Measuring the level of analgesia to adapt the opioids infusion during anesthesia to the real needs of the patient is still a challenge. This is a consequence of the absence of a specific measure ...capable of quantifying the nociception level of the patients. Unlike existing proposals, this paper aims to evaluate the suitability of the Analgesia Nociception Index (ANI) as a guidance variable to replicate the decisions made by the experts when a modification of the opioid infusion rate is required. To this end, different machine learning classifiers were trained with several sets of clinical features. Data for training were captured from 17 patients undergoing cholecystectomy surgery. Satisfactory results were obtained when including information about minimum values of ANI for predicting a change of dose. Specifically, a higher efficiency of the Support Vector Machine (SVM) classifier was observed compared with the situation in which the ANI index was not included: accuracy: 86.21% (83.62%–87.93%), precision: 86.11% (83.78%–88.57%), recall: 91.18% (88.24%–91.18%), specificity: 79.17% (75%–83.33%), AUC: 0.89 (0.87–0.90) and kappa index: 0.71 (0.66–0.75). The results of this research evidenced that including information about the minimum values of ANI together with the hemodynamic information outperformed the decisions made regarding only non-specific traditional signs such as heart rate and blood pressure. In addition, the analysis of the results showed that including the ANI monitor in the decision making process may anticipate a dose change to prevent hemodynamic events. Finally, the SVM was able to perform accurate predictions when making different decisions commonly observed in the clinical practice.
•A machine learning-based analysis of the ANI monitor for guiding opioid titration during general anesthesia is presented.•A non-invasive clinical scheme is proposed for the validation of the ANI monitor.•Including the ANI in the decision making outperforms the accuracy of the predictions based on non-specific clinical signs.•This study evidences the capability of the ANI to prevent mistakes during opioids titration.
Abstract
Automatic control of physiological variables is one of the most active areas in biomedical engineering. This paper is centered in the prediction of the analgesic variables evolution in ...patients undergoing surgery. The proposal is based on the use of hybrid intelligent modelling methods. The study considers the Analgesia Nociception Index (ANI) to assess the pain in the patient and remifentanil as intravenous analgesic. The model proposed is able to make a one-step-ahead prediction of the remifentanil dose corresponding to the current state of the patient. The input information is the previous remifentanil dose, the ANI variable and the electromyogram signal. Modelling techniques used are Artificial Neural Networks and Support Vector machines for Regression combined with clustering methods. Both training and validation were done with a real dataset from different patients. Results obtained show the potential of this methodology to calculate the drug dose corresponding to a given analgesic state of the patient.
•An optimization-based algorithm is proposed to synthesize the adaptive model.•A PK-PD structure for additive drug interaction is considered.•The parametric model deals with patient and process ...variabilities.•Simulation results evidence the benefits of using adaptive models in anesthesia.
The availability of accurate models for predicting the drug effect in patients undergoing general anesthesia is an important factor in producing a personalized drug infusion. These models should consider different clinical factors to provide realistic predictions. This paper proposes a new methodology for modeling the depth of hypnosis (DOH) during anesthesia. The model, which is based on a pharmacokinetic–pharmacodynamic structure, explicitly takes into account the interaction between the hypnotic and opioid drugs delivered during surgery. Patients undergoing general surgery with intravenous propofol–remifentanil anesthesia were considered. The bispectral index (BIS) was used for monitoring the DOH. In contrast with previous research, the uniqueness of this study lies in the proposal of an adaptive model to deal simultaneously with the variabilities in the clinical response of the patients, the drug interactions, and the variable time delay introduced by the BIS monitor. The proposed method was validated using data from 17 patients undergoing general anesthesia. Successful results were obtained for predicting the evolution of BIS during the induction and maintenance phases of propofol–remifentanil anesthesia. Specifically, the convenience of an adaptive model that included all the factors likely to affect the anesthetic process was demonstrated. The proposed methodology can be used for the development of new models to be employed in model predictive control strategies for closed-loop anesthesia.
Abstract
This work focuses on the application of machine learning techniques to assist the clinicians in the administration of analgesic drug during general anaesthesia. Specifically, the main ...objective is to propose the basis of an intelligent system capable of making decisions to guide the opioid dose changes based on a new nociception monitor, the analgesia nociception index (ANI). Clinical data were obtained from 15 patients undergoing cholecystectomy surgery. By means of an off-line study, machine learning techniques were applied to analyse the possible relationship between the analgesic dose changes performed by the physician due to the hemodynamic activity of the patients and the evolution of the ANI. After training different classifiers and testing the results under cross validation, a preliminary relationship between the evolution of ANI and the dosage of remifentanil was found. These results evidence the potential of the ANI as a promising index to guide the infusion of analgesia.
