Juan Albino Méndez Pérez

Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries.

This paper presents a new fault detection system in hypnotic sensors used for general anesthesia during surgery. Drug infusion during surgery is based on information received from patient monitoring devices; accordingly, faults in sensor devices can put patient safety at risk. Our research offers a solution to cope with these undesirable scenarios. We focus on the anesthesia process using intravenous propofol as the hypnotic drug and employing a Bispectral Index (BISTM) monitor to estimate the patient’s unconsciousness level. The method developed identifies BIS episodes affected by disturbances during surgery with null clinical value. Thus, the clinician—or the automatic controller—will not take those measures into account to calculate the drug dose. Our method compares the measured BIS signal with expected behavior predicted by the propofol dose provider and the electromyogram (EMG) signal. For the prediction of the BIS signal, a model based on a hybrid intelligent system architecture has been created. The model uses clustering combined with regression techniques. To validate its accuracy, a dataset taken during surgeries with general anesthesia was used. The proposed fault detection method for BIS sensor measures has also been verified using data from real cases. The obtained results prove the method’s effectiveness.

Modeling the Electromyogram (EMG) of Patients Undergoing Anesthesia During Surgery

All fields of science have advanced and still advance significantly. One of the facts that contributes positively is the synergy between areas. In this case, the present research shows the Electromyogram (EMG) modeling of patients undergoing to anesthesia during surgery. With the aim of predicting the patient EMG signal, a model that allows to know its performance from the Bispectral Index (BIS) and the Propofol infusion rate has been developed. The proposal has been achieved by using clustering combined with regression techniques and using a real dataset obtained from patients undergoing to anesthesia during surgeries. Finally, the created model has been tested with very satisfactory results.

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine

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.

An Intelligent Model to Predict ANI in Patients Undergoing General Anesthesia

One of the main challenges in anesthesia is the proposal of safe and efficient methods to administer drugs to regulate the pain that the patient is sufffering during the surgical process. First steps towards this objective is the proposal of adequate indexes that correlate well with analgesia. One of the most promising index is ANI (Antinociception Index). This research focuses on the modelling of the ANI response in patients undergoing general anesthesia with intravenous drug infusion. The aim is to predict the ANI response in terms of the analgesic infusion rate. For this a model based on intelligent regression techniques is proposed. To create the model, it has been checked Artificial Neural Networks (ANN) and Support Vector Regression (SVR). Results were validated using data from patients in the operating room. The measured performance attest for the potential of the proposed technique.

A Machine Learning Based System for Analgesic Drug Delivery

Monitoring pain and finding more efficient methods for analgesic administration during anaesthesia is a challenge that attracts the attention of both clinicians and engineers. This work focuses on the application of Machine Learning techniques to assist the clinicians in the administration of analgesic drug. The problem will consider patients undergoing general anaesthesia with intravenous drug infusion. The paper presents a preliminary study based on the use of the signal provided by an analgesia monitor, the Analgesia Nociception Index (ANI) signal. One aim of this research is studying the relation between ANI monitor and the changes in drug titration made by anaesthetist. Another aim is to propose an intelligent system that provides decisions on the drug infusion according to the ANI evolution. To do that, data from 15 patients undergoing cholecystectomy surgery were analysed. In order to establish the relationship between ANI and the analgesic, Machine Learning techniques have been introduced. After training different types of classifier and testing the results with cross validation method, it has been demonstrated that a relation between ANI and the administration of remifentanil can be found.

Hybrid model for the ANI index prediction using Remifentanil drug and EMG signal

With the aim to control and reduce the pain of patients during a surgery with general anesthesia, one of the main challenges is the proposal of safe an optimal and efficient methods of drugs administering. First step to achieve this goal is the proposal and development of right indexes that correlate satisfactory with analgesia. One of this index gives the most hopeful results is the Analgesia Nociception Index (ANI). The present research work deals the ANI response of patients during surgeries with general anesthesia with intravenous drug infusion. The main aim is to predict the ANI signal behavior regarding of the analgesic infusion rate. To do that, a hybrid intelligent model is developed, using clustering and regression techniques based on artificial neural networks and support vector regression. The proposal was validated with a dataset of surgeries real cases of patients undergoing general anesthesia. The achieved results attest for the potential of the proposed technique.

An Intelligent Model for Bispectral Index (BIS) in Patients Undergoing General Anesthesia

Nowadays, the engineering tools play an important role in medicine, regardless of the area. The present research is focused in anesthesiology, specifically on the behavior of sedated patients. The work shows the Bispectral Index Signal (BIS) modeling of patients undergoing general anesthesia during surgery. With the aim of predicting the patient BIS signal, a model that allows to know its performance from the Electromyogram (EMG) and the propofol infusion rate has been created. The proposal has been achieved by using clustering combined with regression techniques and using a real dataset obtained from patients undergoing general anesthesia. Finally, the created model has been tested also with data from real patients, and the results obtained attested the accuracy of the model.