A hierarchical system of on-line advisory for monitoring and controlling the depth of anaesthesia using self-organizing fuzzy logic

Positive state observer for the automatic control of the depth of anesthesia—Clinical results

“…However, due to the high complexity of this procedure an automated system for drug administration would be a good support for the clinicians (see Meijler [6]). The development of controllers for the automatic administration of drugs in patients has deserved the attention of several researchers and led to a number of contributions and controllers namely a predictive control in Ionescu et al [7], an adaptive model-based controller in Mortier et al [8] and Simanski et al [9], a PID in Padula et al [10], a neural in Ortolani et al [11], a fuzzy logic in Shieh et al [12], a model predictive control in Sawaguchi et al [13] and Chang et al [14], but in these contributions the control of the DoA is not fully automatic. More concretely, the administration of the hypnotic is made automatically, but the administration of the analgesic is manually made by a clinician.…”

Type-2 fuzzy sets applied to multivariable self-organizing fuzzy logic controllers for regulating anesthesia

“…By stimulating the nerve the expected degree of neuromuscular block is determined based on EMG [33]. To measure BP previous studies have used a MP60 critical care patient monitor to measure patients' MAP at one minute intervals [21]. In our simulations Muscle relaxation percentage was normalized over a scale of 0-1 where the initial value of muscle relaxation was set at 0.…”

“…In the past two decades, there have been several studies on applying SOFLC to biomedical systems, such as muscle relaxation [19,20], depth of anesthesia [21], and patient analgesia control [22]. Controlling the delivery of anesthesia in operating theaters is possible using the multivariable SOFLC structure due to its ability to approximate flexible nonlinear control models which can be dynamically adapted for regulating desired physiological set points for muscle relaxation and unconsciousness (measured from BP).…”

Fuzzy Expert Systems (FES) for Medical Diagnosis

“…[96][97][98][99] Medical advisory systems for deciding the quantum of medicine, taking online measurements, monitoring, controlling of parameters are proposed. [100][101][102] Computer aided medical diagnosis, medical image processing, noise reduction in medical images, simulation and automated generation of fuzzy models and expert systems using generic methodology are the areas that alleviated FES to the new height and could provide a way of generating solutions based on stored data. [103][104][105][106][107][108][109][110] Medical data classification, data transformation system, feature extraction and investigation are the areas where improved performance has been reported.…”