The problem of automating the infusion of anesthesia using fuzzy predictive control techniques is afforded. The control objective is to keep the hypnosis level of the patient in a proper and safe ...value. To provide accurate predictions, an adaptive model based on fuzzy logic and genetic algorithms is included. Thus, the drug infusion is adapted to the real needs of the patient and, consequently, the performance compared to other approaches is improved. The controller was evaluated both in simulation and in the operating room with patients undergoing surgery. Results obtained attest for the efficiency of the proposed method.
One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions ...based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.
In this paper, a new method for characterizing the dielectric breakdown voltage of dielectric oils is presented, based on the IEC 60156 international standard. In this standard, the effective value ...of the dielectric breakdown voltage is obtained, but information is not provided on the distribution of Kelvin forces an instant before the dynamic behavior of the arc begins or the state of the gases that are produced an instant after the moment of appearance of the electric arc in the oil. In this paper, the behavior of the oil before and after the appearance of the electric arc is characterized by combining a low-cost CMOS imaging sensor and a new matrix of electrical permittivity associated with the dielectric oil, using the 3D cell method. In this way, we also predict the electric field before and after the electric rupture. The error compared to the finite element method is less than 0.36%. In addition, a new method is proposed to measure the kinematic viscosity of dielectric oils. Using a low-cost imaging sensor, the distribution of bubbles is measured, together with their diameters and their rates of ascent after the electric arc occurs. This method is verified using ASTM standards and data provided by the oil manufacturer. The results of these tests can be used to prevent incipient failures and evaluate preventive maintenance processes such as transformer oil replacement or recovery.
Modelling the PSI response in general anesthesia Pérez, Gerardo Alfonso; Pérez, Juan Albino Méndez; Álvarez, Santiago Torres ...
Journal of clinical monitoring and computing,
10/2021, Letnik:
35, Številka:
5
Journal Article
Recenzirano
In anesthesia automation, one of the main important issues is the availability of a reliable measurement of the depth of consciousness level (hypnosis) of the patient. According to this value, the ...hypnotic drug dosage can be adequately calculated. One of the most studied hypnosis indexes is the bispectral index (BIS). In this article we analyzed an alternative called patient state index (PSI). The objectives of this study are, first, to validate the accuracy of the PSI describing the hypnosis level during the maintenance phase of general anesthesia, by comparing with the BIS and, second, to model the relationship between propofol infusion rate and PSI values, obtained from a SEDLine monitor. For this, real data from patients undergoing general anesthesia simultaneously monitored with both BIS and PSI signals was used. Results obtained are interesting for a correct interpretation of PSI signal in clinical practice.
•It is possible to translate heuristic knowledge provided by clinicians into a computer system to automate drug delivery in patients undergoing general anesthesia.•A two-level computer decision ...system is proposed in order to separate a supervision level and a control level.•The proposed system performed satisfactorily in cases considered, maintaining the hypnosis level of the patient in the desired target and showing better performance parameter values than the manual standard procedure.
The main objective of this research is the design and implementation of a new fuzzy logic tool for automatic drug delivery in patients undergoing general anesthesia. The aim is to adjust the drug dose to the real patient needs using heuristic knowledge provided by clinicians. A two-level computer decision system is proposed. The idea is to release the clinician from routine tasks so that he can focus on other variables of the patient.
The controller uses the Bispectral Index (BIS) to assess the hypnotic state of the patient. Fuzzy controller was included in a closed-loop system to reach the BIS target and reject disturbances. BIS was measured using a BIS VISTA monitor, a device capable of calculating the hypnosis level of the patient through EEG information. An infusion pump with propofol 1% is used to supply the drug to the patient. The inputs to the fuzzy inference system are BIS error and BIS rate. The output is infusion rate increment. The mapping of the input information and the appropriate output is given by a rule-base based on knowledge of clinicians.
To evaluate the performance of the fuzzy closed-loop system proposed, an observational study was carried out. Eighty one patients scheduled for ambulatory surgery were randomly distributed in 2 groups: one group using a fuzzy logic based closed-loop system (FCL) to automate the administration of propofol (42 cases); the second group using manual delivering of the drug (39 cases). In both groups, the BIS target was 50.
The FCL, designed with intuitive logic rules based on the clinician experience, performed satisfactorily and outperformed the manual administration in patients in terms of accuracy through the maintenance stage.
Adaptive computer control of anesthesia in humans Méndez, Juan Albino; Torres, Santiago; Reboso, José Antonio ...
Computer methods in biomechanics and biomedical engineering
12, Številka:
6
Journal Article
Recenzirano
This paper presents an efficient computer control technique for regulation of anesthesia in humans. The anesthetic used is propofol and the objective is to control the degree of hypnosis of the ...patient. The paper describes the basic hardware/software setup of the system and the closed-loop methodologies. The bispectral index (BIS) is considered as the feedback signal. The control methods proposed here are based in the use of proportional integral controllers with dead-time compensation to avoid undesirable oscillations in the BIS signal during the process. The compensation is based on the Smith predictor. To guarantee the applicability of the method to different patients, an adaptive module to tune the compensator is developed. Some real and simulated results are presented in this work to attest the efficiency of the methods used.
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