Evaluation of an Anesthesia Dashboard Functional Model Based on a Manufacturer-Independent Communication Standard: Comparative Feasibility Study

Background

Current anesthesia workspaces consist of several technical devices, such as patient monitors, anesthesia machines, among others. Commonly, they are produced by different manufacturers; thus, they differ in terms of their modus operandi, user interface, and representation of alarms. Merging the information from these devices using a single joint protocol and displaying it in a single graphical user interface could lead to a general improvement in perioperative management. For this purpose, the recently approved and published Institute of Electrical and Electronics Engineers 11073 service-oriented device connectivity standard was implemented.

Objective

This paper aims to develop and then evaluate an anesthesia workstation (ANWS) functional model in terms of usability, fulfillment of clinical requirements, and expected improvements in patient safety.

Methods

To compare the self-developed ANWS with the conventional system, a pilot observational study was conducted at the University Hospital Aachen, Germany. A total of 5 anesthesiologists were asked to perform different tasks using the ANWS and then the conventional setup. For evaluation purposes, response times were measured and an interaction-centered usability test with an eye-tracking system was carried out. Finally, the subjects were asked to fill in a questionnaire in order to measure user satisfaction.

Results

Response times were significantly higher when using the ANWS, but decreased considerably after one repetition. Furthermore, usability was rated as excellent (≥95) according to the System Usability Scale score, and the majority of clinical requirements were met.

Conclusions

In general, the results were highly encouraging, considering that the ANWS was only a functional model, as well as the lack of training of the participants. However, further studies are necessary to improve the universal user interface and the interplay of the various networked devices.

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

Interval type-2 fuzzy neural network controller for a multivariable anesthesia system based on a hardware-in-the-loop simulation

OBJECTIVE

This manuscript describes the use of a hardware-in-the-loop simulation to simulate the control of a multivariable anesthesia system based on an interval type-2 fuzzy neural network (IT2FNN) controller.

METHODS AND MATERIALS

The IT2FNN controller consists of an interval type-2 fuzzy linguistic process as the antecedent part and an interval neural network as the consequent part. It has been proposed that the IT2FNN controller can be used for the control of a multivariable anesthesia system to minimize the effects of surgical stimulation and to overcome the uncertainty problem introduced by the large inter-individual variability of the patient parameters. The parameters of the IT2FNN controller were trained online using a back-propagation algorithm.

RESULTS

Three experimental cases are presented. All of the experimental results show good performance for the proposed controller over a wide range of patient parameters. Additionally, the results show better performance than the type-1 fuzzy neural network (T1FNN) controller under the effect of surgical stimulation. The response of the proposed controller has a smaller settling time and a smaller overshoot compared with the T1FNN controller and the adaptive interval type-2 fuzzy logic controller (AIT2FLC). The values of the performance indices for the proposed controller are lower than those obtained for the T1FNN controller and the AIT2FLC.

CONCLUSION

The IT2FNN controller is superior to the T1FNN controller for the handling of uncertain information due to the structure of type-2 fuzzy logic systems (FLSs), which are able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of the FLSs.

MUSCLE RELAXATION CONTROLLED BY AUTOMATED ADMINISTRATION OF CISATRACURIUM

In this paper, the muscle relaxant agent (i.e., cisatracurium) and three clinical control methods (i.e., 13 patients undergoing intermitted bolus control, 15 patients undergoing intensive manual control and 15 patients) undergoing automatic fuzzy logic control (FLC), were used for maintaining depth of muscle relaxation (DOM) during surgery. Cisatracurium, a muscle relaxation drug with long-term effect, low metabolic loading, but long delay time, is widely used in operating rooms and ICUs. Meanwhile, the rules for the FLC were developed from the experimental experience of intensive manual control after learning from 15 patient trials. According to experts' experimental experience, our FLC inputs were chosen from T1% error and trend of T1% which differ from other previous studies on eliminating the effect of time delay from cisatracurium. In individual clinical experimental results, the mean(SD) of the mean T1% error in 13 patients for intermitted bolus control, in 15 patients for intensive manual control, and in 15 patients for automatic control was 8.76(1.46), 1.65(1.67), and 0.48(1.43), respectively. The t test results show that automatic control is not significantly different from intensive manual control. The results show that a simple fuzzy logic controller derived from anesthetists ' clinical trials can provide good accuracy without being affected by the pharmacological time delay problem.

