changeable infusion rate and a bolus size of 0.1 or 0.05 mg. The infusion rate was adjusted according to a look-up table that accepted the button-pressing history over the last three lockout intervals. The look-up table was designed using fuzzy logic. The bolus size was adjusted according to the button-pressing history over the past two lockout intervals. Twelve patients were treated using conventional PCA, and thirteen were treated with PCA + fuzzy logic control (FLC). PCA + FLC patients consumed 45% less drug. Also, PCA + FLC patients had a mean delivery/demand ratio of 82%, compared with 60% in conventional PCA. When the pain intensity scale was analysed, PCA + FLC patients had acceptable pain intensity at 62%, compared with 44% in conventional PCA.

Artificial intelligence applications in the intensive care unit

OBJECTIVE

To review the history and current applications of artificial intelligence in the intensive care unit.

DATA SOURCES

The MEDLINE database, bibliographies of selected articles, and current texts on the subject.

STUDY SELECTION

The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis.

DATA EXTRACTION

All literature relevant to the topic was reviewed.

DATA SYNTHESIS

Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control.

CONCLUSIONS

The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.