Surgeons, particularly those in training, work for a long period of time and are often sleep deprived [
In surgical field, virtual reality surgical simulation has been an alternative approach to examine effects of fatigue on performance [
20 healthy college students in the age range of 24 to 26 with correct visual acuity of 1.0 or more were recruited from University of Shanghai for Science and Technology. A lifestyle questionnaire was administered and used as a selection criterion which required the subjects to have no medical contraindications such as history of prior brain injuries, use of prescription medication, severe concomitant disease, drug abuse, or alcoholism as well as psychological or intellectual problems, which more likely limit compliance.
All subjects filled informed agreement form and none of them had previous experience with laparoscopic simulator. Likewise, all subjects received comprehensive instructions about the task described in Section
We designed a real-time monitoring system to evaluate and analyze effects of fatigue on efficiency and accuracy during laparoscopic simulation. The system consists of EEG data acquisition module, fatigue analysis module, Bluetooth communication module, and laparoscopic simulator platform. The data acquisition and fatigue analysis modules were designed based on MATLAB platform.
The experiment employed a virtual reality laparoscopic simulator (called Simbionix LAP Mentor) to perform peg transfer task. This simulator has a wide variety of modules ranging from simple to complex laparoscopic tasks such as suturing, basic operation, cholecystectomy, gastric bypass surgery, incision hernia surgery, gynecological surgery, rectal surgery, and laparoscopic assembly skills. During the experiment, BrainLink was used to record EEG signals through the use of its three forehead electrodes. The recordings of neuronal activity in the brain are identified as EEG signals [
Schematic view of EEG fatigue monitoring system.
Signal processing and fatigue analysis were designed to follow three steps, as demonstrated in Figure
Three steps for signal processing and fatigue analysis.
The first step is to remove artifacts from EEG signals. Although BrainLink is designed to record cerebral activity, it also records electrical activities arising from entities other than the brain. Generally, any activity that is recorded apart from cerebral origin is termed as artifact. In most cases, the artifacts can be obtained from either physiologic or extra physiologic perspectives. The former are unwanted physiological signals that arise from source other than the brain, that is, the body. For example, eye movements, heart, and muscles. The latter are technical and arise from outside the body, for example, noise in AC power line which can be reduced by decreasing electrode impedance and shorter electrode wires. In fact, any EEG signal greater than 50
Raw EEG signal from the BrainLink.
The second step is the extraction of characteristic rhythm waves. Human brain pattern poses regular oscillations which are termed as rhythms and are differentiated on the basis of signal’s frequency [
Brain waves belonging to alpha
The third step is computation of deviation of EEG signals in
The parameter
The system was designed to use GUI platform which enables functions such as data processing and drawings. The system collects EEG signals and extracts characteristic rhythms wave
GUI platform for signal processing.
Peg transfer task requires pegboard (with 12 pegs) and six rubber triangles (Figure
Left and right shift of six rubber triangles.
The training was evaluated objectively by comparing efficiency (in terms of completion time), accuracy (in terms of number of errors), and fatigue level.
In most laparoscopic surgical training, minimizing number of errors, completion time, and fatigue level is preferred in general as it would improve efficiency of the training. To get representative sample of each aforementioned quantities, we averaged the results of all subjects at each training trial. Herein, we present the results of errors made during peg transfer task, time to complete the task, and fatigue level.
Regression Model predicted that the number of errors made during the first seven training trials was associated with lack of technical skills and perhaps new procedures [
Number of errors during peg transfer task.
In terms of completion time, the laparoscopic simulator also calculated time spent for each subject to complete peg transfer task. The results show that all subjects took long time to finish the peg transfer task at the first time (around 130 seconds). However, each subsequent time they did the task took less time than the previous one until around 12 trials. This indicates that the subjects have learnt from doing peg transfer task and became faster each time they repeated the task. Nevertheless, the completion time started to increase at later trials due to fatigue effect. Precisely, during the first few training trials, the subjects took long time to complete the task. This is when they were acclimatized with the training. As more and more trials were conducted, significant improvement was found and the completion time decreased by 24% as predicted by the regression equation
Completion time as a function of training trials.
Fatigue exhibited different behavior, with its rate growing gradually up to around ninth training trials, as predicted by the linear regression equation
The measured rates of fatigue.
Prolonged working hours and lack of sleep have been associated with loss of attention, performance decrements, and increase in errors in medical practitioners [
This paper has explored and evaluated effects of fatigue on efficiency and accuracy during laparoscopic surgical training using direct measurement of brain activity. The findings indicate that there are significant learning and fatigue effects when peg transfer task in the training is repeated in a series of trials. However, for the training to be effective and efficient, there should be monitoring to observe where in the learning curve a trainee gains maximum learning benefits. Moreover, fatigue is a significant indicator of efficiency in terms of time to complete laparoscopic task and accuracy in terms of errors made while doing the task. Even though these results reflect laparoscopic surgical training, the principles also apply to surgeons during patient operation, which provides some useful fundamental lessons for workplace or in hospital. Future work should entail investigating effects of fatigue in surgeons during laparoscopic surgery simulation based on Electroencephalography.
The authors declare that there are no potential conflicts of interest regarding the publication of this paper.