Addressing the flaws of current critical alarms: a fuzzy constraint satisfaction approach

OBJECTIVES

Threshold alarms, the support supplied by commercial monitoring devices to supervise the signs that pathologies produce over physiological variables, generate a large amount of false positives, owing to the high number of artifacts in monitoring signals, and they are not capable of satisfactorily representing and identifying all monitoring criteria used by healthcare staff. The lack of an adequate support for monitoring the evolution of physical variables prevents the suitable exploitation of the information obtained when monitoring critical patients. This work proposes a solution for designing intelligent alarms capable of addressing the flaws and limitations of threshold alarms.

MATERIALS AND METHODS

The solution proposed is based on the multivariable fuzzy temporal profile (MFTP) model, a formal model for describing certain monitoring criteria as a set of morphologies defined over the temporal evolution of the patient's physiological variables, and a set of relations between them. The MFTP model represents these morphologies through a network of fuzzy constraints between a set of points in the evolution of the variables which the physician considers especially relevant. We also provide a knowledge acquisition tool, TRACE, with which clinical staff can design and edit alarms based on the MFTP model.

RESULTS

Sixteen alarms were designed using the MFTP model; these were capable of supervising monitoring criteria that could be satisfactorily supervised with commercial monitoring devices. The alarms were validated over a total of 196h of recordings of physiological variables from 78 different patients admitted to an intensive care unit. Of the 912 alarm triggerings, only 7% were false positives. A study of the usability of the tool TRACE was also carried out. After a brief training seminar, five physicians and four nurses designed a number of alarms with this tool. They were then asked to fill in the standard System Usability Scale test. The average score was 68.2.

CONCLUSION

The proposal presented herein for describing monitoring criteria, comprising the MFTP model and TRACE, permits the supervision of monitoring criteria that cannot be represented by means of thresholds, and makes it possible to construct alarms that give a rate of false positives far below that for threshold alarms.

A novel fuzzy pain demand index derived from patient-controlled analgesia for postoperative pain

A multilayer hierarchical structure for an intelligent analysis system is described in this paper. Four levels (patients', measurement, Web-based, and interpreting) are used to collect massive amounts from clinical information and analyze it with both traditional and artificial intelligent methods. To support this, a novel fuzzy pain demand (FPD) index derived from the interval of each bolus of patient-controlled analgesia (PCA) is designed and documented in a large-scale clinical survey. The FPD index is modeled according to a fuzzy modeling algorithm to interpret the self-titration of the drug delivery. A total of 255 patients receiving intravenous PCA using morphine (1 mg/ml) tested this index by offline analysis from this system. We found the FPD index modeled from a fuzzy modeling algorithm to interpret the self-titration of the drug delivery can show the patients' dynamic demand and past efforts to overcome the postoperative pain. Moreover, it could become an online system to monitor patients' demand or intent to treat their pain so these factors could be entered into a patient's chart along with temperature, blood pressure, pulse, and respiration rates when medical practitioners check the patients.

Comparison of the applicability of rule-based and self-organizing fuzzy logic controllers for sedation control of intracranial pressure pattern in a neurosurgical intensive care unit

This paper assesses the controller performance of a self-organizing fuzzy logic controller (SOFLC) in comparison with a routine clinical rule-base controller (RBC) for sedation control of intracranial pressure (ICP) pattern. Eleven patients with severe head injury undergoing different neurosurgeries in a neurosurgical intensive care unit (NICU) were divided into two groups. In all cases the sedation control periods lasted 1 h and assessments of propofol infusion rates were made at a frequency of once per 30 s. In the control group of 10 cases selected from 5 patients, a RBC was used, and in the experimental group of 10 cases selected from 6 patients, a self-organizing fuzzy logic controller was used. A SOFLC was derived from a fuzzy logic controller and allowed to generate new rules via self-learning beyond the initial fuzzy rule-base obtained from experts (i.e., neurosurgeons). The performance of the controllers was analyzed using the ICP pattern of sedation for 1 h of control. The results show that a SOFLC can provide a more stable ICP pattern by administering more propofol and changing the rate of delivery more often when rule-base modifications have been considered.

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.

DESIGN A HIERARCHICAL SYSTEM FOR MONITORING MOBILITY CHANGES OF THE ELDERLY USING INTELLIGENT ANALYSIS

The application of microchip technology and an intelligent analysis algorithm to design a hierarchical system for monitoring mobility changes of the elderly at house is proposed in this paper. There are four levels which are environment, measurement & sensing, signal processing, and intelligent analyses to build for interpreting the mobility index of daily life according to three different sensors, such as infrared (IR) sensors for detecting human movement, mechanical switch sensors for identifying opening and closing of doors of the bookcases, and current sensors for detecting use of computer and lights. Hence, a smart box has been designed as a passive monitoring system (i.e., non-consciousness for human) where data is gathered from a number of physically distributed sensor points within the house, linked using the mains wiring as the communications medium, and then automatically transmitted over the modem system via internet to a monitoring and supervisory centre. The preliminary study has been tested successfully in volunteer's research room during six weeks in comparison with the questionnaire results. Hence, it can be seen as a demonstration of feasibility of the applicability of microchip technology and an intelligent analysis algorithm for monitoring mobility changes of the elderly at home. But, it still needs a longer series of real testing at home, perhaps to refine the rule-base, and certainly to see how widely they are applicable.

Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms. Part I. Classification of depth of anaesthesia and development of a patient model

OBJECTIVE

The first part of this research relates to two strands: classification of depth of anaesthesia (DOA) and the modelling of patient's vital signs.

METHODS AND MATERIAL

First, a fuzzy relational classifier was developed to classify a set of wavelet-extracted features from the auditory evoked potential (AEP) into different levels of DOA. Second, a hybrid patient model using Takagi-Sugeno Kang fuzzy models was developed. This model relates the heart rate, the systolic arterial pressure and the AEP features with the effect concentrations of the anaesthetic drug propofol and the analgesic drug remifentanil. The surgical stimulus effect was incorporated into the patient model using Mamdani fuzzy models.

RESULTS

The result of this study is a comprehensive patient model which predicts the effects of the above two drugs on DOA while monitoring several vital patient's signs.

CONCLUSION

This model will form the basis for the development of a multivariable closed-loop control algorithm which administers "optimally" the above two drugs simultaneously in the operating theatre during surgery.

Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia

Based on an adaptive genetic fuzzy clustering algorithm, a derived fuzzy knowledge model is proposed for quantitatively estimating the systolic arterial pressure (SAP), heart rate (HR), and bispectral index (BIS) using 12 patients and it validates them according to pharmacological reasoning. Also, a genetic proportional integral derivative controller (GPIDC) to adaptive three controller parameters and a genetic fuzzy logic controller (GFLC) to adaptive controller rules using genetic algorithms (GAs) were simulated and compared each other in a patient model using the BIS value as a controlled variable. Each controller was tested using a set of 12 virtual patients undergoing a Gaussian random surgical disturbance repeated with BIS targets set at 40, 50, and 60. Controller performance was assessed using mean absolute error (MAE) of the BIS target, the percentage of time with acceptable BIS control (PTABC), and drug consumption (DC). It was found that the MAE value of the BIS target was significantly lower (P < 0.05) and the values of PTABC and DC of BIS target were significantly higher (P < 0.05) in BIS targets set at 40 than at 50 or 60 in both GPIDC and GFLC. However, when compared with two controllers in terms of the values of MAE, PTABC, and DC each other in BIS targets set at 40, 50, and 60, there were no significant differences (P > 0.05). Furthermore, when the simulation results in these two controllers were compared with routine standard practice of 12 clinical trials (i.e., manual control) in BIS target set at 50, the values of PTABC in both GPIDC and GFLC groups were significantly higher (P < 0.05) than in the manual control group. In contrast, there were no significant differences (P > 0.05) for these three groups in terms of drug consumption. This indicates that either GPIDC or GFLC can control the BIS target set at 50 better than manual control, although the similar drug consumption is used.

Evaluation of glucose controllers in virtual environment: methodology and sample application

OBJECTIVE

Adaptive systems to deliver medical treatment in humans are safety-critical systems and require particular care in both the testing and the evaluation phase, which are time-consuming, costly, and confounded by ethical issues. The objective of the present work is to develop a methodology to test glucose controllers of an artificial pancreas in a simulated (virtual) environment.

MATERIAL AND METHODS

A virtual environment comprising a model of the carbohydrate metabolism and models of the insulin pump and the glucose sensor is employed to simulate individual glucose excursions in subjects with type 1 diabetes. The performance of the control algorithm within the virtual environment is evaluated by considering treatment and operational scenarios.

RESULTS

The developed methodology includes two dimensions: testing in relation to specific life style conditions, i.e. fasting, post-prandial, and life style (metabolic) disturbances; and testing in relation to various operating conditions, i.e. expected operating conditions, adverse operating conditions, and system failure. We define safety and efficacy criteria and describe the measures to be taken prior to clinical testing. The use of the methodology is exemplified by tuning and evaluating a model predictive glucose controller being developed for a wearable artificial pancreas focused on fasting conditions.

CONCLUSION

Our methodology to test glucose controllers in a virtual environment is instrumental in anticipating the results of real clinical tests for different physiological conditions and for different operating conditions. The thorough testing in the virtual environment reduces costs and speeds up the development process.

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.